Machine Learning in Banking

1. MACHINE LEARNING IN BANKING FINANCE & RISK

2. WHAT IS MACHINE LEARNING? Copyright © 2017 Accenture. All rights reserved. 2 What Is It? Why now?Why is it useful? Machine Learning Artificial Intelligence Data Mining Statistics • Machine Learning is an application of Artificial Intelligence (AI) that allows computers to learn without being explicitly programmed to do so. • It’s the product of established statistical theory and more recent developments in computing power. • The volume, variety, velocity and veracity of data is increasing at an exponential rate. Banks need to make use of the wealth of data they own. • Machine Learning allows banks to very quickly draw valuable insight from their data, reducing risks, automating processes and improving customer engagement. • With falling profit margins, increasing customer expectations and increasing competition from Fintechs (financial technology firms), banks need to cut costs and improve their offering. • The ability to extract value from such vast amounts of data has never been cheaper or more effective.

3. HOW DOES IT LEARN? MACHINE LEARNING ALGORITHMS ARE CATEGORISED AS BEING SUPERVISED OR UNSUPERVISED. THE FORMER CAN APPLY WHAT HAS BEEN LEARNED IN THE PAST TO NEW DATA. THE LATTER CAN DRAW INFERENCES FROM DATASETS. Copyright © 2017 Accenture. All rights reserved. 3 Feedback Training Data Collect and prepare relevant data to support analysis. If the learning objective includes “expert” judgment, also collect the historical “right answers.” Algorithms Algorithms learn to recognise patterns in training data. Teach the programme how to know when it is doing well or poorly, and how to self-correct in the future. Trained Machine Machine is now trained and ready to spot patterns in real world examples in order to drive business value Supervised Learning What? Output variable specified. Algorithm learns mapping function from input to output Why? To make predictions Example: Predicting credit default risk Unsupervised Learning What? Output variable unspecified so algorithm looks for structure in data Why? To describe hidden distribution or structure of data Example: Customer segmentation and product targeting Determine Objective Decide what you would like the machine to handle that has previously been done based on expert knowledge or intuition. OR

4. MACHINE LEARNING AS A SOLUTION MACHINE LEARNING OFFERS SOLUTIONS TO SOME OF THE MOST IMPORTANT CHALLENGES FACED BY THE BANKING SECTOR TODAY. Copyright © 2017 Accenture. All rights reserved. 4 2017 Financial Services Challenges Cost Reduction Recruit/ Retrain Talent Regulatory Compliance Customer Engagement Security CompetitionThrough unsupervised learning techniques, banks can segment their customers and offer a personalised, targeted product offering. Customer Segmentation Machine Learning offers significantly improved fraud, AML (Anti-Money Laundering) and credit risk detection possibilities. Fraud & AML Detection Compliance through automated reports, stress testing solutions, and behavioral analysis of emails and phone recordings to determine suspicious employee behavior. Compliance Investment in Machine Learning offers banks the speed and agility they need to compete with tech-savvy Fintech firms and to make use of Big Data. Big Data & Agility Combined with Robotics, Machine Learning offers the ultimate automation potential with many back office risk, finance and regulatory reporting processes contenders for automation. Cognitive Automation Digital skills are in short supply in FS. Algorithms can evaluate CVs of successful employees and search for and identify online candidates with similar traits and experience. Natural Language Processing

5. Judgement Based COGNITIVE AUTOMATION (1/2) MOST BANKS HAVE GROWN ORGANICALLY, MEANING THEY HAVE A WEB OF OVERLY COMPLEX PROCEDURES BUILT ON MULTIPLE LEGACY PLATFORMS. DEVELOPMENTS IN ROBOTICS AND MACHINE LEARNING MEAN AUTOMATION OF THESE PROCESSES IS NOW MORE FEASIBLE AND POWERFUL THAN EVER. Copyright © 2017 Accenture. All rights reserved. BusinessImpact Nature Of Work Rules Based TransformationalTactical Foundation Simple, ad-hoc, project level automation that can undertake simple rule-based actions of a single task within an application when prompted (e.g. macros). Robotic Process Automation Also rule-based, but robots can respond to external stimuli and have their functions reprogrammed. They can open and move structured data between multiple applications, from legacy systems to third party APIs (application program interfaces). Cognitive Automation Self-learning, autonomous systems driven by Machine Learning and Natural Language Processing (NLP) that can read and understand unstructured information and instruct a computer to act. Understanding the Automation Landscape

