Machine learning More science than fiction © The Association of Chartered Certified Accountants April 2019 About ACCA ACCA (the Association of Chartered Certified Accountants) is the global body for p.
Machine learning More science than fiction About ACCA ACCA (the Association of Chartered Certified Accountants) is the global body for professional accountants, offering business-relevant, first-choice qualifications to people of application, ability and ambition around the world who seek a rewarding career in accountancy, finance and management ACCA supports its 208,000 members and 503,000 students in 179 countries, helping them to develop successful careers in accounting and business, with the skills required by employers ACCA works through a network of 104 offices and centres and more than 7,300 Approved Employers worldwide, who provide high standards of employee learning and development Through its public interest remit, ACCA promotes appropriate regulation of accounting and conducts relevant research to ensure accountancy continues to grow in reputation and influence ACCA is currently introducing major innovations to its flagship qualification to ensure its members and future members continue to be the most valued, up to date and sought-after accountancy professionals globally Founded in 1904, ACCA has consistently held unique core values: opportunity, diversity, innovation, integrity and accountability More information is here: www.accaglobal.com © The Association of Chartered Certified Accountants April 2019 Machine learning More science than fiction About this report This report is an introduction to machine learning, with particular emphasis on the needs of the accountancy profession In addition to an overview of what it is, the findings inform perspectives on how it can be applied, ethical considerations and implications for future skills FOR FURTHER INFORMATION: Narayanan Vaidyanathan Head of Business Insights, ACCA Foreword The impact of digital on the accountancy profession is an important, current thematic focus for ACCA that permeates everything we think about and It is a focus on ourselves as an organisation, as much as on our thought-leadership for wider best practice As an organisation, ACCA incorporates digital applications in both the content and delivery of its training programmes Our course content emphasises the need for professional accountants to develop an appreciation of a range of technology topics, from analytics to artificial intelligence The ACCA qualification and continuing professional development (CPD) offerings are committed to a digital approach: online and flexible, designed to give the best service to our members and students in over 180 countries Our thought leadership work builds on this organisational focus on digital applications The perspectives on machine learning offered in this report are the latest addition to a strong portfolio of research covering technologies from robotic process automation to blockchain The report offers an accessible, practical introduction to the basics of machine learning, and how it is being adopted within the accountancy profession It also explores issues of ethics and other concerns pertinent to the public interest These concerns are integral to ACCA’s mission, and our dialogue with regulators, standard setters, partners, members and students Our aim is to provide a considered and thoughtful voice, in an often over-hyped debate about the danger that artificial intelligence will take over the world We are hopeful that this report will be a useful resource for our stakeholders and play its part in supporting a meaningful and constructive debate Alan Hatfield Executive Director, Strategy and Development Contents Executive summary Introduction 8 Machine learning and accountancy 10 Navigating the terminology 12 Applications of machine learning 18 Ethical considerations 25 Skills in a machine learning environment 35 Conclusion 37 Appendix 38 Appendix – Country snapshots 39 UK 40 China 41 Malaysia 42 Singapore 43 United Arab Emirates (UAE) 44 Ireland 45 Pakistan 46 Appendix DISCLAIMER Parts of this report make reference to machine learning products or other initiatives from third parties This is done for information purposes in response to requests for real-world examples The report does not constitute an endorsement of the particular products or initiatives mentioned or a complete list thereof 47 Executive summary Artificial intelligence (AI) is having a big impact on public consciousness And machine learning (ML), which uses mathematical algorithms to crunch large data sets, is being increasingly explored for business applications in AI-led decision making This follows several years in the wilderness, where the prevailing belief was that AI was the stuff of movie fantasy Now, with access to far more data and far more processing power than ever before, ML seems set to challenge that view This is an area with plenty of terminology and a minefield of differing interpretations as to what they mean ACCA’s survey of members and affiliates reflected this challenge when asked about their understanding of terms such as AI, ML, natural language processing (NLP), data analytics and robotic process automation (RPA) On average for any given term: 62% of respondents had not heard of it, or had heard the term but didn’t know what it was or had only a basic understanding, 13% of respondents had a high or expert level of understanding This suggests a lot of potential for greater education and awareness building among the accountancy community around the world One way to describe AI is the ability of machines to exhibit human-like capabilities in areas related to thinking, understanding, reasoning, learning or perception ML is a sub-set of AI that is generally understood as the ability of the system to make predictions or decisions based on the analysis of a large historical dataset Essentially, ML involves the machine, over time, being able to learn the characteristics of data sets and identify the characteristics of individual data points In doing so, it ‘learns’ in the sense that the outcomes are not explicitly programmed in advance They are arrived at by the ML algorithm as it is exposed to more data and determines correlations therein Machine Learning: More science than fiction | As with any technology, with power comes responsibility And in the case of machine learning, ethical considerations are never far away Executive summary The report begins with an introduction to the basics This is because it is important to have some appreciation of what these applications are doing, to be able to trust such systems and to understand how machine learning can be a step towards developing a greater level of machine intelligence In this context, ‘intelligence’ refers to the ability of the technology, in certain circumstances, to make decisions or draw inferences, without there being an instruction to treat a given dataset in a fixed, predetermined way But it does not mean that the technology has suddenly developed an independent consciousness – this is not about robots going on the rampage! The market is recognising the power of ML with in respondents stating that their organisations are engaged with this technology in some way This includes those who stated that their organisations are in full production mode dealing with live data (6%), advanced testing with ‘go-live’ within 3-6 months (3%), early stage preparation with go-live within 12 months (8%) and in initial discussions exploring concepts/ideas (24%) Applications for adoption range across diverse areas, including for example, invoice coding, fraud detection, corporate reporting, taxation and working capital management The report explores various products and initiatives across these areas These findings reinforce the need for the accountancy profession to prioritise building awareness and understanding in this area, as organisations will increasingly need these skills In fact the biggest barrier to adoption cited in the survey was the lack of skilled staff to lead the adoption (52%) As with any technology, with power comes responsibility And in the case of ML, ethical questions are never far away Professional accountants need to consider, and appropriately manage, potential ethical compromises that may result from decision making by an algorithm Who has accountability in this situation? What is the risk of bias, given that ML algorithms will inevitably reflect any bias in the data sets that feed them? About in 10 respondents were of the view that organisations have a responsibility for some form of disclosure to highlight when a decision has been made by a ML algorithm The report considers a range of ethical considerations relevant to professional accountants, using for guidance, the fundamental principles established by the International Ethics Standards Board for Accountants (IESBA) The ability