MINISTRY OF EDUCATION AND TRAINING STATE BANK OF VIETNAM Ho Chi Minh University of Banking DUONG THUY HANG APPLYING AUDIT DATA ANALYTICS IN FINANCIAL STATEMENT AUDIT A CASE STUDY AT AASC GRADUATION TH[.]
INTRODUCTION
The necessity of the topic
The analysis is a highly effective audit procedure because it takes little time and is low cost, but it can provide evidence of the uniformity and general plausibility of the data, and at the same time, it helps not to get too involved in the transactions. Specifically, analytical procedures are used in all three phases of the audit process to gather evidence to conclude the reasonableness or anomaly of the data.
Although numerous scholarly articles have been written on the future of DA in accounting, relatively little empirical scholarly research has been conducted addressing the issues presented in this article However, several scholarly articles present specific research questions to be addressed in DA For example, Wang and Cuthbertson (in press) interviewed a practitioner with more than 30 years of experience developing analytical tools for internal and external auditors They identified eight categories of research questions that scientists can address, including the role of DA in risk analysis, what procedures should be performed, the impact of testing 100% of the population, whether external data should be used, the role of DA use by internal auditors, the interpretation of DA results, the consequences of DA use, and whether the profession needs a DA framework Similarly, Leonard Combs, PwC U.S Chief Auditor and Leader of Auditing Services, Methodology & Tools reports: “Data analytics is changing both the way we conduct our audits and what those audits deliver It allows us to extract and analyze ever-larger data sets Further use of data analytics will allow us to deliver effective audits more efficiently” (PwC 2017).
Current reality shows that many auditors who apply this procedure are stereotyped, rigid, and do not bring into full play the effect of analytical procedures in finding and detecting fraud and material misstatement in the report financial statement While data analytics can successfully address some assertions, it does not eliminate the necessity for other audit methods to address the risk of a significant misstatement as a whole Such techniques include tracing back to the underlying
2 source documents, which is required to resolve other accusations (e.g., occurrence and rights and obligations) As a result, the auditor must perform procedures to address the risk of a significant misstatement as a whole.
After completing the internship period at AASC Auditing Firm, the researcher has gained knowledge and experience in the financial statement audit process, especially new data analysis techniques Realizing the need to minimize the risk of errors on the financial statement and optimize the efficiency for users, the author chooses the topic name "Applying audit data analytics in financial statements audit:
A case study at AASC" as graduation thesis.
General, specific objectives and research questions
Currently, in Vietnam, this topic is also being interesting and applied by many organizations and units However, there are not many articles or in-depth studies on the potential benefits and difficulties for auditing firms in Vietnam when applying
BD and DA in the audit of financial statements Therefore, in this article, the author synthesizes and analyzes the characteristics and opportunities, challenges of the application of DA and BD in financial statement audit at AASC Auditing Firm with the desire to contribute a more multi-dimensional view of the financial statements. Besides, this study provides insight into how firms’ leadership and engagement partners and managers perceive the prospects and impediments to audit data analytic (ADA) use.
Research on the project application process through 2 case studies when auditing financial statements at AASC and conducting in-depth interviews with auditors in AASC:
- Review and explore opportunities and challenges of applying DA, thereby drawing lessons from experience and specific perspectives to apply to audit activities at other auditing firms.
- Analyze the preliminary balances in all the accounts in the company's general
3 ledger to identify unusual changes from the previous year.
- Use the results of the analysis to decide whether changes were needed in the planned nature, timing and extent of the following:
- Other risk assessment procedures, focused on particular accounts and related assertions.
- Further audit procedures to be performed in response to assessed risks, including tests of controls and substantive procedures.
- To obtain understandings about all transactions occurred in 2021
- To find outliers of transaction amounts in 2021
- To identify risk of frauds in 2021
To achieve the research objectives, the following questions need to be well answered:
Q1: How does the firm currently perform analytical procedures and audit analytics in the audit process?
Q2: What are the advantages/opportunities and challenges for the firm in applying audit data analytics?
Research objects and research scopes
The research subject are two manufacturing and trading companies whose financial statements are audited by AASC Auditing Firm.
- Cross section dimension: The study is limited to an enterprise audited by AASC and conducted in Ho Chi Minh City.
- Time series dimension: a 2-year period from 2020 to 2021 is selected, in which secondary data is collected from financial statements of two manufacturing and trading companies.
The research methodology
The thesis uses observational and descriptive method to collect primary and secondary information at AASC Moreover, the author uses the theoretical research
4 method and empirical method to search, analyze, process information, and describe the statistical methods, and practical experience through real work.
Data sources: In this thesis, the author uses two sources of data, which are primary data and secondary data.
- Primary data: Those data were collected by interview method during the research period at AASC, in order to identify the advantages, opportunities and challenges of applying new audit analytics techniques in the context of the audit firm.
- Secondary data: That information and knowledge collected from internalAASC documents and customer's accounting data (in Excel).
Contribution of the research
Firstly, the thesis has systematized the theoretical basis necessary for further studies on ADA Especially, many previous findings related to BD and DA in large audit firms were collected to provide a more thorough perspective on this regard. Secondly, the thesis has pointed out new data analysis techniques compared to current ones to apply to improve audit quality.
Lastly, policy recommendations based on the research results have been proposed to support Vietnamese auditing firms.
Structure of the thesis
Besides the Introduction, List of tables and figures, References and Appendix, the thesis structure includes 5 chapters as follows:
Chapter 1: Introduction, this chapter gives an overview of the motivation for the research, research objectives, research questions, research scope as well as research objects and new contributions of the study about applying audit data analytics.
Chapter 2: Prior studies on the application of audit analytics, in this chapter, the author summarizes and presents an overview of previous studies in the world and Vietnam related to the application of DA in auditing financial statements.
Chapter 3: Literature review, this chapter indicates contents related to the
5 research process as well as describes the data source and methods to collect data.
Chapter 4: Results and discussions, this chapter describes how auditors apply
DA to the financial statement audit process and discusses the research findings through a case study.
Chapter 5: Conclusion and recommendations, this final chapter summarizes research contents, gives conclusions and thence, some recommendations will be suggested to improve the audit effectiveness of the audit process of financial statement at AASC.
In the first chapter, the thesis has stated the necessity of the topic as well as specifically defined objectives, objects and scopes of the research In addition, a number of new contributions of the thesis toward the originalities of this study in the context of Vietnamese auditing firms.
PRIOR STUDIES ON THE APPLICATION OF AUDIT
Foreign
Data analytics has become increasingly popular in recent years, especially in the field of accounting and auditing Some studies show that the current testing strategy and acceptance is in its fourth revolution Audit 1.0 (Dai and Vasarhelyi,
2016) is commonly known as the old manual audit There were no computers or software during this time, and technology was almost non-existent The only tools available to any auditor were pens, some paper, and a calculator This paper-based audit was time-consuming and inefficient (compared to current capabilities), but there was simply no other alternative Consequently, this process has been around for centuries The first Information technology (IT) audit revolution was created with technological growth, Audit 2.0 (Dai and Vasarhelyi, 2016) Back then, only 15% of auditors used analysis tools like Excel, which are essential today In recent years, BD has gained popularity and been integrated into these systems, marking the third generation of auditing (Dai and Vasarhelyi, 2016) The latest revolution goes even further with the inclusion of non-financial information in the dataset before DA is performed With the use of the Internet of Things (IoT), Cyber Physical Systems (CPS), and Smart Factories, DA seems to be becoming increasingly automated, processing large amounts of data to identify patterns and anomalies (Dai and Vasarhelyi, 2016; Dagiliené and Kloviené 2019) Several studies show that DA can improve audits and make them more efficient by increasing the speed, quality, and quantity of information processed The well-known big four accounting firms also use DA daily Deloitte announced a 9.4% more income compared to the previous year Deloitte's global director of people and purpose said that one of his strategies is the use of data analytics Ramlukan (2015), the main and global assurance analytics leader from EY, said
“It is a massive leap to go from traditional audit approaches to one that fully seamlessly integrates big data and analytics The transformed audit will expand beyond sample-based testing to include analysis of the entire population of audit- relevant data”.
PwC and KPMG published articles explaining the importance of using DA, its benefits, but also the barriers to integrating it with traditional auditing However, DA not only offers benefits but also brings with it several issues and difficulties that audit firms need to consider and handle if they wish to apply it (Earley, 2015). Proper understanding and availability of data are one of the first concerns Second, investor and regulator expectations may change Professional judgment and skepticism, which is crucial for each audit, should not be neglected, so they should be treated with caution when analyzing DA outcomes and results.
The DA was already mainly used in the advisory or tax area According to Deloitte (2013), DA can help reduce tax errors or find ways to reduce tax breaks. For example, companies collect huge amounts of data about their customers, suppliers, competitors, or their environment, but do not have the necessary knowledge to process and analyze it properly According to 85% of managers surveyed by KPMG (2015), the biggest challenge is finding the best way to analyze the data collected Although the application of DA in auditing is slower than, for example, forensic or advisory investigations (Katz, 2014; Whitehouse, 2014), DA is presented as the future of auditing (Liddy, 2014) and "has the potential to be the most significant change in the way audits are conducted since the introduction of paperless auditing” (Capriotti, 2014).
In September 2019, PwC announced plans to invest $3 billion in training and technology over the next 4 years (Chawala, 2020) The aim is to relieve the examiners so that they have more time for themselves In some demonstrations, they showed how the automated robotic process can scan invoices, automatically enter data and collect documents, which could ultimately lead to reduced working hours. (Dignan, 2020)
KPMG announced in December 2019 that they will invest $5 billion in AI technologies and employee training over five years The outputs are mainly based on its cloud-based technology to facilitate communication with its customers (Chawala, 2020).
“Now, we can audit all the client’s transactions with smart data analytics and can also pinpoint transactions with high risk from large datasets Having the ability to detect patterns in clients’ data bring it’s sort of value We can share insights with clients that were hidden in the large piles of data before.” (Consultancy.eu, 2019) Eilifsen et al (2020) interviewed the heads of professional practice (Heads) of five international public accounting firms in Norway to understand the status of the ADA in each company and conducted detailed questionnaires from 206 audit partners and/or managers on ADA performance across 109 audits from the 2017 audited financial statements This research has yielded several important benefits. First, little empirical research exists about ADA since they are relatively new technologies and gaining access to auditors is challenging (Austin et al 2019) Gepp et al (2018, 110) argue that qualitative, interview-based studies are needed to fill this knowledge gap Their study provides insight into how firms’ leadership and engagement partners and managers perceive the prospects and impediments to ADA use Second, they document the prevalence and nature of ADA use on current audit engagements Auditors’ actual use of ADA reflects auditors’ judgments and decisions about the efficiency and effectiveness of ADA use Thus, they provide evidence on audit areas where the output from ADA are being used as an evidential source (i.e., sufficient and appropriate audit evidence) and where obstacles to their use remain Finally, their research provides a starting point for practitioners and researchers in validating the efficiency and effectiveness of the use (or non-use) of ADA In sum, their study contributes by documenting the current status of auditors’ use of ADA at the engagement level and by developing an understanding of why ADA use has not yet fulfilled its promised potential.