6. COGNITIVE AUTOMATION (2/2) ACCORDING TO A 2013 STUDY BY OXFORD ACADEMICS, ABOUT 54% OF FINANCIAL INDUSTRY JOBS ARE AT HIGH RISK OF BEING AUTOMATED.1 COGNITIVE AUTOMATION HAS THE POWER TO AUTOMATE MANY F&R PROCESSES, IN PARTICULAR RISK AND REGULATORY REPORTING. Copyright © 2017 Accenture. All rights reserved. Cognitive Automation In Action – Document Processing Example 1 42 3 5 Open Email Classify according to type Comprehend & extract relevant information Validate information against rules Populate data into Enterprise Resource Planning system Machine Learning & NLP Machine Learning & NLP Robotics Machine Learning & NLP Robotics Process&Technology • Robotics can be thought of as the ‘hand’ work and cognitive the ‘head’ work – together they form a powerful alliance and can automate even those processes that involve comprehending unstructured text or recognising voices, and making subjective decisions • Benefits of cognitive automation include:  Reduce headcount and associated operational costs  Decreased cycle times for processes that can operate 24 hours per day (e.g. risk/regulatory reporting)  Improved accuracy – reduction of human error 1. “Which finance jobs are safe from robots and automation?”. Silicon Angle, May 31,, 2016. Access at: http://siliconangle.com/blog/2016/05/31/which-finance- jobs-are-safe-from-robots-and-automation/

7. DEEP DIVE 1: FRAUD DETECTION FRAUD COSTS THE FINANCIAL INDUSTRY $80BN PER YEAR.1 WITH REGULATIONS EVOLVING IN RESPONSE TO THE FINANCIAL CRISIS, AND TECHNOLOGY DEVELOPING AT AN EXPONENTIAL RATE, BANKS SHOULD INVEST IN THE LATEST SOFTWARE TO REDUCE THEIR EXPOSURE TO RISK. Copyright © 2017 Accenture. All rights reserved. 7 Method Human Involvement AccuracySpeed Machine Learning Traditional Detection Machine Learning Summary  Lower fraud losses  Lower operational costs  Improved customer service  Reduced reputational risk  Reduced regulatory risk • Algorithms analyse historical transaction data for each customer to understand their individual spending patterns. They can therefore spot subtle anomalies that indicate fraud. • Algorithms self-learn, meaning they quickly adapt to new means of fraud, and can stay ahead of fraudsters. • Rely on pattern matching against recognised past fraud types. Transactions then assessed based on general rules, such as whether the customer is buying abroad. • Humans to identify trends and manually update their models to account for changes in fraudulent activity. • Low • Automatic -humans to maintain the algorithmic models. • High • Preventive over corrective, meaning higher rates of fraud detection and fewer false alarms. • High • Real-time, automatic reviews of transactions using vast amounts of data from multiple sources. • High • Requires significant manual analysis and review, with regular updates to fraud systems. • Medium • Often corrective over preventive with limited use of data, meaning lower detection success rates. • Medium • More human involvement, often using audit trails to identify fraud. • Less computing power. Credit Card Fraud Detection Scenario 1. “Using machine learning and stream computing to detect financial fraud,” IBM Research. Access at: https://www.research.ibm.com/foiling-financial-fraud.shtml

8. DEEP DIVE 2: CREDIT RISK MACHINE LEARNING ALLOWS FOR PROACTIVE RISK MANAGEMENT, REDUCING EXPOSURE TO CREDIT RISK WHILST ALSO OFFERING A FASTER, MORE EFFICIENT PROCESS TO CUSTOMERS. Copyright © 2017 Accenture. All rights reserved. Credit Risk Workflow Origination Appetite & Limit Setting Virtual advisors can understand customer questions and instantly provide well-informed responses, improving customer service levels. Automation of labour intensive processes, e.g. risk, finance and regulatory reports, to cut costs and improve speed of output. A machine learning–enhanced EWS allows automated reporting, portfolio monitoring, and recommendations for potential actions, including an improved approach for each case in workout and recovery. Supervised learning algorithms learn from past events in a data-driven manner. They can incorporate vast amounts of internal and external information to more accurately predict potential scenarios, allowing for better risk planning. • More accurate, instant credit default likelihood prediction based on both quantitative and qualitative data. • Removes requirement for manual fact checking, approvals and complex workflows. • Real time credit decisions could allow for instant, self-service credit applications. Virtual Advisors Robotics & Cognitive Automation Early Warning System (EWS) Stress Testing Credit Default Prediction Credit Analysis & Decision Loan Admin/ Reporting Monitoring