of AI to take over jobs is a narrative often recited in the media And there is certainly some truth about the ability of these technologies to a variety of tasks more efficiently – indeed, as mentioned above, this report specifically explores some of these areas But even sophisticated technology such as AI appears to struggle with the full contextual understanding and integrated thinking of which humans are capable Despite advancements in AI, it does not yet appear to be the case that human oversight can be done away with completely; or that the technology can take into account human factors, such as when building client relationships or leading successful teams ACCA’s work on the emotional quotient (EQ) strongly demonstrated the need, in a digital age, for competencies related to emotional intelligence (ACCA 2018) In fact as we look ahead, the Digital Quotient (DQ) and EQ are best seen combined for either to be really effective for professional accountants Even outside behavioural areas such as leadership, core technical activities require judgement and interpretation that draw on multiple considerations ML can provide truly insightful information, using sophisticated algorithms to analyse historical data sets But in some situations, a human may choose to take note of this but for perfectly valid reasons, make decisions based on additional/other factors, that not follows patterns seen in the past Looking ahead, professional accountants have an opportunity to develop a core understanding of emerging technologies, while continually building their interpretative, contextual and relationshipled skills They can then truly benefit from the ability of technologies such as ML to support them in the intelligent analysis of vast amounts of data Introduction Machine learning (ML) is part of an umbrella of terms used when there is a reference to artificial intelligence (AI), the latter term having been coined as far back as 1956 Most early AI work relied on a ‘decision tree’ approach to mapping options, for example, in chess, mapping all possible opening moves and subsequent countermoves With even relatively simple problems, such as a retailer making customer-specific recommendations, the vast number of options in a decision tree led to a combinational explosion that could not be processed by even the most capable hardware This created a series of disappointments about AI, a so-called ‘AI winter’, where computing capability lagged behind theoretical approaches and fell significantly short of hopes for the creation of usable applications In recent years, however, AI has enjoyed renewed interest This is not science fiction; rather it is now increasingly found in consumer technologies and business applications So what has caused this? It is worth interrogating this observation Data-driven insight is at the heart of the ‘intelligence’ driving AI And it is the exponential increase in the availability of data and unprecedented computing power for processing this data that have jointly contributed to moving AI increasingly from fiction to fact Broadly speaking, there are two levels of AI – specific or weak and general As it currently exists, the term ‘AI’ refers to weak AI This means the use of AI in solutionspecific applications, for example in identifying patterns within a large volume of transactions What is not currently possible is artificial general intelligence – the sort of AI often depicted in films and television, with robots displaying humanlike intelligence and characteristics While there are some who believe this latter type of so-called ‘sentient’ understanding may one day be possible, current technological reality appears to be far away from this As many experts have noted1, high-performance adultlevel intelligence for a single activity, such as needed for playing chess, can be easier to model than human mobility or perception – even that of an infant 1 Referred to often as Moravec’s Paradox, the discovery by artificial intelligence and robotics researchers Hans Moravec, Rodney Brooks and Marvin Minsky in the 1980’s that, contrary to traditional assumptions, high-level reasoning requires very little computation, but low-level sensorimotor skills require enormous computational resources As a finance professional it is important to develop an appreciation of all this, given that machine learning is being increasingly used in accounting software and business process applications This report aims to aid the