Their interview results and questionnaire responses show the following First,the firms’ heads of professional practice express significant uncertainty about how the supervisory inspection authorities will evaluate and accept ADA generated audit evidence As a result, none of the firms have introduced mandatory use of
“advanced” ADA tools While ADA use is high on the firms’ agenda and there is a global push for ADA to be used on audit engagements, actual use is limited in their sample Additionally, the firms differ in their strategies in how they implement the use of ADA in their organizations from a “wait and see” approach to centralized ADA functions and extensive firm involvement to facilitate ADA use Second, the partners and managers indicated that their knowledge and training with firm available ADA tools was sufficient to permit their use of ADA and their attitudes towards ADA usefulness are more positive for firm audits in general than for the sampled audit engagements Third, more ADA is used on engagements where the client has an integrated ERP/IT-systems Fourth, there is a higher frequency of ADA use on new audit engagements Participants expressed that recent tenders specifically asked about the use of new technology and ADA in audits and that the audit firms promoted ADA use in the tender process Fifth, they identify where ADA is used across the various phases of the audit In the audit planning phase, ADA is used for the overall assessment of the client's operations and performance, identifying and assessing key risks, and mapping of different processes In the substantive testing phase, ADA is used for journal entry testing, calculating sample- size, selection of random samples, and summarizing ledgers In the completion phase of the audit, ADA is most used for reconciliation and control between final accounts and underlying ledgers, analytical procedures, and final review of financial statements. However, overall, the use of ADA in each phase of the audit is low Sixth, they find little use of what would be considered advanced ADA (i.e., statistical regressions, clustering techniques, statistical predictive analysis, computerized process-mapping, etc.) The use of BD and text mining is almost non- existent When ADA is used, ADA output is mostly used as supplementary evidence In summary, based on the interviews with the heads of professional practice and the questionnaire results, their results suggest that the use of ADA within the firms is limited and at an early stage of implementation.
The study establishes that the actual use of ADA is low at the engagement level, notwithstanding the many arguments of the potential of ADA usage and the auditing firms’ commitment to transforming the audit into an ADA-driven product, and points to factors that may explain this outcome Walker and Brown-Liburd
(2019) present a conceptual framework that comprehensively describes the emergence of the use of ADA by audit professionals through the lens of institutional theory They argue that in the process of incorporating ADA into the audit, audit firms respond to pressure from the external environment by attempting to gain legitimacy of ADA usage within their environment Given external pressure and the legitimation process, the engagement leader acts and decides whether it makes sense to use ADA on the audit They discuss their results from perspectives of institutional theory to better understand the auditors’ complex decision of ADA usage and the observed limited use of ADA in their sample Institutional theory provides insight into the underlying causes of the problems facing audit team leaders in applying ADA and how these issues can be mitigated They conclude that institutional pressure for firms to apply ADA primarily stem from technological advancements and to some extent from audit clients, particularly new clients Except for promoting externally that they are engaged in the process of transforming to ADA usage and to some extent towards audit clients; legitimization of ADA usage to other constituencies seems not to be a high priority for the surveyed firms In deciding the use of ADA at the engagement level, the auditor must be confident in the ability of the ADA tools to efficiently and effectively provide sufficient and appropriate evidence Institutionalized conventions suggest that limited use of ADA and the problems they identify with the use of ADA will persist until ADA usage is proved to be superior to the current evidence gathering process and their use is supported by the firms, regulators, and supervisors.
Domestic
In Vietnam, there have been articles referring to this issue, and enterprises, as well as large corporations, have deployed related applications According to the survey data of the Ministry of Industry and Trade in 2019, 61% of Vietnamese enterprises are still outside of the 4.0 Revolution and 21% of enterprises have just started preparing activities According to statistics from the National Bureau of Science and Technology Information in 2018, 8% of enterprises use advanced technology; 50% of enterprises use medium- and medium-advanced technologies; the remaining 42% of enterprises use outdated technology The benefits that Industry 4.0 brings to businesses are shown in a 2015 PwC study showing that Industry 4.0 will bring businesses in Asia such as increased revenue (39%), increased production efficiency (68%), and cost reduction (57%).
Among the applied enterprises, many large corporations have had strategies and actions to apply technology to their business activities For example, VinGroup invested in building a Big Data Institute (Vingroup Big Data Institute) in 2018 to research key areas in the Big Data industry, and at the same time to research new and valuable technologies with high application, directly applied to products (VinGroup) In early 2020, FPT Corporation successfully implemented the system building and Big Data analysis for TP Bank, this is FPT's first Big Data contract for banks in Vietnam, including key components: Data Lake data warehouse built on top of the open Hortonworks Data Platform (HDP)- stores Big Data, from multiple sources, including raw and unstructured data pools; The Watson Studio Local machine learning model building platform, combined with the optimal IBMIntegrated Analytics System (IIAS) device for high-speed data analysis, reduces model training time In the coming time, FPT IS will continue to deploy consultingBig Data Analyst solutions for Maritime banks (MSB), Techcombank, Vietinbank,BIDV, and Credit Information Center (CIC) (according to FPT InformationSystem) shows that enterprises have been and are ready to apply DA and BD solutions in their main business activities And IBM Vietnam said that Big Data and business analytics solutions are becoming the center of IBM's "transformation".Every day, the world economy generates 2.5 Exabyte of data (equivalent to over 625 million DVDs), and many industries with future strategies will apply Big
Data and DA in production activities its business However, this is still new content and needs a lot of research investment In a survey by KPMG (2014) of CFOs andCTOs conducted in 2014, 99% of respondents noted that data and DA play an important role in their lives with their business strategy, and 96% expressed that they could make better use of Big Data in their organization.
In this chapter, the author summarizes and presents an overview of previous studies in the world and Vietnam related to the application of DA in auditing financial statements Based on studying related research articles, the author identifies research gaps to determine the direction for research on the topic and make recommendations.
THEORETICAL PERSPECTIVES
Related concepts
3.1.1 Concept of audit data analytics
Data analytics is the process of processing and examining data to uncover useful information and help users make decisions In auditing, descriptive analysis and diagnostic analysis are the two main types of data analysis that are used.
Using data analytics helps the audit team improve their understanding of the data and examine the entire portfolio In addition, data visualization helps uncover trends or correlations in the data, allowing the audit team to focus on high-risk areas. For example, for the risk assessment process related to receivables on the balance sheet, if a preliminary analysis is performed, only the growth or decline over the years can be assessed But by applying data analytics, the auditor can look at receivables along with the age of the debt over the years, which can assess the magnitude of the increased risk involved Therefore, the engagement team can recommend the appropriate procedures for examining the item.
Meanwhile, DA is the method of data or information analysis to draw conclusions and facilitate the decision-making process (World Bank Group, 2017).
As a concept, DA primarily encompasses IT functions and applications, from basic business intelligence (BI), reporting, and online analytical processing (OLAP) to multiple modes of advanced analysis used to analyze data In the exam context, DA involves larger and more complex procedures during the exam process This requires the use of sophisticated software or advanced statistical tools and techniques This can include cluster analysis, predictive modeling, data layers, visualizations, and what-if scenarios that enable the use of new strategies to assess large amounts of relevant audit information The use of analysis tools allows auditors to collect information from internal and external sources as proof in various phases of an examination, e.g., during analytical procedures, testing of controls, risk assessment, and statement-related procedures (Tschakert et al., 2016).
Recent discussions in the audit profession have recognized the importance of
DA in audit practices (Vasarhelyi et al., 2015) As stated by Capriotti (2014), it “has the potential to be the most significant shift in how audits are performed since the adoption of paperless audit tools and technologies” There are at least three main benefits of using DA in an audit, as auditors can take advantage of analytical tools and technology (Gray and Debreceny, 2014; McGinty, 2014) First, DA allows auditors to automate transaction testing, and theoretically, 100% of the population audited can be tested (Liddy, 2014) Second, audit quality can be increased by enabling a better understanding of client processes through the identification and analysis of accounting anomalies (BrownLiburd and Vasarhelyi, 2015; Capriotti, 2014; Whitehouse, 2014) Third, using DA can improve fraud detection in an audit (Earley, 2015).
However, implementing DA in revision is not an easy task There are requirements such as the need to understand the current scope and limitations of the auditing profession before imagining the role of more complex analytics and DA in audit practice (Appelbaum et al., 2017a, b) Salijeni et al (2018) indicated that there are several conceptual discussions in the literature about the factors influencing the use of DA in assessment practices For example, Krahel and Titera (2015) discussed the need for specific accounting and auditing standards related to DA that facilitate the approach, analysis, and presentation of data In addition, the application of DA in practice can be promoted by appropriate standards with guidelines on questions related to the examination of large amounts of data, e.g., data collection, error response, and auditor competencies Conducting DA is some of the inhibiting factors in including the DA in external audits Empirical evidence from interviews with 21 participants conducted by Dagiliene and Klovien _e (2019) found that firm- related or audit clients (such as size, data-driven strategy, and business model) and institutional aspects (such as competition in the audit market, regulatory policies about BD and DA and educational institutions) are important motivating factors for the application of DA exam practice A questionnaire study by Eilifsen et al (2020) documented that auditor found DA audit tools simple and not complex enough to use in the course of an audit The auditors also recognized that DA can be applied effectively in audit practice when organizations have adequate DA tools, the necessary skills, and the availability of professional support for the use of DA in audit engagements The auditors also strongly emphasized the importance of integrating customer information systems to enable the use of DA in audits. Respondents in a study by Salijeni et al (2018) highlighted the challenges of including the DA in audits, including detecting “false positives” resulting from testing 100% of the population, costs associated with excessive auditing, over- reliance on analytical specialists, and insufficient guidance on auditing standards. The benefit of DA in assurance is the ability to use non-financial data (NFD) and external data to better inform audit planning (particularly in risk assessment) and more effectively audit those areas that require judgment, such as valuation or going concerned Because auditors can develop models that can predict future events, often referred to as predictive analytics, they can better help their clients make strategic decisions about their business NFD includes data that the company collects internally, such as human resources data, customer data, marketing data, etc that goes beyond the types of financial statements that auditors normally look at As pointed out by Alles and Gray (2014, p 16), “the vast majority of data in dig data is NFD”.
According to Gartner's IT Glossary, the types of data analysis commonly used in financial statements are descriptive and diagnostic: Descriptive analysis is the study of data or content to answer the question "What happened?" and is often characterized by traditional business intelligence and visualizations such as pie charts, bar charts, line charts, tables or generated narratives Diagnostic analytics is a form of advanced analytics that examines data or content to answer the question
"Why did this happen?" and is characterized by techniques such as drill-down, data discovery, data mining, and correlations.
3.1.2 Purpose of audit data analytics
The data analysis was developed to improve audit quality The quality of the audit does not lie in the tools themselves, although this obviously cannot be achieved without proper tools, in the quality of the analysis and judgments provided The value is not in the transformation of the data (impressive as that may be), but in the audit evidence extracted from the conversations and queries that the analysis generates PwC example below:
Figure 1: PwC’s ‘Halo for Journals’ 1
The information on this dashboard can be used to make comparisons to previous years and (possibly) to other companies If the automatic vs manual indicator shows a high level of manual registrations, it could indicate inefficient use of the system, the complexity of the process, or depending on the circumstances.
• Illustration from Halo reproduced with permission of PwC Halo © 2016 PwC PwC refers to the PwC network and/or one or more of its member firms, each of which is a separate legal entity Please see www.pwc.com/structure for further details Halo is also a registered trademark owned by PwC All rights reserved.
