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Analytics in Banking

Webinar August 10, 2019 1200 Hrs. Analytics in Banking. Vineet Khanna Executive Director – SAS Institute India (Pvt) Ltd. Email: Vineet.Khanna@sas.com. Analytics in Banking. Situation & Trends in Banking industry. Analytics / AI & ML A few examples of adoption of Analytics by banks:

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Analytics in Banking

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  1. Webinar August 10, 2019 1200 Hrs. Analytics in Banking Vineet KhannaExecutive Director – SAS Institute India (Pvt) Ltd. Email: Vineet.Khanna@sas.com

  2. Analytics in Banking • Situation & Trends in Banking industry. • Analytics / AI & ML • A few examples of adoption of Analytics by banks: • Risk Management • Controlling Financial Crimes • Contextual Marketing • Evolving Areas www.actuariesindia.org

  3. Analytics in Banking • Situation & Trends in Banking industry. • Analytics / AI & ML • A few examples of adoption of Analytics by banks: • Risk Management • Controlling Financial Crimes • Contextual Marketing • Evolving Areas www.actuariesindia.org

  4. Asia Pacific Banking Source: McKinsey, Asia Pacific Banking Review 2019 www.actuariesindia.org

  5. Strategic Priorities www.actuariesindia.org

  6. Analytics in Banking • Situation & Trends in Banking industry. • Analytics / AI & ML • A few examples of adoption of Analytics by banks: • Risk Management • Controlling Financial Crimes • Contextual Marketing • Evolving Areas www.actuariesindia.org

  7. Analytics Trends In-database, cloud, Internet-of-Things, APIs Client-server, Internet-based systems Computing Paradigms Open / networked systems In-memory, multi-core, virtualization Monoliths Technology Platforms Minicomputers, internal networks, UNIX, PCs Cloud-based infrastructure, platforms-as-a-service Mainframe Networks, Linux Mobile, grids Big Data (Hadoop),data governance, cloud, streams 2010 1970 1980 1990 2000 Data virtualization, MDM, unstructured data Data warehouses Relational databases (Oracle, DB2), data integration Flat Files Data Interactive interfaces, code generation, SQL pushdown, remote submit, business rules In-database processing, in-stream processing, machine learning, deep learning Classic Statistical Methods Grid-based analytics, distributed processing, in-memory processing, text analysis, model management Analysis Advanced analytics methods, multi-step process flows www.actuariesindia.org

  8. Analyst Forecast "The AI market continued to grow at a steady rate in 2018, and we expect this momentum to carry forward over the forecast period. IDC forecasts the overall market to maintain a steady growth rate annually through 2023, approaching $98.4 billion in revenue at a CAGR of 28.5%," "Investments in analytics and artificial intelligence are driven by the promise, opportunity, and excitement of a new wave of automation that not only drives inefficiency out of processes but also changes how people interact with the digitized world around them and how processes and whole ecosystems change because of automation," "However, investment in analytics and AI will be moderated by shortage of algorithm training data, outdated legal frameworks, shortage of analytics staff, behavioral biases, and insufficient attention to analytic orientation and data literacy." Source : IDC Website www.actuariesindia.org

  9. Analytics in Banking • Situation & Trends in Banking industry. • Analytics / AI & ML • A few examples of adoption of Analytics by banks: • Risk Management • Controlling Financial Crimes • Contextual Marketing • Evolving Areas www.actuariesindia.org

  10. ENTERPRISE RISK MANAGEMENT DASHBOARDS AND REPORTS Risk Analytics Capital Planning, IFRS 9 EST RBS & ICAAP MARKET RISK CREDIT RISK ALM Operational Risk Instrument Valuation Credit Scoring Cash Flow generation EGRC Monitor Models – CASA, Options Risk Measures Validations, Calibration OpRisk Global Data Counterparty Credit Risk Economic Capital Funds Transfer Pricing OpRiskVaR Liquidity Risk STD & IMM Compliance, STD & IRB Compliance, Compliance Management FRTB Portfolio Optimization Cash Flow Optimization Audit Management Risk Based Limits Stress Testing & Scenario Analysis RISK INFRASTRUCTURE Comparatives Model Governance Re Runs Recalibration Auditability Data Governance Model Risk Integrations Treasury Core Banking Loan Origination System MARKET DATA www.actuariesindia.org