process of developing this understanding • Andreas Georgiou, Sage • Ruth Preedy, PwC • Dorothy Toh, King’s College London • Shamus Rae, KPMG • Lisa Webley, University of Birmingham • Stuart Cobbe, Brevis • Maria Mora, Fujitsu • Thomas Toomse-Smith, Financial Reporting Lab The report provides an introductory, end-to-end perspective on ML It explains the basics of what it is, and identifies use-cases where this technology is being deployed It further delves into the ethical issues the finance professional may need to consider, and implications of the technology for the future skills required in the profession In addition to inputs from experts in the field and ACCA’s technology research more broadly, the report is informed by a survey of 1,897 ACCA members and affiliates, and a roundtable discussion on ‘ethics in machine learning’ conducted in conjunction with the Financial Reporting Lab, the learning and innovation hub of the Financial Reporting Council, UK We are grateful to the following delegates for sharing their views at the roundtable: Machine learning and accountancy Double-entry accounting traces its roots to the medieval period, and from that time onwards it has served as the worldwide basis for business record-keeping The business processes by which those records are created, and by which independent auditors evaluate the accuracy and completeness of those records, have evolved over time Despite this, an accountant from the late 1500s and one from the late 1900s would have had enough assumptions in common, linked to the double-entry approach, to allow them to have a professional conversation in a meaningful way So accountancy practices have broadly been keeping pace and evolving with developments over the last 500 years, while retaining some common elements over time And the question now is how might technologies such as ML create the next big transformation? The view from ACCA’s survey is that AI is currently perceived as more ‘hype’ than reality; but that this is set to change in the relatively near future (Figure 1.1) As of mid-2018, the online publishing platform Medium reported that there were over 3,400 AI/ML start-ups around the world As with any new venture, the vast majority of these will fail, and many will so because they are ‘solutions’ in search of problems, rather than actual solutions to a specific set of business problems or needs ML is capable of many amazing things but accountants really have a need for any of those amazing things to the job well? On the whole, the answer appears to be ‘yes’, and this is not just a matter of staying current The capabilities that machine learning offers could assist the work of professional accountants in various ways over time One of the key drivers of this is the proliferation of data FIGURE 1.1: Artificial Intelligence: ‘Hype’ versus reality based on what can be seen in the working environment n 70% Now n years’ time 60% 58% 50% 40% 30% 34% 26% 20% 10% 13% All / Mostly hype Mostly / Entirely reality Note: remaining respondents said ‘Equal hype and reality’ 10 Appendix The fundamental principles are described in the Code established by IESBA and are as follows a Integrity – to be straightforward and honest in all professional and business relationships b Objectivity – not to compromise professional or business judgements because of bias, conflict of interest or undue influence of others c Professional competence and due care: i to attain and maintain professional knowledge and skill at the level required to ensure that a client or employing organisation receives competent professional service, based on current technical and professional standards and relevant legislation, and ii to act diligently and in accordance with applicable technical and professional standards d Confidentiality – to respect the confidentiality of information acquired as a result of professional and business relationships e Professional behaviour – to comply with relevant laws and regulations and avoid any conduct that the professional accountant knows or should know might discredit the profession 38 Appendix Country snapshots UK 40 China 41 Malaysia 42 Singapore 43 United Arab Emirates (UAE) 44 Ireland 45 Pakistan 46 39 Machine Learning: More science than fiction | Appendix – Country snapshots UK UK 1: AI as hype or reality n 70% Now n UK 2: Who in the organisation should own the process and data gathering associated with the use of ML? XX% Global years’ time 60% 