They pose a risk of fraud Metrics related to individual users may also indicate unusual activity that warrants further investigation.
It seems clear that the following unique characteristics of data analysis, when applied correctly, can significantly improve audit quality:
• the ability to graphically visualize results: data visualization is now a discipline in its own right;
• sophistication, and the breadth of interrogation options;
• ease of use by non-specialists; and
Interviewees emphasize different elements of the list above when asked about how data analytics contributes to audit quality For some, the sophistication of inquiries generated by high-quality visualizations has resulted in better-quality explanations For others, more comprehensive and accurate analyses facilitated by the sheer speed and volume of processing are more important, but:
“… auditing standards will eventually catch up with us on this but at the moment, they’re based on the assumption that what matters is how you go about finding needles in haystacks Data analytics has shrunk the haystacks and, in the future, it’ll be about what you do with those needles when you’ve found them.” Auditors can navigate much larger external datasets much more quickly than ever before, as the greatest recent advances have been in the interfaces between client and accountant systems, software, and data interfaces that facilitate data extraction These interfaces allow auditors to perform routines not just as substantive procedures as in the past, but earlier during the audit at the risk assessment stage to understand processes and work on controls.
Many of the analyzes that are performed are not fundamentally different from those performed in the past but are now more granular and at the same time broader in scope Part of the content check, for example by running routines that scan large magazines and unexpected users, which in turn facilitates a further investigation.
3.1.3 Auditor’s approach to developing specific data analysis tools
Auditors often use some combination of the following development approaches:
• take the tools and consider how they can be used in the audit;
• ask what has been performed before that might be automated and expanded; or
The first approach is probably the most creative and valuable in the long term. The second is safer, more focused, more complainant probably less wasteful While the third approach may seem ideal, it is only just becoming possible.
3.1.4 Reliability of audit data analytics
• The firm continues to have difficulty determining where the ADAs can be used as essential methods to detect misstatements due to error or fraud (i.e., going beyond risk identification and assessment) The main problem is the amount of work required to establish the reliability of the required data When an ADA is used as a material procedure This is a big hurdle.
• The engagement team's completeness test of the trial balance could often identify issues for tracking, as journal entries reflected in the subsystems were sometimes not yet reflected in the general ledger. Therefore, it can be difficult to allocate journal entries through the system Finally, the information used to control operations may differ from the information used for financial reporting due to disruptions between aspects of the system.
Applying audit data analytics in financial statement audit
3.2.1 Applying audit data analytics in the audit planning phase
To prepare well for the audit of financial statements, first, the auditor needs to prepare the data for use. a) Data Acquisition:
Since effective and efficient data collection is one of the critical success factors for using data analytics, engagement teams should determine early on whether the quality of the data that business management can provide is sufficient to support the analytics being used.
Engagement teams may not have the IT skills required to extract the relevant data from the entity's systems in the required format or to organize the data extracted by the client's IT staff into a format suitable for use in data analysis suitable is If this is the case, using specialized personnel and standard scripts for data collection and uploading data analysis tools is good practice to ensure accurate data is obtained in a usable format.
Examples of situations that may justify some data transformation include the following:
• when the date format of different systems in an organization is different, for example, "yyyy-mm-dd" format in one system and "dd- mm-yyyy" format in another, or
• where it may be necessary to remove trailing zeros in an inventory item code to ensure proper comparison with another data source, which may not contain such leading and trailing zeros.
While some data error issues are relatively easy to resolve, the nature of certain issues identified can cast doubt on the quality of the data the auditor intends to use. For example, in cases where certain data fields are unlikely to contain spaces or null values, the presence of such elements may indicate that controls over the data are not working effectively and consequently the data in which they may be used may not be appropriate for the test until the company takes appropriate action to correct the records. b) Considering Relevance and Reliability of Data
SSA 500 Audit Evidence states that the auditor shall consider the relevance and reliability of the statistics for use as audit evidence.
• Relevance refers back to the logical connection with, or bearing upon,the motive of the audit method and, wherein appropriate, the statement below consideration.
• Reliability of the statistics is stimulated through its supply and its nature, and the situations below which it's miles obtained, which includes the controls over its guidance and preservation wherein relevant. c) Relevance of Data
With the countless opportunities round statistics analytics, the relevance of statistics becomes more and more essential because the statistics being analyzed want to apply to the audit techniques responding to the chance of cloth misstatement on the declaration degree of the elegance of transaction or account stability below consideration One instance in which relevance is in query is in which the statistics analytics offer exciting insights to control however does now no longer produce adequate audit evidence. d) Reliability of data
The majority of statistics applied in statistics analytics is IPE, and as such, the auditor is needed to assess whether or not the data is adequately dependable for the auditor’s purposes, inclusive of as vital with inside the situations below:
• Obtaining audit proof proximately to the accuracy and completeness of the IPE; and
• Evaluating whether or not the IPE is adequately particular and targeted for the auditor’s purposes.
In figuring out the method that the auditor can also additionally use in figuring out whether or not facts are satisfactorily reliable, the reason for which facts analytics is being performed (for example, whether or not as a danger evaluation procedure, check of controls, significant analytical procedure, or check of details) desires to be considered.
For example, if information analytics is used to carry out similar audit processes, the volume of trying out the information underlying the similar audit processes could probably be more than whilst trying out the information meant for use inside the information analytics used as danger evaluation processes. These processes to check the underlying information could now no longer be distinctive to the technique taken for conventional audit processes as set out in SSA 500 and might include:
• Obtaining audit proof of the accuracy and completeness of the IPE thru processes carried out simultaneously with the real audit processes implemented to the IPE whilst acquiring such audit proof is a quintessential a part of the audit method itself; or
• Testing the controls over the practice and upkeep of the IPE.
If the IT widespread controls are ineffective, the auditor must check their effect on the use of statistics analytics inside the audit.
3.2.2 Applying audit data analytics in the audit implementation phase a) Cash Receipts:
• The use of ADA may not be feasible due to the complexity of the way the audited entity's system processes some transactions For example, there can be many variations in how sales transactions are recorded, including cash receipts This results from rebates, returns,
"clearing" of various aspects of transactions, and batch payment processing.
• The ADA accustomed map the sales-receivables-receipts method as a part of risk assessment wasn't entirely successful The engagement team had issues in coping with the various completely different sources of information concerning cash receipts. b) Fixed Asset Additions
• When executing the ADA for fixed asset additions, the hiring team was unable to obtain a correct correlation between additions, WIP, and cash payments For example, labor costs are an important part of the fixed asset additions for this company; this was not taken into account when designing the ADA In addition, the audit of this company did not include the conduct of proceedings at an interim date As a result, problems with this ADA were not identified early enough to develop an appropriate solution The team concluded that for this customer, the additional time and effort required to design and execute the ADA far outweighed the benefits of the ADA Therefore, they resorted to traditional testing methods that were easier to carry out They don't plan to use an ADA for property, plant, and equipment in the next year. c) Purchases, Payables, Payments
• ADAs used to scan various fields in database files related to purchases and payments improved audit quality by allowing better assessment of the overall risk of fraud related to segregation of duties In that respect they were successful But the ADAs:
- did not replace traditional accounts payable procedures
- were not directed at detecting material misstatements in accounts payable - did not contribute to audit efficiency.
- Accounts Payable integrity is an important requirement This could not be addressed through the use of an ADA Traditional methods were used for this. d) Payroll
• The engagement team does not plan to use an ADA in the payroll audit. They believe that traditional auditing methods are better suited for payroll auditing and that using ADA is unlikely to provide management with useful information In addition, previous audits have not identified any significant risks of material misstatement in payroll transactions or vendor purchases The team wants to focus ADA usage on areas of importance,such as the risk of fraud related to revenue Company-developed ADAs related to sales verification are readily available The engagement team didn't expect to get very interesting insights from using an ADA for payroll.
RESULTS AND DISCUSSIONS
Apply some new audit analytics techniques to two client cases of this
Case 1: Having been granted the Certificate of Business Registration by the
Department of Planning and Investment, Company A officially came into operation in October 2015 with the business line of wholesale petroleum and related products.
The company uses Fast Accounting Offline (FAO) accounting software to easily manage transactions arising during the year FAO with 1 system subsystem and 13 business subsystems, fully meet the accounting and tax requirements for businesses Chief accountants can do accounting with complex models such as many subordinate units, many accounting departments, and many types of foreign currencies, and especially always be updated with circulars on accounting and tax of the Ministry of Finance and General Department of Taxation, prepare financial statements and accounting books by regulations Carry out collection and expenditure, payment related to cash, deposit, or loan in a strict and detailed manner according to the right object, according to the invoice, according to the contract, and according to the charge item Prevent negative spending With just one click, chief accountants can view a quick report of the cash balance in the fund, deposits, and loans at each bank The sales-collection cycle can be done on the software from order creation to delivery and collection, helping to receive full orders, on-time delivery, and timely collection of money The purchase - payment cycle can be done on the software from ordering to receiving goods and paying suppliers Reports on inventory status, sales orders, support timely ordering, and optimal inventory Debts are tracked in detail according to the payment due date of each invoice, helping to pay on time and build a good relationship with suppliers Allows accountant to choose to calculate inventory prices by business requirements: monthly average, moving average, or first-in, first-out Extremely fast pricing, even with thousands of material listings and a large number of import and export coupons Along with other diverse features, FAO has greatly supported the accounting operations at
Company A as required by management.
Before conducting report analysis, the auditor will ask the chief accountant to output data (including general journal, liabilities, fixed assets, inventory, balance sheet, ) from accounting software that the company uses In order to reveal any unusual changes from previous year, the auditor obtained data of trial balance (in Excel) from the client and performed some data cleaning procedures to convert data into the appropriate format for analytics The following table show the calculation of changes of all G/L accounts relating to the Balance sheet of the client.
131 - Phải thu của khách hàng 247.0 709.0 1 87.0
3 2111 - Nhà cửa; vật kiến trúc 16115.0 16115.
8 2141 - Hao mòn TSCĐ hữu hình 0.0 0.0 N aN
9 2143 - Hao mòn TSCĐ vô hình 0.0 0.0 N aN
2147 - Hao mòn bất động sản đầu tư
217 - Bất động sản đầu tư 0
5 2422 - Chi phí trả trước dài hạn 5983.0 5940.0
244 - Cầm cố; thế chấp; ký quỹ; ký 330.0 0.0 -
7 331 - Phải trả cho người bán 10124.0 14457.
9 3331 - Thuế giá trị gia tăng phải nộp 0.0 38.0 i nf
0 3335 - Thuế thu nhập cá nhân 11.0 6.0
3337 - Thuế nhà đất; tiền thuê đất
2 3338 - Thuế bảo vệ môi trường và các loại thuế 7244.0 3416.0 52.8
5 3341 - Phải trả công nhân viên 472.0 722.0 5
0 3388 - Phải trả; phải nộp khác 13864.0 13864.