9. DEEP DIVE 3: TRADING FLOORS THE AUTOMATION OF INVESTMENT ADVICE AND TRADES UTILISING A VAST ARRAY OF INTERNAL AND EXTERNAL DATA HAS ABILITY TO SIGNIFICANTLY IMPROVE PERFORMANCE ON TRADING FLOORS AND CUT OPERATING COSTS Copyright © 2017 Accenture. All rights reserved. Machine Learning on Trading Floors Performance • Algorithms autonomously evolve and search for new patterns in data, making real-time high-frequency trading decisions to exploit volatility in stock. The Opportunities We See: Automated Trades • Deal orders, execution and settlement of trades and the analysis and monitoring of risk automated, significantly reducing costs. Compliance • Undertake behavioral analysis by reviewing trade activity for each employee alongside mining chat-logs and emails to identify suspicious activity. Robo-Advisors • Provide algorithm-based portfolio management advice without the requirement for financial planners. • Anticipate changing investments needs as client circumstances change, improving customer service levels. • Ability to utilise external data such as stock prices, Google™ searches and news articles to strengthen pre-trade predictions. • Continuous and real-time, resulting in the ability to prevent non-compliant activity.  Automated Investments – Cost Saving  Improved Investment Decisions  Enhanced Operational Speed and Accuracy  Navigation of Large Amounts of Data  Reduction in Human Error  Lower Compliance Risk  Enhanced Customer Experience

10. DEEP DIVE 4: FRONT OFFICE UNSUPERVISED AND SUPERVISED LEARNING TECHNIQUES ALLOW BANKS TO TRULY UNDERSTAND THEIR CUSTOMERS AND PROVIDE THEM WITH A PERSONALISED SERVICE WITH TARGETED PRODUCT OFFERINGS. Copyright © 2017 Accenture. All rights reserved. Customer Segmentation BenefitsProduct Targeting • Through cluster analysis, an unsupervised learning technique, banks can discover distinct groups in their customer base and see similarities over several dimensions. • Unlike supervised learning, they do not need to define what characteristics the computer should be looking for. • This way, banks can segment in ways traditional analytics would not allow. • Customer segmentation discoveries can be used to build predictive, supervised models. • Algorithms produce personalised views of the most suitable products for each customer, helpful for cross- selling and up-selling. • Since algorithms learn, they recognise changes in customer preferences in real-time and therefore automatically adjust product recommendations. • Personalised, improved customer offerings. • Speed of service - banks recognise change in behavior and respond in a timely manner. • Revenue can increase from successful identification of cross-sell and up-sell opportunities. • Automated – reduced human involvement.

11. APPLICATION CHECKLIST THE FOLLOWING PURPOSE, PROCESS AND LOCATION CHECKLIST CAN BE USED TO HELP YOU UNDERSTAND WHETHER MACHINE LEARNING CAN BE SUCCESSFULLY APPLIED TO A PROCESS. Copyright © 2017 Accenture. All rights reserved. Location: Front, Middle & Back Office Purpose: Prediction? Purpose: Segmentation? Process: Big Data? Process: Digital? Process: Repetitive & Judgement Based? Checklist Why? Supervised learning: Algorithms spot trends in historical data and use this to make predictions based on new data. Unsupervised learning: Machine Learning can spot differences and similarities not visible to the human eye between each data point and make sensible groupings based on these characteristics. Processes that involve the use of paper and physical contact between people are not applicable to Machine Learning. Algorithms thrive off large datasets, offering better results. They also have the computing power to analyse big data at speed. Algorithms learn and improve from each repetition, and the automation of such processes offers huge cost saving potential. The advent of tools such as Natural Language Processing and Speech Recognition mean that Machine Learning can be applied to processes with and without customer/client interaction.