58% 50% 40% 26% 20% 13% 10% 14% 30% 23% 37% 40% 20% 10% All / Mostly hype Global 50% 40% 30% n UK 60% 53% 34% 40% n 70% Mostly / Entirely reality Note: remaining respondents said ‘Equal hype and reality’ Finance/ accountants 13% 14% 14% 15% 12% 13% IT Strategy team Business unit 19% 5% 12% Don’t know 5% Another team UK 3: Machine learning (ML) adoption in organisation n 50% 40% 44% 30% 20% 10% No plans for adoption 25% 24% Global 21% Don’t know Initial discussions and exploring concepts/ideas UK 4: Role of ML in applying judgement appropriately in complex scenarios? n NET: ML leads/full reliance UK n 6% 8% 6% 4% Early stage preparation with ‘go-live’ within 12 months 2% Full production mode dealing with live data 3% Advanced testing with ‘go-live’ within 3-6 months UK 5: Please tell us about the main barriers to using ML in the organisation more generally n Global 50% UK n Global 70% 60% 20% 31% 35% 25% 50% 29% 21% 10% 10% 16% 7% 10% Se a lec ou ppli tion nti ca ng tio and po n o lici f es Bu sin eg es fut s ju d u for re p gem the ros en bu pec t sin ts ess eg Ethic tec scen al jud hn ari ge no ically os th men tc lea lega at ar t r if l, b e eth ut ica l acc Re c ass dere ogni ets co tion an gnit an dl d iab ion o ilit f ies P an rese d d nt isc atio los n ure acc M ou eas nti ure ng m est ent im of ate s 40% 24% 52% 44% 46% 49% 30% 20% 10% 22% 24% 23% 21% 24% 19% 16% 17% 12% 14% 11% 11% L to ack lea of d t skil he led ad op staff t io n Co st im pli cat ion s Po or qu alit yo fd ata No c fro lear b m usi ene ng fit ML Do n’t kn ow /no ba rrie rs 33% 36% vo Ins lum uffi e o cie f d nt ata Re gu lat req ory uir /leg em en al ts Eth ica ld ile mm as 40% 30% n 38% 20% UK 40 Machine Learning: More science than fiction | Appendix – Country snapshots China CHINA 1: AI as hype or reality n 70% Now n CHINA 2: Who in the organisation should own the process and data gathering associated with the use of ML? XX% Global years’ time 60% 58% 50% 54% n 70% n China Global 60% 50% 40% 34% 30% 20% 30% 23% 13% 10% 40% 26% 30% 20% 12% 38% 40% 14% 14% 10% All / Mostly hype Mostly / Entirely reality Note: remaining respondents said ‘Equal hype and reality’ Finance/ accountants IT 21% 15% 11% 13% Strategy team Business unit 12% 12% 3% Don’t know 5% Another team CHINA 3: Machine learning (ML) adoption in organisation n 50% 42% 30% 24% 19% 10% 20% 21% 10% No plans for adoption Initial discussions and exploring concepts/ideas Don’t know CHINA 4: Role of ML in applying judgement appropriately in complex scenarios? n NET: ML leads/full reliance China n 8% 6% 5% Early stage preparation with ‘go-live’ within 12 months 4% Full production mode dealing with live data 3% Advanced testing with ‘go-live’ within 3-6 months CHINA 5: Please tell us about the main barriers to using ML in the organisation more generally n Global 50% China n Global 70% 60% 34% 36% 34% 35% 26% 20% 50% 29% 27% 15% 16% 10% 15% 10% Se a lec ou ppli tion nti ca ng tio and po n o lici f es Bu sin eg es fut s ju d u for re p gem the ros en bu pec t sin ts ess eg Ethic tec scen al jud hn ari ge no ically os th men tc lea lega at ar t r if l, b e eth ut ica l acc Re c ass dere ogni ets co tion an gnit an dl d iab ion o ilit f ies P an rese d d nt isc atio los n ure acc M ou eas nti ure ng m est ent im of ate s 40% 24% 57% 52% 55% 49% 30% 20% 10% 23% 24% 27% 21% 19% 10% 22% 17% 18% 14% 5% 11% vo Ins lum uffi e o cie f d nt ata Re gu lat req ory uir /leg em en al ts Eth ica ld ile mm as 40% 30% Global 38% 20% n L to ack lea of d t skil he led ad op staff t io n Co st im pli cat ion s Po or qu alit yo fd ata No c fro lear b m usi ene ng fit ML Do n’t kn ow /no ba rrie rs 40% China 41 Machine Learning: More science than fiction | Appendix – Country snapshots Malaysia MALAYSIA 1: AI as hype or reality n 70% Now n MALAYSIA 2: Who in the organisation should own the process and data gathering associated with the use of ML? XX% Global years’ time 60% 50% 58% 70% 59% 60% n n Malaysia Global 50% 40% 34% 30% 29% 20% 26% 40% 27% 30% 13% 10% 40% 20% 11% 34% 10% All / Mostly hype Mostly / Entirely reality Note: remaining respondents said ‘Equal hype and reality’ Finance/ accountants 14% 14% 16% 15% 16% IT Strategy team Business unit 18% 13% 12% 2% Don’t know 5% Another team MALAYSIA 3: Machine learning (ML) adoption in organisation n 50% Malaysia n Global 40% 38% 20% 20% 10% 21% 10% No plans for adoption Don’t know Initial discussions and exploring concepts/ideas MALAYSIA 4: Role of