4111 - Vốn góp của chủ sở hữu
6 Source: Audit files from AASC company
7 Source: Audit files from AASC company
4211 - Lợi nhuận sau thuế chưa 0
4212 - Lợi nhuận sau thuế chưa 1
Table 1: The Balance sheet of company A between 2020 and 2021 2
Based on the above table, the auditor drew the two bar charts for the purpose of visualization of the unusual changes in account balances However, with the first chart below, the auditor did not recognize anything unusual due to the similarity of
131 - Phải thu của khách hàng
2111 - Nhà cửa; vật kiến trúc
2141 - Hao mồn TSCĐ hữu hình
2143 - Hao mòn TSCĐ vô hình
2147 - Hao mòn bất động sản đầu tư
217 - Bất động sản đầu tư
2422 - Chi phí trả trước dài hạn
244 - Cầm cố; thể chẩp; ký quỹ; ký cược
331 - Phâi trả cho người bán
3331 - Thuế giá trị gia tăng phải nộp
3335 - Thuế thu nhập cá nhân
3337 - Thuế nhà đất; tiền thuê đất
3338 - Thuế bảo vệ môi trường và các loại thuế khác
3341 - Phải trầ công nhân viên
3388 - Phải trả; phải nộp khác
4111 - vổn góp của chủ sờ hữu
4211 - Lợi nhuận sau thuế chưa phân phổi năm trước
4212 - Lợi nhuận sau thuế chưa phân phối năm nay the bars.
L account balances as of 2021 compared to 2020 (in million
10 Source: Audit files from AASC company
Then, the auditor did some more calculations and prepared the second chart as in the picture below.
131 - Phải thu cùa khách hàng
2111 - Nhi của vật kãền trúc
2141 - Hao mòn TSCĐ hữu hinh
2143 Hao mòn TSCĐ vỗ hỉnh
2147 - Hao môn bát đông sân đàu tư
217- Bít đỏng sân đàu tư
2422 • Chi phí trà trước d*i han
244 Cim cò thè ch*p kỹ quỹ kỹ cược
331 - Phả trả cho người bân
3331 - Thui giá tn gia tảng phổi nộp
3335 ■ Thuế thu nh*p cá nhân
3337 - Thuế nha đốt hèn thuê đát
3338 - Thuế bão vé môi trường và cAc loai thuế khác
3341 - Phải trả cổng nhân viên
3388 - Phái tri phải nộp kh*c
4111 - Vỏn góp cùa chù sỏ hữu
4211 ■ Lợi nhuân sau thuế chưa phản phò năm trước
4212 - Lợi nhuận sau thuế chưa phân phối nám nay
Changes in ending G/L account balances as of
According to the policy of materiality benchmark of the firm, all changes of account balances that exceed 20% (both directions, in red lines) are considered unusual So, the auditor noted the list of G/L accounts below for further investigation:
- Cash account (G/L number 1111): ending balance increases by 43,8% in comparison with the beginning of the year.
- Bank account (G/L number 1121): ending balance increases by 26,2% in comparison with the beginning of the year.
- Account receivables (G/L number 131): ending balance increases by 187% in comparison with the beginning of the year.
Given the largest (unusual) change of account receivables, the auditor paid attention to the risk of material misstatement of provision for bad debts. ẩ
10 Source: Audit files from AASC company
For further understandings about the above unusual change of accounts receivable in 2021, the auditor made a comparison of debit amount of A/R between the two consecutive years 2020, 2021.
Figure 4: Monthly Debit amount of A/R in 2020, 2021 (in million VND) 5
From the chart, the auditor found a significant increase in debit amounts for accounts receivable from the last month of the year 2021 (between two vertical red lines) This finding suggested the auditor obtain an explanation from the client’s management whether it was unusual or not To sum up, all the above data analytics of the company showed a potential risk of material misstatement of occurrence of revenue in 2021.
Case 2: With the concept of "Health comes first", Company B entered the market in July 1998 with the business lines of manufacturing drugs, pharmaceutical chemicals and herbal ingredients (in detail, production of herbal medicines: traditional medicine drugs that have been granted a registration book by the Ministry of Health.)
In contrast to Company A, Company B chooses Misa software to support its accounting operations Misa software supports the calculation of revenue and expenditure, salary, profit, and bonus for employees and departments automatically as well as controlling debt and sales, saving a lot of time compared to other methods
10 Source: Audit files from AASC company crafts of the past To bring about such great benefits, the software has been developed with features in many different businesses and fields to bring optimal productivity to many businesses The software has a mechanism to automatically detect errors on reports, books, and financial documents to easily check and offer solutions Misa accounting software allows the creation of multiple databases, which means that each unit of many branches or departments can work on an independent database Support from the software as well as the homepage of the Misa software system helps to update continuously and quickly on new laws, circulars, and new economic decrees of the state that businesses need to pay attention to and ensure during business operation.
To achieve the objectives of chapter 1 above, the auditor gathered 100% of transactions in 2020 and 2021 of the company Then, the auditor prepared charts for the purpose of comparison in volume and number of transactions over the two years.
6 Source: Audit files from AASC company
7 Source: Audit files from AASC company
Figure 6: Monthly total amount of transactions in 2020, 2021 (in million VND) 7
From the above two charts, the auditor drew some remarks:
- In general, monthly volume of transactions in 2021 tend to be lower than the same of 2020 This may be the consequence of COVID-19 pandemic in Vietnam in
- Total amount of transactions occurred in Jan 2021 is somewhat high
10 Source: Audit files from AASC company unexpectedly in comparison of the same period of previous year This may reveal
10 Source: Audit files from AASC company the risk of subsequent adjusting entries for latest accounting entries of previous year.
To find if there is (are) an outlier(s) of transaction amounts in 2021, the auditor prepares a boxplot of transaction amount by month of the year as following.
Figure 7: Outliers of transaction amounts by month in 2021 8
From the above boxplot chart, the auditor recognized that there were some outlier transactions (in red circle) occurred in July 2021 Then, the auditor prepared a listing of ten largest transactions in July 2021 for further investigation. o.
10 Source: Audit files from AASC company
10 Source: Audit files from AASC company
Table 2: Ten largest transactions in
For the purpose of identifying the risk of fraudulent transactions, the auditor summarized the number of transactions by weekdays and created a heat maps of the number of transactions that occurred per week days and month.The auditor then filtered out a list of transactions with a date of Sunday for further investigation.
Figure 8: Number of transactions by week day and month in 2021 10
For both the cases analyzed above and when interviewing with experts, we can see the benefits that DA brings: auditors check 100% of transactions and accounts by clients provided In addition, auditors gain deeper insights into key client activities That results in higher audit efficiency than before.
Both companies A and company B mentioned above, when receiving data from customers, the auditor will compare the data changes between 2 years,which new transactions arise or the amount that is much different from the previous year, analyze financial ratios and identify trends or unusual signs in the business's operations, thereby assessing risks and locating risks of material misstatement.
Interviewing results on the advantages, opportunities and challenges of
challenges of applying those new audit analytics techniques in the context of the audit firm
The author conducted an in-depth interview on the strengths and limitations of applying data analysis techniques in auditing financial statements with an auditor at
AASC Auditing Firm The interview lasted 30 - 45 minutes, conducted on June
4, 2022; the results are analyzed and used to improve audit quality.
According to auditors in the company, when performing audits of credit institutions, finance - the financial sector has the characteristics of providing diversified financial services, with a large scale, with a high level of expertise the earliest, strongest, and most extensive application of information technology and digital transformation Implement the orientation and direction of the superior leadership, determine the role of the application of information technology in general and the application of big data technology in auditing activities in particular, over time, audit the staff has gradually applied big data in auditing at financial institutions and organizations and has achieved some important results, with many remarkable findings In particular, from the effects achieved when applying big data to audit activities, it has been confirmed that the application of big data is inevitable and brings many benefits, increasing audit efficiency.
Accordingly, big data helps auditors to examine more transactions Using conventional methods, the auditor applies an audit assurance approach based on risk assessment and selection of transaction patterns to determine whether the balance of accounts and transactions are properly presented Due to limited time and human resource testing, random elements are selected in a selective mode Meanwhile, the application's big data will allow auditors to test more samples, up to 100%.
With the help of technology, frauds are easier to detect as auditors can leverage big data analysis tools and techniques to identify risk areas more precisely In particular, with the application of big data to localize risks, auditors can focus on areas of concern and drill down into areas with the highest risk; Apply tools and techniques to check for irregularities, thereby uncovering fraud.
In addition, the auditor may provide advice and resolve matters to the entity itself beyond its current capacity using non-financial and external data (human resource data, customer data, market data, etc.) to provide information for the audit By applying big data, non-financial data can be used to build models that can predict future events, such as errors or omissions in reports… By providing predictive models, auditors can give recommendations and advice to the audited entity to help the entity improve mechanisms and policies or overcome current problems and inadequacies.
However, auditors also pointed out that they also face some challenges when applying for DA Largely because of the gap in the auditor's expertise when it comes to using big data, the company must seek out analysts to train the auditors on the DA techniques used appropriately There are increased costs related to having the firm’s IT people involved There is also increased time spent in learning to apply the tools effectively in the context of this audit More hours were spent in year one to set up the use of ADAs for the engagement. There have been changes in the mix of the work (e.g., senior staff or manager involvement in running reports) The team is expected to be more efficient in year two, the team composition will be the same as the prior year Costs have increased because more senior staff are now being used for the audit than was the case a few years ago However, audit fee issues have never come up with this client.
At the individual level, the audit team leaders (partners and managers) perceive that the firms give incentives or exert pressure to use ADA instead of traditional audit tools Audit client pressure may also influence their decision around the adoption of ADA although all heads of professional practice did not agree with such pressure.
It can be difficult to determine when and where DA can be used in audits.
In addition, stakeholders indicated that consideration should be given to the nature of the audit evidence that is gathered by using DA tools He also opined that although DA could provide some pervasive audit evidence, it should not be a substitute for other methods that are currently used in gathering audit evidence (such as audit confirmation and inspection of documentation) In this regard, audit practitioners need further clarity on what the audit procedures of
DA may be used for, and whether it is used as an exploratory tool or as a confirmatory tool related to audit evidence.
A challenge for more ADA use seems to be how to cost-efficiently transform the output from ADA to sufficient and appropriate audit evidence. Research can contribute to better understand which factors inhibit this transformation to assist auditors to better apply ADA, for example along the lines suggested by No et al (2019) Uncertainty about supervisory bodies’ inspection behavior inhibits auditors’ use of ADA A better understanding on how inspection risk affects auditors’ ADA use (or none-use) is of importance. Multi-location application of ADAs is difficult because different locations may do things differently; this makes it challenging to run standardized routines to acquire data Obtaining data in a format usable for audit purposes has been a challenge, particularly when client system changes have resulted in the updated system providing less detail than was previously available It is important to discuss data acquisition issues up front with the client The engagement team had proposed using the ERP extract functions to obtain data for audit purposes However, the client did not allow this Their view was that this would entail management having to engage ERP personnel with specialized expertise This would be expensive both in terms of cost and time Obtaining access to individual sub-ledgers did not present any issues However, the volume of data required for the general ledger analysis was very large The data had to be obtained quarterly, which took considerable time If an attempt had been made to provide the auditor with the data all at once, the company’s system would have crashed in the process.
It is difficult to work with data that is still being modified by the client. The examiner must keep track of the adjustments made to the data used for the analysis This could be a bigger problem for some audits For example, because smaller customers can sometimes make more adjustments, using preliminary data may not be practical It is unproductive to undertake a significant amount of work and then find that the data does not reconcile with the records used to prepare the financial statements On the plus side, the lack of disparate input sources for this client helped minimize data collection issues.
