English in baking and finance

MINISTRY OF EDUCATION AND TRAINING NATIONAL ECONOMICS UNIVERSITY THE GROUP EXERCISE Subject name English in Baking and Finance The members 1 Quách Thị Ngọc Ánh 2 Lê Minh Anh 3 Nguyễn Công Trình 4 Phan[.]

MINISTRY OF EDUCATION AND TRAINING NATIONAL ECONOMICS UNIVERSITY THE GROUP EXERCISE Subject name: English in Baking and Finance The members: Quách Thị Ngọc Ánh Lê Minh Anh Nguyễn Cơng Trình Phan Khánh Huyền Hanoi, September 26th, 2019 INDEX INDEX .2 INTRODUCTION CONTENT I Introduce about Big Data and Big Data Analytics Big Data Big Data Analytics II The role and impact of Big Data on Banking sector .8 II.1 The role of Big Data in Banks .8 II.2 The impacts of Big Data on Banking sector .9 a Opportunities of Big Data for Banks b Challenges of Big Data for the Banking industry ingeneral and for Vietnamese Banks in particular 10 III.Application of Big Data for the Banking sector 11 IV.Practical contacts in Vietnam .16 CONCLUSION 21 REFERENCE 22 INTRODUCTION Nowadays, big data affects almost every field and industry The banking industry, too, suffered much from big data Big Data is playing a big role in the banking sector with specific applications such as: analysis, customer satisfaction and customer classification; analysis of detection and warning, prevent risky and fake acts; optimize data processing activities during the operation of analysis and support decision making Big Data will bring many benefits to banks in business such as: Reduce costs; increase product development and optimization time; at the same time support the management, bank officials to make more appropriate and reasonable decisions; saving customers' information processing time and preventing fraud risks However, when exploiting Big Data, banks also face many financial challenges; policies and regulations of the law on data access and use; data mining and management level; IT infrastructure Although Big Data is having a profound impact on the business and marketing strategy of the Banking and Finance Industry, but without proper technology, knowledge and practical application of effective As a result, it will be difficult for banks to maximize the potential benefits of Big Data CONTENT I Introduce about Big Data and Big Data Analytics Introduce about Big Data a The Concept According to Gartner: Big Data are information resources with properties such as high-volume, high-velocity and high-variety, requiring innovative and cost-effective forms of information processing to enhance understanding and make a decision Big data is a term applied to data sets whose size or type is beyond the ability of traditional relational databases to capture, manage and process the data with low latency Big data has one or more of the following characteristics: high volume, high velocity or high variety Artificial intelligence (AI), mobile, social and the Internet of Things (IoT) are driving data complexity through new forms and sources of data For example: Big Data comes from sensors, devices, video, networks, log files, transactional applications, web, and social media much of it generated in real time and at a very large scale Big Data is a broad term for processing very large and complex data sets that traditional data processing applications cannot handle Includes challenges such as analysis, collection, data monitoring, search, sharing, storage, transmission, visualization, querying and privacy The term Big Data is often simply understood as used for predictive analysis or as some other obvious advanced method for extracting values from data with little reference to the size of the data set Accuracy in Big Data can lead to better right decisions, and better decisions can lead to better performance such as cost and risk reduction b Five Vs of Big Data  Volume Talk about the amount of data created and stored The size of the data will be assessed as valuable and potential, and to consider whether it can be considered as big data For example: Facebook receives nearly 350 million images, more than 4.5 billion likes, and nearly 10 billion messages and comments every day For that reason, traditional types of data storage and analysis are in no way possible But with the technology we are talking about here, it can easily process and store all the information on separate small branch systems  Variety This concept is about the data type and the nature of the data This helps those who analyze it effectively use detailed information about the results They are composed of text, images, audio and video; plus it completes the missing part through data aggregation algorithms  Velocity In this day and age, the speed with which data is created and processed to meet the needs and challenges lies in the path of growth and development Big data is usually available in real time For example: People can chat with each other on Facebook with fast speed in today's network environment Big Data allows us to analyze the parameters of a generated data without saving them to the database  Variability Because of the variety of data types, the inconsistency of a data set can hinder processes to process and manage it Therefore, the accuracy of this technology can guarantee the reduction of unfortunate deviations that may occur  Value The data quality of the data collected can vary greatly, which will greatly affect the exact analysis We can see this is the nature as well as the concept that businesses or researchers who want to use and exploit Big Data must hold and understand it first image c The growth of Big Data: Worldwide Big Data market revenues for software and services are projected to increase from $42B in 2018 to $103B in 2027, attaining a Compound Annual Growth Rate (CAGR) of 10.48%. As part of this forecast, Wikibon estimates the worldwide Big Data market is growing at an 11.4% CAGR between 2017 and 2027, growing from $35B to $103B ( Source:  Wikibon and reported by Statista.) image 2 Big Data analytics a The Concept Big data analytics is the often complex process of examining large and