ML in applying judgement appropriately in complex scenarios? n NET: ML leads/full reliance Malaysia n 30% Early stage preparation with ‘go-live’ within 12 months 36% 40% 35% 35% 30% 29% 10% 21% 40% 16% 17% 10% Se a lec ou ppli tion nti ca ng tio and po n o lici f es Bu sin eg es fut s ju d u for re p gem the ros en bu pec t sin ts ess eg Ethic tec scen al jud hn ari ge no ically os th men tc lea lega at ar t r if l, b e eth ut ica l acc Re c ass dere ogni ets co tion an gnit an dl d iab ion o ilit f ies P an rese d d nt isc atio los n ure acc M ou eas nti ure ng m est ent im of ate s 50% 24% 2% Full production mode dealing with live data n 60% 20% 6% 3% Advanced testing with ‘go-live’ within 3-6 months MALAYSIA 5: Please tell us about the main barriers to using ML in the organisation more generally 70% 41% 5% Global 50% 40% 8% 30% 20% 10% Malaysia n Global 69% 52% 54% 49% 32% 24% 21% 21% 14% 19% 19% 17% 10% 14% 14% 11% L to ack lea of d t skil he led ad op staff t io n Co st im pli cat ion s Po or qu alit yo fd ata No c fro lear b m usi ene ng fit ML Do n’t kn ow /no ba rrie rs 28% 24% vo Ins lum uffi e o cie f d nt ata Re gu lat req ory uir /leg em en al ts Eth ica ld ile mm as 36% 30% 42 Machine Learning: More science than fiction | Appendix – Country snapshots Singapore SINGAPORE 1: AI as hype or reality n 70% Now n SINGAPORE 2: Who in the organisation should own the process and data gathering associated with the use of ML? XX% Global years’ time 58% 60% 40% 20% 11% 46% 30% 25% 13% 10% 40% 26% 32% 20% Global 50% 34% 30% n Singapore 60% 50% 40% n 70% 63% 10% All / Mostly hype Mostly / Entirely reality Note: remaining respondents said ‘Equal hype and reality’ 11% Finance/ accountants 14% IT 16% 15% Strategy team 22% 13% Business unit 2% 12% 3% Don’t know 5% Another team SINGAPORE 3: Machine learning (ML) adoption in organisation n 50% Singapore n Global 40% 35% 30% 38% 27% 20% 24% 21% 10% No plans for adoption 13% Don’t know Initial discussions and exploring concepts/ideas SINGAPORE 4: Role of ML in applying judgement appropriately in complex scenarios? n NET: ML leads/full reliance Singapore n 6% 6% Full production mode dealing with live data 3% Advanced testing with ‘go-live’ within 3-6 months SINGAPORE 5: Please tell us about the main barriers to using ML in the organisation more generally n Singapore n Global 70% 40% 60% 36% 33% 35% 20% 50% 30% 29% 27% 17% 16% 10% 16% 10% Se a lec ou ppli tion nti ca ng tio and po n o lici f es Bu sin eg es fut s ju d u for re p gem the ros en bu pec t sin ts ess eg Ethic tec scen al jud hn ari ge no ically os th men tc lea lega at ar t r if l, b e eth ut ica l acc Re c ass dere ogni ets co tion an gnit an dl d iab ion o ilit f ies P an rese d d nt isc atio los n ure acc M ou eas nti ure ng m est ent im of ate s 40% 24% 57% 52% 48% 49% 30% 20% 10% 27% 24% 19% 21% 19% 13% 16% 17% 6% 14% 5% 11% vo Ins lum uffi e o cie f d nt ata Re gu lat req ory uir /leg em en al ts Eth ica ld ile mm as 30% 6% Early stage preparation with ‘go-live’ within 12 months Global 50% 40% 8% L to ack lea of d t skil he led ad op staff t io n Co st im pli cat ion s Po or qu alit yo fd ata No c fro lear b m usi ene ng fit ML Do n’t kn ow /no ba rrie rs 13% 43 Machine Learning: More science than fiction | Appendix – Country snapshots United Arab Emirates (UAE) UAE 1: AI as hype or reality n 70% Now n UAE 2: Who in the organisation should own the process and data gathering associated with the use of ML? XX% Global years’ time 60% 58% 50% 55% 34% 40% 60% Global 63% 40% 26% 13% 20% 40% 30% 21% 18% 10% 20% 12% 14% 10% n UAE 50% 40% 30% n 70% All / Mostly hype Mostly / Entirely reality Note: remaining respondents said ‘Equal hype and reality’ Finance/ accountants 10% IT 15% 7% 13% Business unit Strategy team 7% 12% 0% Don’t know 5% Another team UAE 3: Machine learning (ML) adoption in organisation n 50% 27% 20% 24% 21% 10% 5% 12% No plans for adoption Don’t know Initial discussions and exploring concepts/ideas UAE 4: Role of ML in applying judgement appropriately in complex scenarios? NET: ML leads/full reliance n UAE n 8% 6% 5% Early stage preparation with ‘go-live’ within 12 months 5% Full production mode dealing with live data 3% Advanced testing with ‘go-live’ within 3-6 months UAE 5: Please tell us about the main barriers to using ML in the organisation more generally n Global 50% UAE n Global 70% 46% 36% 35% 20% 41% 29% 24% 50% 29% 40% 16% 10% 15% 10% Se a lec ou ppli tion nti ca ng tio and