Coming to company A and company B, Chapter 4 has shown the audit processes and analytical procedures when auditing financial statements atAASC Auditing Firm The benefits when auditors begin to include audit analysis in the audit of financial statements, and at the same time point out the difficulties and challenges that auditors face Besides, Chapter 4 also answered two research questions in Chapter 1.
CONCLUSION AND RECOMMENDATIONS
Conclusion
Firstly, auditors can check more transactions; second, audit quality is increased by providing more insight into the client's process; third, fraud is easier to detect because auditors can leverage the tools and techniques, they use to identify risk zones; and fourth, auditors can service and solve problems for their clients beyond current capacity by using non-financial and external data to inform the call audit.
Related to the first benefit that DA brings to audit activities is that it helps the auditors to examine a larger number of transactions In auditing, audit evidence always needs to meet two requirements: relevancy and completeness. Completeness is a relatively complex category because it is difficult to determine what is complete For the first benefit, DA and big data can improve audit quality by increasing the completeness of the evidence collected Currently, auditors apply an audit approach based on risk assessment and transaction sampling to determine whether account balances and transactions are properly presented DA and BD will allow auditors to automatically check transactions and theoretically, 100% of samples can be selected for inspection For non- financial data and metrics that are not widely used by current audit practice, but where tools will be developed in the future to run models and analyses forecasting to help the auditor identify business risks and areas of audit focus in planning, fraud detection, and to help assess the entity's ability to continue as a going concern Concerning financial data that is currently practiced by the auditor, the auditor collects and examines a sample of transactions and uses judgment in areas that are difficult to test (e.g., accounting estimates influenced by the unit administrator), with the audit method predicted shortly, there will be tools that can check 100% of transactions This will help detect anomalies in the transaction data provided by the customer, thereby helping to direct additional checks, possibly detecting more fraudulent transactions.
Judgments will be used in the evaluation of the next steps after abnormalities are detected.
Data analytics involves the extraction of data using fields within the basic data structure, rather than the format of records A simple example is Power View, an Excel tool which can filter, sort, slice and highlight data in a spreadsheet and then present it visually in variety of bubble, bar and pie charts. Visualizations are as good as the data on which they are based, and the quality of the analyses thereby facilitated depends on the underlying data that must be extracted, analyzed and linked in the right way.
These tools can be used in risk analysis, transaction and controls testing, analytical procedures, in support of judgments and to provide insights They can draw on external market data such as third-party pricing sources, to re- price investments, for example Interest and foreign exchange rates, changes in GDP, and other growth metrics can also be used in analytical procedures Many data analytics routines can now easily be performed by auditors with little or no management involvement The ability to perform these analyses independently is important Many routines can be performed at a very detailed level, and/or in total The higher- level routines can be used for risk analysis to find a problem, while the more detailed analysis can be used to sharpen the focus, and provide audit evidence and/or insights.
Commonly performed data analytics routines:
- Comparing the last time an item was bought with the last time it was sold, for cost/NRV purposes.
- Inventory ageing and how many days inventory is in stock by item.
- Receivables and payables ageing and the reduction in overdue debt over time by customer.
- Analyses of revenue trends split by product or region.
- Analyses of gross margins and sales, highlighting items with negative margins.
- Matches of orders to cash and purchases to payments.
- ‘Can do did do testing’ of user codes to test whether segregation of duties is appropriate, and whether any inappropriate combinations of users have been involved in processing transactions.
- Detailed recalculations of depreciation on fixed assets by item, either using approximations (such as assuming sales and purchases are mid- month) or using the entire data set and exact dates.
- Analyses of capital expenditure repairs and maintenance.
- Three-way matches between purchase/sales orders, goods received/despatched documentation and invoices.
Some routines can provide audit evidence to support judgments relating to the appropriateness of methods used in calculating accounting estimates If a business has a policy of writing off any receivable over 90 days, for example, an analysis of the application of the method when credit notes are removed might result in the method appearing less appropriate if the routine shows that a large number of credit notes relate to billing errors.
Although there are many benefits to using DA and BD in auditing, there are also some challenges These challenges mainly depend on three major issues as follows: Firstly, the training and specialization of auditors; the second is the availability, relevance and, truthfulness of the data source; the third is the expectations of regulators and users of financial statements.
Regarding the first problem, the increasing rate of a large amount of data in which non-financial data accounts for a significant amount can overwhelm the auditor's ability to process information Skills such as pattern recognition and understanding how to assess anomalies have traditionally been no longer the primary focus of training in audit firms, often acquired through years of professional experience Typically, new graduate auditors from accredited institutions will be proficient in understanding how accounting rules apply and in understanding the audit risks associated with particular accounts For example, they can understand how an uncollected sale should be accounted for and understand the possibility of understatement of sales and accounts receivable. But they are often not trained to consider whether the transactions themselves make sense or to build revenue estimation models that will then allow them to recognize when an anomaly has occurred or more importantly how to track the anomaly once it is detected The concern of managers is that auditors will lack the necessary skills to appropriately apply project techniques, and firms will have to begin to expand their consulting services to attract and hire data scientists with DA skills.
There are several different ways companies can address their auditor’s expertise gaps, in addition to training them in project techniques One of those options is that the unit may have to outsource most of the project work from foreign analysis centers, and this outsourcing only provides the project output auditors to provide information for the decision to decide whether additional audit procedures are needed However, this issue also poses challenges, that is, the reliability of the outsourced parties, and the consent of customers for a third party to obtain their information is a great difficulty Another option involves creating tools that automate as much of the process as possible and classify anomalies into manageable groups so that the auditor can apply judgment in resolving the anomalies are that often detected effectively Auditors need to have an in-depth understanding of the client's accounting system for an appropriate assessment In addition, audit firms should also be aware of so-called “false positives” (e.g., a DA tool detects anomalies but these are legitimate transactions) and still must See if automated tools can eliminate “false positive” results or reduce them to a manageable level And too many such “false positives” will cause auditors to focus more on areas where there is ultimately no risk of material misstatement, and this reduces the effectiveness and efficiency of the audit process.
The second challenge focuses on data source availability, data ownership, and data integrity Many clients may lack the ability to collect data in a way that is useful to the auditor, or the data may be difficult to use Furthermore, data may be collected by the client, but it is unclear to what extent the auditors have access to it and how likely it is to be shared with the client This is a potential drawback in data mining for fraud detection, and their many clients allow auditors to access their databases directly Since big data can come from both internal and external sources, it is the auditor must evaluate the data originated from a secure source and whether it can be tampered with before the auditor collects it collect or not Completeness of the data set is also an issue As explained by Alles and Gray (2014, p 28), in fields outside of auditing, a bit of ambiguity or lack of quality of data sets may be tolerated, but for auditors,
‘‘allowing some inaccuracies to ‘slip in’ is difficult to reconcile with the focus in auditing on data integrity.’’
The third challenge concerns how DA is viewed by investors and regulators Over the years, the audit profession has addressed the gap between the expectations of the results and the audit significance of the users, and the,standards that are required by the auditors A gap in expectations occurs when users believe that the auditor can ensure that the financial statements are presented with a fair and honest opinion in all respects, but in reality, the auditor provides only a reasonable degree of assurance based on the basis for sampling transactions for testing With the ability to audit all transactions, DA can exacerbate the expectation gap problem Boards of directors and users of financial statements may require auditors to have a higher standard of fraud detection and liability in detecting errors in financial statements Under traditional auditing, the auditor has protections for undetected fraud if the sample selected does not have clear evidence of fraud Data mining can be considered equivalent to 100% sampling If the irrefutable evidence is in the sample, but the auditor has ignored it, then the auditors can no longer protect themselves under traditional due process safeguards In addition, with the focus of DA on non-financial information, managers are apprehensive about the possibility that auditors may focus less on audits for their clients and more on their non-audit service provision Finally, existing auditing standards have not been established to take the DA-based approach into account in the audit process, and standard- setters will have to consider the standards appropriate to the new methods For example, the standards by which the auditors draw conclusions based on sampling for the collection of evidence, for example, must change significantly to accommodate a 100% test of transactions, or the standards must be written to focus on checking the integrity and truthfulness of data.
Recommendations
Above are some benefits and challenges of applying DA and big data in financial statement audits The audit’s benefits are positive prospects for significantly improving audit quality through the adoption of DA and big data, but challenges are also barriers However, the increasing application of DA in audits demonstrates its role and need Businesses are investing in big data to improve their decision-making, and they hope auditors can leverage big data to improve audit effectiveness and efficiency Therefore, to face these challenges, the issues that need to be discussed are as follows:
5.2.1 Training on data analytics skills for audit staff
Training students who will be involved in the audit, and providing existing auditors with extensive skills to be able to effectively carry out a project, is one way that can contribute to solving this problem skills gaps and professional challenges related to big data and DA applications in auditing Like many meetings and seminars taking place, experts and researchers all agree with the message that accounting training programs need to focus more on training students in research skills and data science, such as statistics, data visualization tools, etc More advanced skills such as pattern recognition, critical thinking, and increased training in analytical processes should be encouraged at the same level Currently, major auditing firms in the world have also made significant investments in developing tools to help auditors work with big data without having to program themselves, so training programs should focus more deeply on ensuring students can understand the relationship between financial statements, business processes and external factors that pose a business risk to the entity Students also need to understand how financial information patterns can “tell the story” of a business's performance A deep understanding of not only how accounting works, but also why it happens, will help the auditor better analyze the data provided through visualization and thrive in the environment of big data.
In addition, universities training accounting majors need to innovate training programs and integrate general interdisciplinary knowledge and data science and data analysis skills for accounting students Educating students who are entering the public accounting profession and providing existing auditors with expanded skill sets to be able to perform data analysis effectively is another way academics can contribute to addressing the skills gap and expertise challenge associated with DA in auditing (PwC, 2015) Perhaps rather than invest in more statistics courses or hire faculty with backgrounds in big data,programs should focus more deeply on ensuring that students understand relationships between financial statement accounts, business processes, and how external factors drive business risk Students should also comprehend how patterns of financial information can tell a story about a company’s performance VACPA (Vietnam Association of Certified Public Accountants),which trains independent audit practitioners, also needs to update auditors' knowledge regularly every year, encouraging and rewarding the appropriate use of technology related to audits, including ADA among audit staff This may be part of a broader objective related to audit innovation or transformation It seems that a deep understanding of not only how accounting occurs but also why will enable auditors of the future to better analyze the data provided to them through visualizations and to thrive in a big data environment.
5.2.2 Support for auditing firms to apply big data and data analytics in professional practice
Analysts, managers in the field of accounting-auditing, international organizations, associations, and auditing firms all agree that the challenge of big data growth is also an opportunity to improve efficiency and resource allocation in accounting and auditing activities (ICAEW, 2019) In this regard, emphasis should be placed on the implementation stage where these techniques are discovered based on large-scale projects and information processes to understand the path to be taken and open future possibilities for the accounting profession In view of this challenge, we can distinguish foreseeable effects depending on the type of structure of the audit firms As a result, large firms will have no difficulty using DA in their client's management applications as they may sometimes use these systems in some audits With the high turnover and size of the companies they audit, so they will be able to make it easier to deploy profitable analytics and application systems.