12. HOW TO GET MACHINE LEARNING RIGHT AS MACHINE LEARNING IS ENJOYING A MOMENT OF RENAISSANCE, THERE ARE IMPLEMENTATION CHALLENGES A BANK SHOULD CONFRONT TO BE SUCCESSFUL. Copyright © 2017 Accenture. All rights reserved. • Older generations and less tech savvy customers prefer human interaction to communication with robots. An education/marketing piece may be required to highlight the benefits to the customer. • Judgement currently often trumps insights in firms – a cultural shift will therefore be required. • Democratisation of use of analytics required – there should be incentives to encourage data sharing between business divisions. • Introducing Machine Learning to a business requires a shift in skillset requirements from operational management to analytics and data science. • Banking data is often poor quality and inaccessible as it is stored in siloes on multiple legacy systems. • Algorithms thrive off easily accessible, large data sets. The integration of data sources, ideally onto to a cloud platform, is therefore key. • Some self-learning models cannot be traditionally validated and therefore may be deemed insufficient by the regulator. Thorough research into regulatory requirements is recommended ahead of implementation. • There is a vast array of new and evolving Machine Learning technologies. A thorough consultation process with digital specialists is recommended ahead of any purchase. Talent Customers Regulatory Data Tools Culture Machine Learning Challenges

13. HOW CAN ACCENTURE HELP? ACCENTURE HAS BOTH THE BUSINESS EXPERIENCE AND THE TECHNOLOGICAL KNOW-HOW REQUIRED TO HELP OUR CLIENTS IDENTIFY PROCESSES THAT CAN BENEFIT FROM MACHINE LEARNING TECHNOLOGIES, AND HELP IMPLEMENT THEM. Copyright © 2017 Accenture. All rights reserved. • We are the world’s largest system integrator of IBM technology and an IBM-Technology Premier Business Partner • We have multi-faceted relationships with specialist AI firms such as Mighty AI, Inc. • We have several Fintech Innovation Labs bringing together disruptive innovators and corporates to help shape the future of industry Innovation Alliances We extend our technology and business capabilities through a powerful alliance ecosystem of market leaders and innovators FS Knowledge We invest heavily in our own innovation centres to create applications tailored to our clients’ needs Our solutions are embraced by 84% of the top 50 banks worldwide* • Accenture has been positioned as one of five leaders in the Gartner Magic Quadrant for Business Analytics Services Worldwide (Feb 2017) • Collette is our Digital Mortgage Advisor providing subjective advice to customer questions** • Accenture Finance & Risk provides clients with integrated offerings to improve management of internal complexity, regulatory requirements and capital decisions, and to enable long-term profitability * Accenture Finance Services Index: https://www.accenture.com/gb-en/careers/financial- services-index ** Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner's research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

14. MACHINE LEARNING IN BANKING Contacts Matt Baker (matt.baker@accenture.com) Darius Ansari (dariush.ansari@accenture.com) Anaita Tejpal (anaita.tejpal@accenture.com) Lilian Okorokwo (lilian.u.okorokwo@accenture.com) Disclaimer This presentation is intended for general informational purposes only and does not take into account the reader’s specific circumstances, and may not reflect the most current developments. Accenture disclaims, to the fullest extent permitted by applicable law, any and all liability for the accuracy and completeness of the information in this presentation and for any acts or omissions made based on such information. Accenture does not provide legal, regulatory, audit, or tax advice. Readers are responsible for obtaining such advice from their own legal counsel or other licensed professionals. About Accenture Accenture is a leading global professional services company, providing a broad range of services and solutions in strategy, consulting, digital, technology and operations. Combining unmatched experience and specialized skills across more than 40 industries and all business functions— underpinned by the world’s largest delivery network—Accenture works at the intersection of business and technology to help clients improve their performance and create sustainable value for their stakeholders. With more than 401,000 people serving clients in more than 120 countries, Accenture drives innovation to improve the way the world works and lives. Visit us at www.accenture.com Accenture, its logo, and High Performance Delivered are trademarks of Accenture. Copyright © 2017 Accenture. All rights reserved.