po n o lici f es Bu sin eg es fut s ju d u for re p gem the ros en bu pec t sin ts ess eg Ethic tec scen al jud hn ari ge no ically os th men tc lea lega at ar t r if l, b e eth ut ica l acc Re c ass dere ogni ets co tion an gnit an dl d iab ion o ilit f ies P an rese d d nt isc atio los n ure acc M ou eas nti ure ng m est ent im of ate s 60% 39% 49% 52% 56% 49% 30% 20% 10% 24% 24% 29% 21% 20% 19% 12% 17% 15% 14% 17% 11% vo Ins lum uffi e o cie f d nt ata Re gu lat req ory uir /leg em en al ts Eth ica ld ile mm as 46% L to ack lea of d t skil he led ad op staff t io n Co st im pli cat ion s Po or qu alit yo fd ata No c fro lear b m usi ene ng fit ML Do n’t kn ow /no ba rrie rs 30% Global 38% 30% 40% n 46% 40% UAE 44 Machine Learning: More science than fiction | Appendix – Country snapshots Ireland IRELAND 1: AI as hype or reality n 70% Now n IRELAND 2: Who in the organisation should own the process and data gathering associated with the use of ML? XX% Global years’ time 60% 58% 70% 50% 56% 60% 34% 40% 30% 20% 40% 32% 13% 30% 20% 10% Global 39% 40% 20% 10% n Ireland 50% 26% 38% n All / Mostly hype Mostly / Entirely reality Note: remaining respondents said ‘Equal hype and reality’ Finance/ accountants 14% 14% 14% 15% 15% 13% IT Strategy team Business unit 5% 12% 12% Don’t know 5% Another team IRELAND 3: Machine learning (ML) adoption in organisation n 50% Ireland n Global 40% 38% 30% 38% 30% 20% 24% 17% 10% No plans for adoption Don’t know Initial discussions and exploring concepts/ideas IRELAND 4: Role of ML in applying judgement appropriately in complex scenarios? n NET: ML leads/full reliance Ireland n 7% Early stage preparation with ‘go-live’ within 12 months 6% 1% Full production mode dealing with live data 3% Advanced testing with ‘go-live’ within 3-6 months IRELAND 5: Please tell us about the main barriers to using ML in the organisation more generally n n Ireland Global 70% 60% 30% 36% 38% 35% 26% 50% 29% 15% 10% 12% 16% 2% 10% Se a lec ou ppli tion nti ca ng tio and po n o lici f es Bu sin eg es fut s ju d u for re p gem the ros en bu pec t sin ts ess eg Ethic tec scen al jud hn ari ge no ically os th men tc lea lega at ar t r if l, b e eth ut ica l acc Re c ass dere ogni ets co tion an gnit an dl d iab ion o ilit f ies P an rese d d nt isc atio los n ure acc M ou eas nti ure ng m est ent im of ate s 40% 24% 52% 43% 49% 42% 30% 20% 10% 28% 24% 18% 21% 21% 19% 12% 17% 21% 14% 11% 11% vo Ins lum uffi e o cie f d nt ata Re gu lat req ory uir /leg em en al ts Eth ica ld ile mm as 40% 20% 8% Global 50% 30% 7% L to ack lea of d t skil he led ad op staff t io n Co st im pli cat ion s Po or qu alit yo fd ata No c fro lear b m usi ene ng fit ML Do n’t kn ow /no ba rrie rs 21% 45 Machine Learning: More science than fiction | Appendix – Country snapshots Pakistan PAKISTAN 1: AI as hype or reality n 70% Now n PAKISTAN 2: Who in the organisation should own the process and data gathering associated with the use of ML? XX% Global years’ time 58% n 70% 65% 60% n Pakistan Global 60% 50% 50% 40% 34% 30% 26% 31% 20% 40% 30% 30% 13% 10% 20% 11% 41% 40% 18% 10% All / Mostly hype Mostly / Entirely reality Note: remaining respondents said ‘Equal hype and reality’ Finance/ accountants 14% IT 15% 15% 8% Strategy team Business unit 9% 13% 9% 12% Don’t know 5% Another team PAKISTAN 3: Machine learning (ML) adoption in organisation n 50% Pakistan n Global 40% 33% 38% 28% 20% 24% 17% 10% No plans for adoption Don’t know Initial discussions and exploring concepts/ideas PAKISTAN 4: Role of ML in applying judgement appropriately in complex scenarios? n NET: ML leads/full reliance Pakistan n 9% 8% Early stage preparation with ‘go-live’ within 12 months 6% 5% Full production mode dealing with live data 3% Advanced testing with ‘go-live’ within 3-6 months PAKISTAN 5: Please tell us about the main barriers to using ML in the organisation more generally n Global 50% Pakistan n Global 70% 40% 30% 8% 60% 37% 36% 35% 35% 20% 36% 29% 29% 40% 24% 17% 16% 10% 15% 10% Se a lec ou ppli tion nti ca ng tio and po n o lici f es Bu sin eg es fut s ju d u for re p gem the ros en bu pec t sin ts ess eg Ethic tec scen al jud hn ari ge no ically os th men tc lea lega at ar t r if l, b e eth ut ica l acc Re c ass dere ogni ets co tion an gnit an dl d iab ion o ilit f ies P an rese d d nt isc atio los n ure acc M ou eas nti ure ng m est ent im of ate s 50% 51% 52% 53% 49% 30% 20% 10% 23% 24% 17% 21% 21% 19% 21% 17% 14% 14% 12% 11% vo Ins lum uffi