HOW TO USE ARTIFICIAL INTELLIGENCE & ANALYTICS IN AUDIT. Babu Jayendran

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1 HOW TO USE ARTIFICIAL INTELLIGENCE & ANALYTICS IN AUDIT Babu Jayendran

2 SPEAKER PROFILE Babu Jayendran has over 35 years experience in Information Systems Auditing and Controls. Consults on SAP s Governance, Risk & Compliance (GRC) suite, and covers the Security, Audit and Control aspects for SAP customers, from a business and technical perspective. An Expert Reviewer for ISACA s 2 nd, 3 rd & 4 th edition publication of SAP R/3 Security, Audit and Control Features A Technical and Risk Management Reference Guide Babu is also consulting in the area of pattern recognition software for forensic auditing in banks, using neural networks and artificial intelligence Qualifications B.Sc (Hons) in Mathematics - IIT, Kharagpur India FCA Institute of Chartered Accountants of India CISA - Certified Information Systems Auditor, USA.

3 OVERVIEW AND BASIC CONCEPTS OF ARTIFICIAL INTELLIGENCE (AI) AND BIG DATA

4 HISTORY 51 YEARS BACK!!

5 WHAT IS ARTIFICIAL INTELLIGENCE? THOUGHT Systems that think like humans Systems that think rationally BEHAVIOR Systems that act like humans Systems that act rationally HUMAN RATIONAL

6 NEURAL NETWORKS A neural network is a series of algorithms that attempts to identify underlying relationships in a set of data by using a process that mimics the way the human brain operates. Neural networks have the ability to adapt to changing input so the network produces the best possible result without the need to redesign the output criteria.

7 THE BIOLOGICAL NEURON The brain is a collection of about 100 billion interconnected neurons. Each neuron is a cell that uses biochemical reactions to receive, process and transmit information. Each terminal button is connected to other neurons across a small gap called a synapse. A neuron's dendritic tree is connected to a thousand neighbouring neurons. When one of those neurons fire, a positive or negative charge is received by one of the dendrites. The strengths of all the received charges are added together.

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9 NEURAL NETWORKS

10 OVERVIEW OF VARIOUS TYPES OF DATA ANALYTICS

11 EVOLUTION OF ANALYTICS Descriptive Prescriptive Predictive Cognitive What happened? Why did it happen? How to make it happen? What could happen? What to do, why & how? Historical data helps understand past performance & for root cause analysis Analysis that suggests a prescribed action Forecast future performance, events and results Proactive action and recognising patterns using big data Tools Used Tools Used Tools Used Tools Used Standard Reports Adhoc Queries Statistical Analysis Graphics etc. Business Intelligence Heuristic Methods Optimization etc. Forecasting Predictive Modeling Simulation etc. AI Machine Learning Neural Networks Deep Learning Pattern Recognition

12 RELEVANCE OF AI AND ANALYTICS FOR CHARTERED ACCOUNTANTS

13 LEVERAGING CAs SKILLS & AI Expert domain knowledge in Finance, Audit, Taxation etc. Excellent understanding of business processes In depth knowledge of Risks & Control Forensic experience in identifying suspicious transactions through audit Availability of large volumes of historical data (both structured and unstructured) Availability of powerful computing resources on the cloud Expert functional knowledge of CAs coupled with AI technology can help in discovering patterns for cognitive analysis

14 CAs Expert Knowledge Required AI STREAMS

15 NEED FOR USING PATTERN RECOGNITION IN AUDIT

16 NEED OF THE HOUR

17 PATTERN RECOGNITION A number of variables have to be considered in order to establish whether a transaction or set of transactions is suspicious Eg. customer s salaryaccount in a bank: Multiple credits in account other than salary credit Sizeable increase in Cash to Non-Cash Transaction Ratio - large cash deposits and cash withdrawals Many transactions witha fewrelated accounts Burst in Deposits - Number of Transactions Burst in Withdrawals - Number of Transactions Burst in Deposits - Amount Burst in Withdrawals - Amount Unusual applications for Demand Drafts against cash. Transactions that are too high or low in value in relation to customer s profile Computers will learn the past behavioral pattern of the customer based on historicaltransactions and identifying unusualactivities

18 PRACTICAL EXAMPLES OF AI IN BANKING

19 THE FUTURE OF DIGITAL FORENSICS A PRACTICAL BANKING EXAMPLE

20 PROFILING

21 CROSS CHANNEL CONVERGENCE ACROSS CHANNELS, PRODUCTS ETC

22 FEATURES FOR FRAUD DETECTION AND PREVENTION

23 Salary Account Decay Cash Deposits closer to the daily threshold limit (50,000/-) Cash Deposits take place almost daily Some times the amount are transferred in less than 2 hours after the cash deposit Some times the amount is accumulated over a period and a DD has been taken The balance is kept almost zero and no big transactions after that Not a single non-cash Deposit transaction

24 New Account High Activity High Activity in a new account opened in March 2008 Huge Cash Deposits accumulating to millions Burst in ATM Withdrawals accumulating to same amount Account balance becomes to zero No transactions after funds are washed out

25 Static Change Event Static Change event occurs in an account through internet banking channel Followed by adding a new beneficiary Burst in activity in terms of volume or value of transactions

26 Stolen/Duplicate Plastic Multiple Transactions using the same card at different locations It is impossible to travel to these locations in the short span of time

27 Employee Small Value Transactions Multiple Transactions with small amount (<50) with same descriptions The amounts deposited has been withdrawn as cash Some transactions referred to as GL There are very small value transactions like Rs 1/-, Rs 2/- etc

28 Anytime, Anywhere Banking KYC in one geography All ATM cash transactions in another geography A set of people following the same pattern All deposits are by Cash at the Home branch All withdrawals are by ATM with in 8 hours of deposit in an unusual geography High frequency of ATM withdrawals

29 ROAD AHEAD FOR AUDIT USING AI & TIPS ON HOW CAS CAN LEVERAGE AI

30 AI PLATFORMS IBM Watson Analytics Google Deep Mind Tensor Flow Microsoft Cognitive Services Voyager Labs Amazon AWS AI Services Facebook FB Learner Flow.

31 LEVERAGING AI BY CHARTERED ACCOUNTANTS CAs possess the domain knowledge and experience to create the relevant learning algorithms for identifying patterns in Finance & Audit CAs should work closely with AI programmers to convert their functional ideas into reality. The concepts and thought process can be extended to Retail, Telecom, Insurance etc. and any area with large volumes of data. Frauds do not happen overnight and is executed over a period of time. There is always a pattern and a modus operandi. The future will see all business transactions flowing through neural networks which will learn patterns of behavior and send out real time alerts of any suspicious transactions, for investigation. This will help CAs to use their intellect optimally, for carrying out more effective and efficient audits.

32 THANK YOU Babu Jayendran