However, for smaller audit firms, we find a different scenario, as a single firm will have difficulty in developing the analytic system and deriving sufficient profit from it, only it is possible to use it in limited audit work, and in many cases not enough to make investments in this technology To overcome this shortcoming, and for the entire audit industry to make new techniques available, it is necessary to develop transformational, i.e generic, applications that allow these applications to be used with a wide range of users Client and audit work, by some audit firms, offers an affordable price because their development and marketing costs can be shared by different firms in the field. Accordingly, it is necessary to have a plan to support audit firms to access and apply data and projects in professional practice.
5.2.3 Auditing firms need to consider carefully before investing in big data and data analytics
DA has been a significant area of investment for audit firms, mainly in the performance of consulting services, and more recently in audit services Many companies collect huge amounts of data about their customers, external environment, competitors and often don't know how to take the next step of analyzing and applying the data to run their businesses However, in a profession where the legal responsibility and audit environment are highly regulated by the law, this means that auditing firms will have to be careful when boldly investing in projects in the future that provides audit services (Liddy, 2014; Lombardi, Bloch, and Vasarhelyi, 2014).
Researchers should additionally investigate how the adoption of DA impacts the business risk of the audit firm itself from a liability standpoint (Gray & Debreceny, 2014) or from the standpoint of being subject to regulatory sanctions, which could range from fines to being driven out of the audit business altogether Research into investor expectations, juror decision-making, and board of directors’ perceptions of the level of assurance provided in DA audits versus traditional audits remain important avenues for future study Another area that academics may be able to address is how the client’s transaction information can be analyzed to detect errors in accounts For example, Vandervelde et al.(2008) modeled the relations between accounts to determine if account errors could be detected, and found that the relatedness of accounts is an important factor that auditors need to consider Relatedness refers to how accounts are recorded in the client’s records to ultimately create financial statements Accounting is based upon double-entry bookkeeping, where accounts are debited on one side and corresponding accounts are credited on the other side to create a symmetrical entry or balance Certain debit-credit relationships make sense, like debit to an asset account and a corresponding credit to a revenue account However, some relationships might appear unusual, such as a debit to an expense account and a credit to an owner’s equity account.
DA tools could flag these suspicious account relationships so that auditors can investigate them further As DA tools are being developed by firms, modeling relevant features similar to those in Vandervelde et al (2008) would be very informative In addition, researchers should examine whether economic data and social media data from external sources can be modeled to make predictions about factors that impact a client’s business, which will aid auditors in planning and making business risk assessments.
Alles, M., & Gray, G (2014) A framework for analyzing the potential role of big data in auditing: A synthesis of the literature (Working Paper) Rutgers, NJ:
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APPENDIX A: The process of implementing a typical contract at AASC Auditing Firm £aasc
Quy trình triển khai một hợp đồng điển hình
Chương trình dành cho sinh viên thực tập Tháng 12,2021
Trần Phương Thúy - KSCL&ĐT Ịaasc Quy trình triển khai một hợp đồng
Khảo sát, đánh giá khách hàng và Dự thảo, thống nhất và ký hợp đồng Công tác hậu cần cho việc thực hiện hợp đồng ị aasc Khảo sát, đánh giá KH và Dự thảo, ký hợp đồng
Thông tin so bộ vê khách hàng
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5 £ aasc Chuẩn bị hậu cần
Hỵp đồng djch vụ ị Yêu cầu cung cấp dịch vụ
Ban Giám đốc Đàn khào sát và đánh giá khách hàng
- Đăng ký xin VPP trên bitrix24 >> Lãnh đạo phòng phẽ duyờtằ Lắy VPP tại Lễ tõn (T2)
• Phương tiện đi lại, thẻ taxi:
- Đăng ký xin xe ụ tụ, thẻ taxi trờn bitrix24 ằLónh đạo phũng phờ duyệtằ Lễ tằn hoặc P.Tổng hợp
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-Đề nghị tạm ứng mẫu excel tại phòng >> Trưởng phũng ằ BTGĐ phụ trỏch phũng >> Kế toỏn (tầng
^ef £aasẹ Hoàn tất các công việc cuối cùng
Viết hóa đơn và lập thanh lý hợp đồng
APPENDIX B: Illustrative Data of Company A for the year 2021
Chi tiêu Mà sô Thu vét minh SÔ cuôi năm SÔ đâu năm
A TAI SAN NGAN HẠN ìoo 21,712,221,7 11.548.866.2
I Tiền và các khoăn tương đương tiền '110 2,667 1,941 381 867
2 Cãc khoản tương đương tiền ì 12 n Đầu tư tài chinh ngăn bạn '120
2 Dự phông giâm giã chửng khoán kinh 122
3 Đầu tư nắm giữ đến ngày đao hạn 123 m Các khoăn phãỉ thu ngân hạn 130 11,268,225,9 4,857,243,879
1 Phái thu ngăn hạn của khách hàng 131 VL03.a 3,294, 246,5
2 Trã trước cho người bân ngăn hạn 132 3 099
3 Phãi thu nội bộ ngăn hạn 133
4 Phãi thu theo tiên độ kế hoạch hợp đồng 134
5 Phái thu về cho vay ngắn hạn 135
6 Phái thu ngắn hạn khác '136 VI.04.a 4,273, 4,610,
7 Dự phông phái thu ngắn hạn khô đôi (*) '137
8 Tài sân thiếu chờ xữ lý '139 VI.05
IV Hàng tốn kbo '140 VL07 7,628,281,7 4.105,828,581
2 Dự phòng giâm giã hàng tồn kho (*) '149
V Tài sân ngăn bạn khác 150 148.237,801 644.411,947
1 Chi phi ưã trước ngắn hạn '151 VI.13a
2 Thuế GTGT được khấu trứ '152
3 Thuế và các khoản khác phái thu Nhà nước '153 VI.17.b 148237,801 644,411,94
4 Giao dịch mua bân lại trãi phiếu Chinh phủ '154
5 Tài sân ngắn hạn khác '155 VI.14.a
I Các khoản phải tbu dài bạn '210 10
1 Phái thu dài hạn của khách hàng ^11 VI.03.b
2 Trả trước cho người bân dai hạn ^12
3 Von kinh doanh ỡ đơn vị trực thuộc Ỉ13
4 Phái thu nội bộ dài hạn 514
5 Phái thu về cho vay dài hạn 515
6 Phái thu dài hạn khác 516 VI.04.b
7 Dự phỏng phải thu dai hạn khó đòi (*) 519 n Tài sân cô định 520 29,734,101,0 31,586,564,6
1 Tài sàn cổ định hừu hĩnh '221 VL09 12,307 13,311
- Giã trị hao mòn lũy kế (*) '223 (2,751, (1,747,400,
2 Tài sân cố định thuê tài chinh '224 VI.11
- Giã trị hao mòn lũy kế (*) '226
3 Tài sân cố định vồ hình '227 VI.10 17,426 18,2
- Giã trị hao mòn lũy kẻ (*) 529 (1,878, (1,029,983,
III Bât động sân đâu tư '230 VL12 37,347,906.6 40,778,355,4
- Giã trị hao mòn lũy kè (*) 532 (14,376,799 (10,946,35
IV Tài sàn dở dang dài bạn 240 VI.08 9.364.361.4 9.330.000.000
1 Chi phi sân xuẩt, kinh doanh dỡ dang dài 541 VI.08.a
2 Chi phi xày dựng cơ bân dỡ dang 542 VI.08.b 9,364, 9,330,
V Đâu tư tài cbinb dài bạn 550
1 Đàu tư vào công ty con 551
2 Đâu tư vào công ty Kên doanh, Hên kết 552
3 Đàu tư góp vốn vào đơn vị khác '253
4 Dự phông đầu tư tài chinh dài hạn (*) '254
5 Đầu tư nắm giữ đến ngày đáo hạn '255
VI Tài sân dài bạn khác '260 5.940.391,3 5,983,297,561
1 Chi phi ừã trước dài hạn '261 VI.13.b 5,940, 5,983,
2 Tài sân thuế thu nhập hoãn lại '262 VI.24.a
3 Thiết bi vật tư, phụ tùng thay thế dài hạn '263
4 Tài sân dài hạn khác '268 VI.14.b
NGUÒN VÓN c - NỢ PHAI TRA '300 36,247,074,069 31.888.417,391
1 Phái trả người bán ngấn hạn '311 VI 16.a 17,042,496, 10,124,229,
2 Người mua trả tiền trước ngăn hạn 512 1,158,476,0 174,513,38
3 Thuê và cãc khoản phải nộp Nhà nước 513 VI.17.a 3,460,062.7 7,254,197,1
4 Phải ưã người lao động 514 722,071,47 471,510,00
5 Chi phi phải ưã ngăn hạn 515 VI.18.a
6 Phải ưă nội bộ ngăn hạn 516
7 Phái ưã theo tiên độ kế hoạch họp đồng xây 517
8 Doanh thu chưa thực hiện ngắn hạn '318 VI.20.a
9 Phái ưả ngắn hạn khác '319 VI.19.a 13,863,967, 13,863,967,
10 Vay và nợ thuê tài chinh ngắn hạn '320
11 Dự phòng phái trà ngắn hạn '321 VI.23.a
12 Quỳ khen thường, phúc lợi 522
14 Giao dịch mua bản lại trãi phiêu chinh '324
1 Phái trả người bán dài hạn '331
2 Người mua ưă tiên trước dai hạn 532
3 Chi phi phải trà dài hạn 533 VI.18.b
4 Phải trả nội bộ về vốn kinh doanh 534
5 Phải trả nội bộ dài hạn 535
6 Doanh thu chưa thực hiện dài hạn 536 VI.20.b
7 Phải ưă dai hạn khác 537 VI.19.b
8 Vay và nợ thuế tài chinh dãi hạn '338
11 Thuế thu nhập hoãn lại phái trà '341 VI.24.b
12 Dự phòng phái trã dãi hạn '342 VI.23.b
13 Quy phát triển khoa học và còng nghệ '343
1 Vòn góp của chủ sờ hừu te 60,000 60,000,000,
- Cô phiếu phò thông cò quyền biêu quyẻt 411 60'000 60'000'000'
2 Thặng dư vòn cò phàn 412
3 Quyền chọn chuyên đôi trãi phiêu ^13
4 Vòn khác của chủ sỡ hừu ^14
6 Chênh lệch đánh giã lại tài sân ^16
7 Chênh lệch tỳ giã hối đoái ^17
8 Quỳ đầu tư phát triển ^18
9 Quỹ hồ trợ sắp xếp doanh nghiệp ^19
10 Quỳ khác thuộc vốn chủ sỡ hừu ^20
11 Lợi nhuận sau thuế chưa phàn phổi '421 7,851, 7,338,666,4
- LNST chưa phàn phổi lũy kể đến cuối kỳ 421 3,001, 3,001,924’
- LNST chưa phàn phổi kỳ này 421 4'849'983,1 4,336,741'6
12 Nguồn vốn đầu tư XDCB te n Nguồn kinh pbi và quỳ khác '430 41.28
2 Nguồn kinh phi đã hình thánh TSCĐ to
Mà sổ TÀI SẤN Thuyết 31-12-21 31-12-20 minh VXD VND
110 I Tiên và các khoăn tương đương tiên 3 2,667,476,333 1*941,381,867
130 IU Các khoăn phái thu ngán hạn 8,682,655,916 4*857*243*87
131 Phái thu ngắn hạn cũa 9 khách háng r 04 708,708,328 246,579,568
132 r 2 Trả trước cho người bân ngăn hạn
136 r 2 Phải thu ngắn hạn khác r 06 4,273,967,66
150 V Tài săn ngăn hạn khác 210,564,411 690,319,522
151 'ĩ Chi phi trả trước ngăn hạn 12 62,326,610 45*907,575
153 "2 Thuế và các khoăn khác phải thu Nhà nước 15 ỉ 48,237,80 ỉ 644,411,947
221 ì Tải sản cò định hữu hình [ 09 12,307
223 - Giã trị hao mòn htỹ kế Ợ) /2,751,075,083) (1,747,400,831)
227 r 2 Tài sân co định vô hình 10 17,426,176,9
229 - Giá trị hao mòn ìỉiỹ kế (*) fl,578,773,004) (1,029,983,628)
230 ỉỉỉ Bất dộng săn đầu tư 11 37,347,906,604 40,778,355,412
232 - Giá trị hao mòn ỉuỹ ke Ợ) (14,376,799,376) (10,946,350,568)
240 IV Tài sãn dớ dang dài hạn r 08 9,362,174,876 9,330,000,000
260 VI Tài sàn dài hạn khác 5,878,064,740 5*937,389,986
261 Y Chi phi trả trước dâi hạn 12 5,878,064,74
Mã số NGUỒN VÓN Thuyết minh
311 'ĩ Phải trả người bân ngăn hạn 13 14*456*926*618 10*124*229*67
312 Người mua trả tiền trước 0 ngắn hạn 14 1*158*476^01 174*513*385
313 Thuê vá các khoán phải nộp Nhá nước 15 3*460*062*79
314 '4 Phải trả người lao động 722,07 Ẹ470 471*510*000
319 5 Phái trã ngắn hạn khác 16 13,863*967*185 13,863*967*185
411 'ỉ Vòn góp của chú sờ hữu 60,000,000,000 60,000,000,000
421 Lọi nhuận sau thuế chưa phân phoi
42ỉa LNST chưa phân phối liiy kể đến cuối năm trước 7,338*666*493 9*187*517*91
42 ỉ b LNST chưa phân phồi năm nay' 51L055*049 6 (1*848^85L423)