e o cie f d nt ata Re gu lat req ory uir /leg em en al ts Eth ica ld ile mm as 21% L to ack lea of d t skil he led ad op staff t io n Co st im pli cat ion s Po or qu alit yo fd ata No c fro lear b m usi ene ng fit ML Do n’t kn ow /no ba rrie rs 30% 46 Appendix A starting point for keeping abreast of basic developments is to look at how consumer applications and capabilities incorporate AI techniques and functions This can range from smartphone ‘apps’, software, Web applications, consumer devices, IoT devices, to autonomous cars and drones etc Technology publications are a good source of practical details of these new capabilities TED talks can be a useful way of gaining insight into a range of topics, including technologies such as AI A vast range of topics are covered and available online at the TED website, all no longer than 18 minutes MeetUp groups are an easy way of gaining insight into technology (including AI) and of expanding one’s understanding and connecting with people involved directly in AI development and use Many organisations hold ‘hackathons’ for speedy creation and testing of ideas for using new technologies in an innovative way These work best where technology and business professionals collaborate, and are an ideal opportunity for finance professionals to get involved, find out more, and show where they can add business value AI is an emotive topic that raises concerns over data access and sharing, as well as for jobs, employment and training Accountants should stay aware of media coverage of AI and its impact on jobs; government policy on employment and training; and research and surveys of consumer opinion Accountants need to be aware of the perceptions of social impact from AI Forums such as AI4People provide valuable insight to the direction of AI and how to address the social and ethical challenges Case studies from software vendors and consultancies are a (admittedly biased) source of information for how these developments are being deployed but they give examples of the benefits being achieved Many software vendors will also provide training on AI capabilities Technology analysts (such as Gartner, Forrester, IDC) publish reports and survey results, and run events on topics such as AI and ML with a bias towards identifying and explaining the practical capabilities available from vendors Organisations such as Digital Catapult, created to encourage collaboration between business and technologists, are another source of information about applications, including AI and ML, especially those from early-stage companies that may not yet have broader visibility There are independent course providers that provide specific AI and ML education: eg EdX 17 [www.ted.com/talks] 18 [www.meetup.com] 19 http://www.eismd.eu/ai4people/ 20 https://www.digicatapult.org.uk/ 21 https://www.edx.org/course/machine-learning-columbiax-csmm-102x-4 https://www.edx.org/course/artificial-intelligence-ai-columbiax-csmm-101x-4 47 References ACCA (2017), Professional accountants – the future: Ethics and trust in a digital age , accessed 20 March 2019 ACCA (2018), Professional accountants: the future: Emotional quotient in a digital age , accessed 18 March 2019 Datamaran (2018), Global Insights Report 2018: The Rise of ESG Regulations , accessed 20 March 2019 Vaidyanathan, N (2017), Divided we fall distributed we stand The professional accountant’s guide to distributed ledgers and blockchain , accessed 20 March 2019 WEF (World Economic Forum), Marsh and McLenan Companies and Zurich Insurance Group (2019), The Global Risks Report 2019, 14th edition , accessed 20 March 2019 Yao, M., Zhou, A and Jia, M (2018) Applied Artificial Intelligence: A Handbook for Business Leaders (USA: TopBots) 48 PI-MACHINE-LEARNING ACCA The Adelphi 1/11 John Adam Street London WC2N 6AU United Kingdom / +44 (0)20 7059 5000 / www.accaglobal.com ... involved ML: Machine learning AI DL: Deep learning ML NLP: Natural language processing AI: Artificial intelligence DA: Data analytics RPA: Robotic process automation DL NLP DA RPA 12 Machine Learning: ... summary Introduction 8 Machine learning and accountancy 10 Navigating the terminology 12 Applications of machine learning 18 Ethical considerations 25 Skills in a machine learning environment 35... indicating greater comfort; NET Comfortable is sum of 4, 5; NET Not Comfortable is sum of 1,2 18 Machine Learning: More science than fiction | Applications of machine learning When considering the