varied data sets, or big data, to uncover information such as hidden patterns, unknown correlations, market trends and customer preferences that can help organizations make informed business decisions b The importance of big data analytics Driven by specialized analytics systems and software, as well as highpowered computing systems, big data analytics offers various business benefits, including: - New revenue opportunities - More effective marketing - Better customer service - Improved operational efficiency - Competitive advantages over rivals Big data analytics applications enable big data analysts, data scientists, predictive modelers, statisticians and other analytics professionals to analyze growing volumes of structured transaction data, plus other forms of data that are often left untapped by conventional BI and analytics programs This encompasses a mix of semi-structured and unstructured data For example: internet clickstream data, web server logs, social media content, text from customer emails and survey responses, mobile phone records, and machine data captured by sensors connected to the internet of things (IoT) image II The role and impact of Big Data on Banking sector The roles of Big Data in the Banking sector: Diversity with data from various sources specific to banking operations creates a large data source from structured data, such as transaction histories, customer records to unstructured data, such as customer activities on the web, mobile applications, etc Using big data to exploit this data will bring competitive advantages and great effects in the field of banking Specifically, Big data is playing a big role in the banking sector with specific applications such as: analysis, satisfaction classification and customer behavior; analysis of detection and warning, prevent risky and fake acts; Optimize data processing operation during analysis operation Big data is very important to the bank’s activities, and analyzing big data also helps the bank to make appropriate business decisions image The impacts of Big Data on the Banking sector: a Opportunities for Banking:  Fraud Detection: It helps Banks to detect, prevent and eliminate internal and external fraud as well as reduce the associated costs  Risk management: Banks analyse transaction data to determine risk and exposures based on simulated market behavior, scoring customer and potential clients  Contacts Center Efficiency Optimization: It helps Banks to resolve problems of customers quickly by allowing Banks to anticipate customers need ahead of time  Customer Segmentation For optimize Offers: It provides a way to understand customers’ needs at a granular level so that Banks can deliver targeted offers more effectively  Customer Churn Analysis: It helps Banks to retain their customers by analyzing their behavior and identifying patterns that lead to a customer abandonment  Customer Experience Analytics: It can provide better insight and understanding, allowing Banks to match offers to customers’s needs b Challenges for Banking:  Difficult to fully harness customer profile data: Banking services data is highly diverse, stored in different departments It is difficult to profile a customer based on customer investment behavior as his accounts, loans, insurances, etc may be spread over various branches and departments of the bank. Big data needs to collate all such data first, in order to provide comprehensive intelligence  Legacy infrastructure needs to be upgraded before integrating big data capabilities: Most banking solutions are not equipped to handle constant influx of data, which is a pre-requisite for big data, even if they have moved to cloud solutions Integrating big data requires a complete revamp of most of the existing bank solutions in partnership with a big data consulting company This is not easy to implement, as the system needs to be constantly up even when the changes are being deployed  Staff has limited access to modern digital technologies: Not only are there limitations in the old machinery system, human resources with modern digital technology in banks are still weak and thin, and there is a shortage of personnel capable of capturing and deploying digital technologies in the world  Customer concerns about privacy and security: Although the data logged by big data systems is anonymous at the high level, if the bank wishes, they can track behavior patterns of each individual customer It is advantageous in detecting illegal activities, but is a serious security threat to the customer if it falls into wrong hands 10 c Challenges of Big Data for Banking in Vietnam: - The complex traditional core banking system is the biggest barrier to the success of digital banking Without depth changes, banks may lag behind in the race to provide digital experiences to customers Outdated information technology (IT) systems with inflexible structure and monolithic operations are also hindering banks from developing to digital banking while system changes are complex and expensive about time and money - Strategic investment budgets for new technologies are limited as Vietnamese banks currently focus on short-term business At the same time, due to the lack of a digital strategy and vision, limited knowledge of digitization and the potential of digitization are also restricting banks from investing properly in modernizing the system - Not only are there limitations in the old machinery system, the human resources with modern digital technology in Vietnamese banks are still weak and thin, there is a shortage of personnel capable of capturing and deploying the technologies modern in the world - With the current pace of digital technology development, security is also a problem for global banks, including Vietnam, to pay great attention to when the qualifications of cyber attackers and crime is also much higher, along with the high level of globalization brought about by the Industrial Revolution 4.0, the attack on Vietnamese banks is not only encapsulated within the country but in any