Chi tiêu Mà số Thuyết minh Năm nay Năm trước
1 Doanh thu bán hàng và cung câp dịch vụ 01 vn.1 591,097 407,927,
2 Các khoản giảm trừ doanh thu 02 VII.2 208,848,531
3 Doanh thu thuần vê bán hàng và cung câp 10 591,097 40
4 Giá vôn hàng bán 11 VII.3 5 3
5 Lợi nhuận gộp vê bán hàng và cung câp 20 16,579, 9,684, ố Doanh thu hoạt động tài chính 21 VII.4 2,355,828 1,902,779
7 Chi phí tài chính 22 VII.5
- Trong đó: Chi plií lãi vay 23
8 Chi phí bán hàng 25 VII.8 12,542, 8,686,
9 Chi phí quân lý doanh nghiệp 26 VII.8 3,525,918,2 2,843,
10 Lợi nhuận thuần từ hoạt động kinh doanh
14 Tông lợi nhuận kê toán trước thuê (50 = 5 ú 513,2 (1.848.
15 Clii plií thuế TNDN hiện hành 51 VII.10
16 Clii plií thuế TNDN hoãn lại 52 VII.11
17 Lợi nhuận sau thuê thu nhập doanh 60 513,2 (1.848.
18 Lãi cơ bân trên cô phiêu ( + ) ■0
19 Lãi suy giâm trên cô pliiêu ( + ) 71
Mà số CHÌ TIẺl Thuyết minh
01 ì Doanh thu bán háng vá cung câp dịch vụ 19 591,097.891,615 407,927.930,163
02 "2 Các khoăn giám trừ doanh thu 20 0 208.848.531
10 3 Doanh thu thuần bán bâng vá cung cầp dịch vụ 591,097.891,615 407,719.081,632