  11. Market Risk www.actuariesindia.org

  12. Market Risk www.actuariesindia.org

  13. Market Risk www.actuariesindia.org

  14. Market Risk www.actuariesindia.org

  15. Market Risk www.actuariesindia.org

  16. IFRS 9 Data Collection New Information Individual Account Level Forecasts / Historical Segmentation Individual Asset Level = Massive Amount Data = More Granular Data Governance Documentation Governance Change Control Regulatory Capital forecast Model Management = New Control Framework Forward Looking Calculations Financial Impact Increased Measurement complexity Additional Data Collection More Risk Models = New Analytical Models Audit Preparation One of the most challenging areas of an ECL implementation will be aligning the banks interpretation with what is deemed acceptable by the auditor and regulator. = Risk and Finance Integration www.actuariesindia.org

  17. IFRS 9 www.actuariesindia.org

  18. Financial Crime Business Modules Compliance Early Warning Signals AML Customer Risk Rating Threshold Optimization Internet Banking Mobile Banking ATM Application Frauds Trade Finance Fraud Office Accounts Internal Fraud Branch Banking Cards UPI Fraud and Financial Crimes Management Platform Ops and Management Reporting Case Management & Workflows Enterprise Data Orchestration Advanced Analytics & Machine Learning Alert Triage www.actuariesindia.org

  19. Anti Money Laundering www.actuariesindia.org

  20. Fraud Business Rules Anomaly Detection Predictive Models / Advanced Analytics Social Network Analysis Text Mining www.actuariesindia.org

  21. AML & Fraud www.actuariesindia.org

  22. AML & Fraud Application Area Business Value AI / ML Technique Bayesian Networks Boolean Rules Decision Trees Factorization Machines Frequent Item Set Mining Gradient Boosting K Nearest Neighbor Image Processing Market Basket Analysis Moving Windows PCA Network Analytics/Community Detection Neural Networks / Deep Learning Random Forest Robust PCA Support Vector Data Description (SVDD) Support Vector Machines Text Mining Variable Clustering Detecting the topics and subject of interest from unstructured text (example : SWIFT) Anomaly Detection in transaction behavior as an individual or peer group (ex: Quick Service Restaurants) Detect new modus operandi or change in existing modus operandi of financial crime Change existing thresholds settings to reduce False Positives Identify look alike profiles for undetected ones Rescore the alerts to prioritize investigation based on revised score www.actuariesindia.org

  23. Contextual Marketing Hey Eve! Your current balance is now €67, but we have good news. Text “Y” to activate a €200 credit extension on your account 3:29 pm Dress 3:46 pm Jeans 3:57 pm Scarf 3:57 pm Offer <200Ms Track customer behaviour and look for specific patterns (i.e. 3 transactions within 30 minutes resulting in low balance) Eve is shopping, her balance is close to zero and she is eligible for contact Apply business logic to determine best action Calculate personalized offer parameters Deliver assistance, guidance or offer Response capture & fulfillment Shopping = • 3 txns in <30 mins • same debit card www.actuariesindia.org

  24. Behind the Scenes Check for Card w/Headroom Encourage Card Use Check for Deposit Account Recommend Transfer Pre-Approved Overdraft? Advise Mobile App or Branch Offer Overdraft Run Credit Risk Check Warning! No Offer Evaluate Potential Offers Offer New Overdraft Offer Personal Loan Offer Credit Card www.actuariesindia.org

  25. Cyber Analytics Information Retrieval Meets Intrusion Detection System bases identification of security incidents and system failures on event log messages SpatialTemporalAnalysis Text analytics based Parsing, topic generation and term frequency and inverse frequency calculated TF-IDF weighting calculation to determine whether a new log message deviates from the norm. Less frequently occurring terms are given a higher weight commonly occurring log messages have a lower weight than do those with infrequently occurring elements A list of terms and corresponding IDF weights can be stored as a hash, which generally yields fast results for searching Rarity Detection www.actuariesindia.org

  26. Key Takeaways Analytics is a rapidly changing & fast growing area Banks are adopting Analytics to solve business problems Demand for data scientists is quite high An Actuary brings a lot more to the table It’s a win-win situation for Banks and Actuaries www.actuariesindia.org

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