country In any case, criminals can attack Vietnamese banks - The competition comes from technology finance companies, when Apple Pay and Samsung Pay are in turn, and are direct competitors to the payment products of traditional banks III Application of Big Data for the Banking sector Identifying and Eliminating Fraud - Combining and analyzing large volumes of data like transactions, geolocation, merchant information and more helps financial services companies identify anomalies and behavior patterns that signal potential fraud With 11 these insights, you can dramatically reduce the risk of fraud and tighten security For example, Danske Bank is fighting fraud with deep learning and AI techniques The bank struggled with low fraud detection rates (40 percent) and has over 1200 false positives per day After the implantation of a modern enterprise analytics solution, the bank realized a 60 percent reduction in false positives; increasing true positives by 50 percent image Improved cybersecurity and risk management - Operational risk mitigation is near the top of every bank’s operating agenda With your employees and your business operating at a greater pace and level of complexity than ever, it’s critical to monitor and report on employee behaviors, key operational process performance and other KPIs to ensure you mitigate risk Example, UOB bank from Singapore is an example of a brand that uses big data to drive risk management Being a financial institution, there is huge potential for incurring losses if risk management is not well thought of UOB bank recently tested a risk management system that is based on big data The big data risk management system enables the bank to reduce the calculation time of the value at risk Initially, it took about 18 hours, but with the risk management system that uses big data, it only takes a few minutes Through this initiative, the bank will possibly be able to carry out real-time risk analysis in the near future (Andreas, 2014) 12 image Preventing Overdraft Fees and Other - A lot of times, bank customers feel the need to create an automated savings plan, but are afraid to so if an unexpected charge comes up and causes an overdraft With smart AI, banks can mitigate such a circumstance With steps like forward cash flow predictions, aggregated account data and data-driven intelligent awareness, banks can hold transfers to the automated savings account until there’s more money in the account, alert the customer of a possible overdraft and suggest a top up, and take other steps to prevent penalties Example, Metro Bank is already doing that with Insights, an in-app money management tool that gives customers complete control of their finances It alerts customers when there’s not enough money to cover a likely spend, recommends a top up before an automated payment is due, flags if a customer has accidentally been charged twice and alerts the customer when there has been any kind of unusual activity 13 image Improving Customer Experience - With so many financial institutions in the market, it gets tough for the customer to decide which bank to transact with Customer experience, in this case, becomes a deciding factor Big data analysis presents with the customised analysis for each customer, thus improving their services and offerings image Personalised Marketing - Personalized marketing is nothing but the next step of highly successful segment-based marketing where we divide the customers into a different segment based on some parameters and then follow with them accordingly to convert to sales - Personalized Segmentation using Big Data In personalized marketing, we target individual customer based on their buying habits Industries can take help of the data from e-commerce profiles like what they are buying, what they are browsing etc to get the data of individual customers These data will 14 unstructured and so use Big Data technologies; it can be converted into structured and can be analyzed further - Banks can also take data from customers’ social media profile and can sentiment data analysis to know the habit and interest - Further risk assessment can be done to decide whether to go ahead with the transaction or not image Customer Segmentation Segmentation is categorizing the customers based on their behavior This helps in targeting the customer in a better way.Banks are moving now from the label of product centric to customer centric and so targeting individual customer is at most necessary Big data analysis is helping them to know about the details like demographic details, transaction details, personal behavior, etc Based on these data, banks can make a separate list for such customer and can target them based on their interest and behavior image 10 IV Practical contacts in Vietnam: 15  In Vietnam: We cannot deny the strong impact of the 4th industrial revolution on the economic and social development around the world, with one notable technology being Big Data Analyst This technology, in particular, is a core value for the application and development of Internet of Things (IoT) and artificial intelligence (AI) In the Big Data race, FPT IS (FPT Information System -FPT IS) is providing platforms for development solutions to big data analysis problems, as well as building procedure platforms for developing machine learning models, analyzing deep data, and storing prevalent data types of customers The contract with TPBank is the first Big Data project of FPT IS for banks in Vietnam         Big data say about customers - Shopping habits: shopping topics, number of transactions, transaction history, items that customers are interested in, - Habits on the web: topics of interest, advertisements, reading information, reading time, - Mobile habits: SMS, geographic location, usage habits, number of apps used, - Habits on email: the ability to care, open, read, complain - Customer's preferences: personality, interest, language, approach, exploitation, - Social networking habits: likes, friends, information, posts, - Banking information: CRM, finance, ERP, - Customer information: name, age, occupation, address, hours online per day The amount of data can be up to thousands of entries (instead of a few, as many as traditional Data) Comparing digital marketing technology with traditional marketing Traditional marketing: -difficult to identify the right target customers                                    - Basic information, general information 16                                    - Can not track, analyze customer behavior                                    - cost savings Digital marketing: - identify the right target customers                             - give information exactly, at the right time, in the right place                             - track and analyze customer behavior                             - Allows access to multiple channels with custome TPBank digital banking ecosystem - Sales and marketing: LikeBank, eBank, ATM, eCounter and Digital Marketing services - Digitizing processes, paperless: ECM applies new technologies such as OCR, QR code; Research and apply new technologies such as AI and Block Chan - Risk management: AI, AML, irregular risk management and detection system - Customer care: QR code savings book, authentic documents; 24/7 Call Center, automatic switchboard looking up account numbers, automatic cards; AI Chat box via social networking channel - Digital products and services: e-savings, personal ebanking and eBank services, digital signatures, mPOS, QR Code Digital Bank by TPBank LiveBank: TPBank LiveBank is a 24/7 online transaction model, the latest generation of banking transactions in Vietnam, customers can perform almost all banking 17 transactions without being limited by the time thanks to the application of the latest technology on world  Features: Works 24/7 all days (including holidays) Support all basic banking transactions Modern biometric technology and security Deposit savings with higher interest rates than counters and internet banking Online support via video call with TPBank staff Open an account and receive card immediately within minutes  Features: Opening Payment Account: With TPBank LiveBank, you can open a Payment Account as well as an eBank Account immediately after only minutes, using one of the following documents: CCCD / ID / Passport Opening Savings Book: LiveBank will help you open Savings books with attractive interest rates quickly, by one of the following methods: Open passbook with TPBank card Open passbook with CCCD / ID / Passport Complete / Change account information: You can Complete / Change your TPBank account information with one of the following documents: CCCD / ID card / Passport Immediate Card Issuance: LiveBank is the first and only transaction point in Vietnam that allows customers to open an ATM card to receive immediately at any time of the day After only minutes of transaction at LiveBank, you can own a TPBank eCounter card with the most advanced security technologies available today and it's free Cash deposit / Withdrawal: Besides the function of Withdrawal of a traditional ATM, LiveBank also supports Customers to Deposit with the maximum amount of up to VND 100 million each time Even better, through LiveBank, you can deposit cash into accounts of other banks at any time of the day Bank transfer / Bill payment: LiveBank can assist you to perform Bank Transfer, Bill payment (electricity, water ) transactions.  Other features: Query account information; Change PIN; Settlement of savings book; Apply for loans, credit cards & insurance By the beginning of 2019, TPBank had nearly 100 LiveBank machines, present in 19 provinces and cities across the country, has served more than 2,500 successful transactions / month with each machine, bringing convenience, saving time Great for customers at any time of the day   18 Thanks to the influence of Artificial Intelligence and Big Data, according to the newly announced 2018 business results, TPBank's profit before tax reached VND 2,258 billion - nearly doubled compared to 2017 By the end of 2018, TPBank's total assets is more than VND 136,000 billion, equity is over VND 10,500 billion, TPBank QuickPay: Fast payment and money transfer application with QR code TPBank QuickPay application can be installed from App Store and Google Play for free To use this app, users only need to open the application, log in, scan the QR code and scan the fingerprint to confirm payment  Features: - Deposit: Deposit money from TPBank accounts, ATM cards and other bank accounts, VISA & Mastercard - Payment: Pay for goods and services at the store, pay bills for electricity, water, air tickets - Accept payment According to EMVCO standard, accept payments from VISA, Mastercard, Napas, - Transfer money: Transfer money to other QuickPay accounts within 3s - Easy & free: Simple operation, free registration and transaction for users - Safe: Transactions meet PCI-DSS security standards; Fingerprint authentication, FaceID After only months of launching, TPBank QuickPay attracted 10,000 people using the service, 8,000 business partners accepted QR codes In November 2018, The LEADER Vietnam Airlines, Jetstar Pacific and Vietjet Air all in turn allowed QR Code payment on their websites, contributing to significant convenience for customers The representative 19 of the bank said that in the last months of 2018, the amount of money to pay for tickets by QuickPay increased steadily by over 50% V CONCLUSION 20 ... data access and use; data mining and management level; IT infrastructure Although Big Data is having a profound impact on the business and marketing strategy of the Banking and Finance Industry,... Data for the Banking sector Identifying and Eliminating Fraud - Combining and analyzing large volumes of data like transactions, geolocation, merchant information and more helps financial services... days (including holidays) Support all basic banking transactions Modern biometric technology and security Deposit savings with higher interest rates than counters and internet banking Online support