20 5 Lợi nhuận gộp về bán háng vá cung câp dịch vụ 13,149.020 102 9.684.631,792
21 r 6 Doanh thu hoạt động tài 22 2.355.828 1.902.779
23 Tiong đó: Chí phí lãi vay 0 0
26 Chi phi quăn lý doanh 24 3 155.009.168 2 843
30 "10 Lợi nhuận thuân từ hoạt động kinh doanh 511.055.049 (1 843
50 14 Tông lọi nhuận kè toán trước thuê 511.055.049 (1.848 851
51 "ì 5 Chi phi thuê thu nhập doanh nghiệp hiện 25 0 423) 0
60 "17 Lọi nhuận sau thuê thu nhập doanh nghiệp 511.055.049 (1.848.851,423
5 Excerpt of Journal of entries 2021
Ngày hạch toán Diễn giải Tài khoản TK đói ứng Phát sinh Nợ Phát sinh cỏ
Bán hàng Khách hàng lè - Người mua không lấy hoá đơn 131 5111
Bán hàng Khách hàng lé - Người mua không lây hoá đơn 5111 131 2,732,645
Bán hàng Khách hàng lé - Người mua không lấy hoá đơn 131 3331 1 273,265
Bán hảng Khách hàng lè - Người mua không lẩy hoá đơn 33311 131 273,265
Bán hàng Khách hảng lé - Người mua không lây hoá đơn 131 5111 551,027
Bán hàng Khách hàng lè - Người mua không lẩy hoá đơn 5111 131 551,027
01/01/21 Mua hảng của Công ty 1561 331 3
01/01/21 Mua hàng của Công ty 331 1561 368,254,6
01/01/21 Mua hàng của Công ty 1331 331 36.825,460 00
01/01/21 Mua hàng cùa Công ty 331 1331 36,825,460
Tíển thướng sản lượng cho CBCNV Tháng 11-
Tiên thưởng sàn lượng cho CBCNV Tháng 11-
01/01/21 Tiên phạt của hô sơ đât 6429 331 46.000.000
01/01/21 Tìển phạt cùa hổ sơ đất 331 6429 46,000,000
01/01/21 Lệ phí môn bài năm 2021 cúa Công ty 6428 331 3,000,000
01/01/21 Lệ phí môn bải năm 2021 của cỏnẹ tỵ 331 6428 3,000,000
01/01/21 Xuât kho bán hàng Công ty 632 1561 91.039,610
01/01/21 Xuât kho bán hàng Công ty 1561 632 91,039,610
01/01/21 Xuất kho bán hàng Cồng ty 632 1561 180,464,5
01/01/21 Xuất kho bán hàng Công ty 1561 632 80 180,464,5
09/02/21 Xuầt kho bân hảng DNTN 632 1561 114,008,6 80
09/02/21 Xuất kho bán hàng DNTN 1561 632 70 114,008,6
09/02/21 Xuầt kho bán hàng DNTN 632 1561 172.662.3 70
09/02/21 Xuất kho bán hàng DNTN 1561 632 40 172,662,3
Bán hảng Khách hàng lẻ - Người mua không lảy hoá đơn 131 5111 37,327,273
Bán hảng Khách hàng lẻ - Người mua không lấy hoả đơn 5111 131 3 7,327,273
Bán hảng Khách hàng lé - Người mua khóng lây hoá đơn 131 33311 3,732,727
Bán hàng Khách hàng lè - Người mua không lây hoá đơn 33311 131 3,732,727
19/02/21 Bán hảng CÕNG TY TNHH MỘT THÁNH VIÊN 131 5111 60.800,000
19/02/21 Bán hảng CÕNG TY TNHH MỌT THÀNH VIÊN 5111 131 60,800,000
19/02/21 Bán hàng CÕNG TY TNHH MỌT THÀNH VIÊN 131 33311 6,080,000
19/02/21 Bán hảng CÕNG TY TNHH MỌT THÀNH VIÈN 33311 131 6,080,000
22/02/21 Thu tiên bán hàng CH 1121 131 11,507.9
22/02/21 Thu tiên bán hàng CH 131 1121 20 11,507,920
22/02/21 Thu tiên bán hàng CH 1121 131 12,161,510
22/02/21 Thu tiền bán hàng CH 131 1121 12,161,510
06/07/21 Thu tiên của Doanh Nghiệp Tư Nhân 1121 131 1,296,000,0 00
06/07/21 Thu tiển của Doanh Nghiệp Tư Nhân 131 1121 00 1,296,000,000
06/07/21 Chi tạm ứng làm giây PCCC xe 141 1111 4,000.000 00
06/07/21 Chi tạm ứng làm giây PCCC xe 1111 141 4,000,000
06/07/21 Chuyên thanh toán tiên hàng 331 1121 1,600,000,0 00
06/07/21 Chuyên thanh toán tiên hàng 1121 331 00 1,600.000,0
11/08/21 Mua hàng của Công ty 1331 331 303,895,5
11/08/21 Mua hàng của Công ty 331 1331 86 3 03.89
11/08/21 Mua hàng cùa Công ty 1561 331 1,189,872,6
11/08/21 Mua hàng cùa Công ty 331 1561 80 1,189,872,680
11/08/21 Mua hàng cùa Công ty 1331 331 118,987,2
11/08/21 Mua hàng của Công ty 331 1331 68 118.987,2
APPENDIX C: Illustrative Data of Company B for the year 2021 Đơn vị tinh: VND
Chi tiêu Mà số Thuyẽt minh Sõ cuõi quý Sổ đau nam
I Tiên và các khoản tương đương tiên ' 110 445,195,738 1,208,743,318
2 Các khoán tương đương tiên ' 112 n Đáu rư tài chính ngân hạn " 120 18,000,000,000 15,000,000,000
2 Dự phòng gĩãm giá chững khoán kinh doanh
3 Đàu tư nám giữ đèn ngáy đáo hạn ' 123 18,000,000,00
IU Các khoăn phãi thu ngăn hạn " 130 2.116.252.902 4.978.907.354 00
1 Phái thu ngăn hạn của khách háng ' 131 110,108,269 205,805,195
2 Trã trước cho người bán ngăn hạn ' 132 2,006,14
3 Phải thu nội bộ ngân hạn ' 133
4 Phải thu theo tiên độ kè hoạch hợp đòng xảy đựng ' 134
5 Phãi thu vê cho vay ngán hạn ' 135 4,000,000,00
6 Phai thu ngán hạn khác r 136 444,432,045 0
7 Dự phòng phải thu ngán hạn khó đòi (*) " 13?
8 Tái sân thiêu chờ xử lý ' 139
2 Dự phòng giâm giá hàng tồn kho (*) ' U3 (657,467,690) (897,004,675)
V Tài săn ngăn hạn khác " 150 570,255,108 554,082,717
1 Chi phi trà trước ngăn hạn ' 151 1,400,000
2 Thuế GTGT được khau trừ ' 152 23,192,392
3 Thuê và các khoán khác phái thu Nhà nước " 153 547,06
4 Giao dịch mua bán lại trái phiêu Chinh phủ ' 154
5 Tái sân ngăn hạn khác " 155
I Các khoán phái thu dái hạn ' 210 7
1 Phái thu dái hạn của khách hàng ’ 211
2 Trả trước cho người bán dái hạn ' 212
3 Vòn kinh doanh ớ đon vị trực r 213
4 Phãi thu nội bộ dài hạn ' 214
5 Phải thu vê cho vay dải hạn ' 213
6 Phải thu dãi hạn khác ' 210
7 Dự phóng phải thu dái hạn khó ' 219
II Tái sân cò định " 220 3303323.478 3,878,42
- Giá trị hao môn lũy kê (*) ' 223 (5 450 565 958) (6.339.3
- Giá trị hao món lũy kê (*) ' 226
- Giá trị hao mòn lũy kê (*) ' 229 (148471 433) (120363
III Bát động săn đàu tu r 230
- Giá tậ hao mòn luỹ kẻ (*) ' 232
IV Tài sân dỡ dang dai hạn ' 240
1 Chi phi tán xuãt, kinh doanh dớ ' 241
2 Chi phi xây dựng co băn dó dang ' 242
V Đáu tư tái chinh dái hạn ' 250
1 Đàu tư váo công ty con " 231
2 Đâu tư vào công ty hèn doanh, ' 232
3 Đâu tư gõp vòn vào đơn vị khác ' 253
4 Dự phòng đàu tư tài chinh dài ' 254
5 Đàu tư năm giữ đèn ngây đáo r 253
VI Tái sán dái hạn khác ' 260 432,465,442 143,612,165
1 Chi phi trá trước dái hạn ' 261 432,465,442 143,6124
2 Tái sán thuê thu nhập hoàn lại ' 262
3 Thièt bị vật tư, phụ tùng thay ' 263
4 Tài sân dãi hạn khác '
TONG CỌNG TAI SAN (2700+200) ' 270 25,651,114,051 26.654.812,539 c - NỢ PHAI TRÁ ' 300 492,435,073 484,568,3
1 Phái trả người bân ngăn hạn ' 311
2 Người mua trả tiên trước ngăn ' 312 200,0 268,1
3 Thuê vi các khoản phái nộp ' 313 31'200 2
4 Phải trả người lao động ' 95'32 53
5 Chi phi phải trả ngan hạn '
6 Phải trã nội bộ ngan hạn ' ứ 31
7 Phải trả theo tiên độ kê hoạch ' 317
8 Doanh thu chưa thực hiện ngăn '
9 Phải trả ngăn hạn khác ' 159,592,9 15 7
10 Vạy và nợ thuê tải chinh ngăn ' 320
11 Dự phòng phải tri ngăn hạn ' 321
12 Quỵ khen thưởng, phúc lợi ' 322 3,246260 3,246,26
14 Giao dịch mua băn lại trãi ' n Nọ dài hạn ' 330
1 Phái trả người băn dãi hạn ' 331
2 Người mua trả tiên trước dãi ’ 332
3 Chi phi phải trả dải hạn ’ 333
4 Phải trã nội bộ vẽ vòn kinh '
5 Phải trã nội bộ dãi hạn ’ 335
6 Doanh thu chưa thực hiện dãi ' 336
7 Phải trả dãi hạn khác '
8 Vay và nợ thuê tài chinh dài ' 338
11 Thuê thu nhập hoàn lại phải '
12 Dự phòng phải tri dãi hạn '
13 Quỳ phát triển khoa học vả '
1 Vòn góp của chũ sờ hửu r 411 23,699, 23,699,140,00
- Cô phiêu phô thòng cỏ quyên 41 23'69914 23,699,1 0
- Vốn góp cùa đôi tượng khác 41
2 Thặng dư vòn cò phàn r 412
3 Quyên chọn chuyên đôi trãi r 413
4 Vốn khác của chũ sờ hừu ' 414
6 Chênh lệch đánh già lại tâi sân ’ 416
7 Chênh lệch tỹ gia hời đoái ' 417
8 Quỵ đàu tư phát triên ' 418 11,6812 11,681,2
9 Quỵ hò trợ săp xêp doanh ’ 419
10 Quỷ khác thuộc vòn chú sờ ’ 420
11 Lợi nhuận sau thuê chưa phàn ' 421 (10221,70 (9,210,1
- LNST chưa phàn phôi lũy kè 42 (9210 (6
- LNST chưa phàn phôi kỳ nảy - 2 2:: 5 (2 98 1 2
12 Nguôn vòn đâu tư xây dựng r 422
II Nguòu kinh phí rà quỳ khác r 430
2 Nguòn kinh phi đà hình thành r 432
110 I Tiên và các khoăn tưưng đương tiên " 03 6,445,195,738 1,208,743,318
120 II Đâu tư tài chinh ngàn hạn r 04 12,000,000.000 15'000,000,000
123 Đàu tư năm giữ đèn ngày đáo hạn 12,000,000,000 15,000,000,000
- Tiền gúi có ký' hạn 12,000,000 000 15'000'000'000
- Các khoán đầu tư khác nắm giữ đến ngày đáo hạn 0 0
130 m Các khoăn phái thu ngân bạn 2,416,074,544 4,978.907,354
131 Y Phải thu ngân hạn của khách háng " 05 110,108,269 205,805,195
132 r 2 Trả trước cho người bán ngăn hạn r 06 2,006,144,633 328370314
135 '3 Phái thu về cho vay ngăn hạn 0 4,000'000'000
136 ■4 Phải thu ngăn hạn khác r 07 299,821,642 444,432,045
- Hàng mua đang đĩ đường 0 0
- Chì phi sàn xuắt kình doanh dỡ dang 149,081,508 164,669,982
- Hàng hoá kho bão thuế 0 0
- Hàng hoá bắt động sán 0 0
149 ■6 Dự phòng giám giá hàng tồn kho (65 7,467,690)_ (897,004,675
150 V Tài săn ngăn hạn khác 644.896,425 ) 554'082,717
151 'í Chi phi trả trước ngăn hạn 11 74,641,317 1,400,000
152 *2 Thuế GTGT được khấu trữ 23,192,392 0
153 3 Thuế và cãc khoăn khác phái thu Nhà nước 13 547,062,716 552,682,717
221 1 Tài săn co định hữu hình r 09 3,278,554,966 3,826,051,009
223 - Giá trị hao mòn ìtiỹ kế (5,450,565,958) (6,339,889,915)
227 r 2 Tài săn cò định vô hình 10 24,768,512 52376363
229 - Giá trị hao mòn luỹ kế (148,171,48 8)_ (120363^37)
- Tiền gũi có kỳ hạn 0 0
- Các khoán đầu tư khác nắm giữ đến ngày đáo hạn 0 0
260 VI Tài sãn dài hạn khác 357,824,125 143,612,165
261 Y Chi phi frã trước dài hạn 11 357,824,125 143,612,165
Mà sổ NGUÔN VỐN Thuyết
31 Người mua tră tiền trước ngắn hạn 12 200,067,001 268,152,492
313 "2 Thuê và cãc khoăn phải nộp Nhà nước 13 31,200,172 2*800*41
- Thuế Giá trị gia 8 tăng 0 0
Thuế Tiètt thụ đặc biệt 0 0
- Thuế Xuất khâu, Nhập khâu 0 0
- Thuế Thu nhập doanh nghiệp 0 0
- Thuế Thu nhập cá nhân 31,200,172 2,800,418
- Thuế Nhà đắt, Tiền thuê đất 0 0
- Thuế bão vệ môi trường 0 0
- Phi, ìệ phỉ và các khoán phãt nộp khác 0 0
314 "4 Phải trà người lao động 98,328,670 53,129,227
319 5 Phãi trà ngăn hạn khác 14 159*592*97
- Dự phóng bão hành sán phẵm hàng hóa 0 0
- Dự phòng bão hành cõng trình xây dựng 0 0
- Dự phóng tái cơ cấu doanh nghiệp 0 0
- Dự phòng phãi trà khác 0 0
32 6 Quỹ khen thường phúc lọi 3,246,260 3,246,260
- Ọuỹphát triẻn khoa học và công nghệ 0 0
Qỉiỹphát trién khoa học và công nghệ đã hình thành TS 0 0
41 Von góp cũa chủ sỡ hữu 23,699,140,000 23,699,140,000
4ỉỉa Cồ phiếu phổ thông có quyền biểu quyết 23,699440,000 23,699,140,000
418 I 2 Quỹ đầu tư phát triền 11,681,247,698 11*681447*698
421 3 Lọi nhuận sau thuế chưa phàn phối
421a LNST chưa phân phối ìũy kể đến cuối năm trước (9*210*143*49
LNST chưa phân phổi năm nay (711*743482) (2*981,264*058
Chì tiêu Mà sổ Năm nay Nã LU trước
1 Doanh thu bán hàng và cung càp dịch vụ F Ũ1 3,3347 4,051,3
- Doanh thu bán hàng và cung càp dịch vụ 01A 3.334,7 4,051,3
- Thuế TTĐB: thuế XK: thuế GTGT tr t phai 01B 0 0
- Giâm trừ doanh thu bán hàng 01C 0 0
2 Các khoan giâm trừ doanh thu F Q2 0 21,857,139
+ Hàng bán bị trả lại 02C 0 20,000,000
3 Doanh thu thuân vê bán hàng và cung cảp ìo 3.334,7 4,029'5
5 Lợi nhuận gộp vê bán hàng và cung càp F 20 1,330,8 -206,116,659
6 Doanh thu hoạt động tài chinh F 21 1,383,8 1,256.758.05
- Trong đó: Chi phi lài vay F 23 0 0
9 Chi phi quân lý doanh nghiệp ^6 2,815'9 2'477'8
10 Lợi nhuận thuần từ hoạt động kinh F 30 - -
14 Tòng lợi nhuận kẻ toán trước thuê F 50 - -
15 Chi phi thuế TNDN hiện hành F 51 0 0
15 Chi phi thuế TNDN hiện hành 51A ũ 0
15 Chi phi thuế TNDN hiện hành 51B 0 ũ
16 Chi phi thuế TNDN hoãn lại F 52 0 0
- Chi phi thuê TNDN hoàn lại phai trả 52A 0 0
- Chi phi thuê TNDN hoẫn lại phai thu 52B 0 0
17 Lợi nhuận sau thuê thu nhập doanh F ỄŨ - -
18 Lãi cơ bân trên cô phiêu F 70 -300 -1,258
19 Lãi suy giâm trẻn cò phiêu F 71 Ũ 0
01 ì Doanh thu bán hàng và cung càp dịch VND vụ 17 3,334,723,4 4,051,368,1
- Hàng bán bị trả ỉại 0 20,000,000
10 3 Doanh thu thuần bán hang và cung cắp dịch vụ 3,334,723,4 4,029,510,9
20 5 Lợi nhuận gộp vè bán háng và cung câp dịch vụ 1,330,860,8 (206,116,65
21 F ổ Doanh thu hoạt động tài 20 1,383,828,7 1,256,758,0
23 Trong đó: Chi phí lãi vay ' 0 0
26 9 Chi phí quản lý doanh 22 2,815,939,4 2,477,813,6
30 ìo Lợi nhuận thuần từ hoạt động kinh doanh (548,712,83 (2,759,6
50 Ì4 Tông lợi nhuận kê toán trước thuê (711,743,58 (2,981,2
51 F ^ chi phi thuế thu nhập doanh nghiệp 25 ũ 0
52 F 16 Chỉ phí thuế thu nhập doanh nghiệp 0 0
60 '17 Lợi nhuận sau thuê thu nhập doanh nghiệp (711,743,58 (2,981,2
70 18 Làl Cữ bàn trên cổ phiếu 26 (300) (1,258)