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Webinar August 10, Hrs. Analytics in Banking

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Presentation on theme: "Webinar August 10, Hrs. Analytics in Banking"— Presentation transcript:

1 Webinar August 10, 2019 1200 Hrs. Analytics in Banking
Vineet Khanna Executive Director – SAS Institute India (Pvt) Ltd.

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 1

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 1

4 Asia Pacific Banking www.actuariesindia.org 1
Source: McKinsey, Asia Pacific Banking Review 2019 1

5 Strategic Priorities www.actuariesindia.org Improve Productivity
Digital Risk Management Optimize Capital Fraud Control Grow Business 1

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 1

7 Analytics Trends www.actuariesindia.org 1 Data Analysis Flat Files
Classic Statistical Methods Relational databases (Oracle, DB2), data integration Advanced analytics methods, multi-step process flows Data warehouses Interactive interfaces, code generation, SQL pushdown, remote submit, business rules Data virtualization, MDM, unstructured data Grid-based analytics, distributed processing, in-memory processing, text analysis, model management Big Data (Hadoop),data governance, cloud, streams In-database processing, in-stream processing, machine learning, deep learning 1970 1980 1990 2000 2010 Technology Platforms Mainframe Minicomputers, internal networks, UNIX, PCs Networks, Linux Mobile, grids Cloud-based infrastructure, platforms-as-a-service Computing Paradigms Monoliths Open / networked systems Client-server, Internet-based systems In-memory, multi-core, virtualization In-database, cloud, Internet-of-Things, APIs 1

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 1

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 1

10 Risk Analytics www.actuariesindia.org 1 Risk Based Limits
ENTERPRISE RISK MANAGEMENT DASHBOARDS AND REPORTS Capital Planning, IFRS 9 EST RBS & ICAAP Stress Testing & Scenario Analysis Recalibration Re Runs Comparatives Model Governance Integrations Auditability Data Governance Model Risk Treasury Core Banking Loan Origination System MARKET DATA Instrument Valuation Risk Measures Counterparty Credit Risk STD & IMM Compliance, FRTB MARKET RISK Credit Scoring Validations, Calibration Economic Capital STD & IRB Compliance, Portfolio Optimization CREDIT RISK Cash Flow generation Models – CASA, Options Funds Transfer Pricing Cash Flow Optimization ALM EGRC Monitor OpRisk Global Data OpRisk VaR Compliance Management Audit Management Operational Risk RISK INFRASTRUCTURE Liquidity Risk 1

11 Market Risk Valuations Risk Reporting Performance Management
Bootstrapping Valuation – FIMMDA / FEDAI Derivatives - Greeks Cash flow Pre/Post Trade Processing Risk Standardized Approach IMA Risk Factor Modelling Stress Testing CVA / SA CCR Sensitives FRTB Reporting Data Quality Disallowance Back testing RBI Regulatory Limit Management Internal MIS In Memory Reporting Performance Management Performance Analysis – Sharpe, Jensen, Sortino etc.. Portfolio Optimization - Efficient Frontier Portfolio Attribution 1

12 Market Risk 1

13 Market Risk 1

14 Market Risk 1

15 Market Risk 1

16 IFRS 9 www.actuariesindia.org 1 Data Collection New Information
Individual Account Level Forecasts / Historical Segmentation Individual Asset Level = Massive Amount Data = More Granular Data Forward Looking Calculations Financial Impact Increased Measurement complexity Additional Data Collection More Risk Models = New Analytical Models Governance Documentation Change Control Regulatory Capital forecast Model Management = New Control Framework 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 1

17 IFRS 9 1

18 Financial Crime www.actuariesindia.org Business Modules 1 UPI Cards
Branch Banking Fraud Compliance Customer Risk Rating AML Internal Fraud Office Accounts Internet Banking ATM Mobile Banking Application Frauds Trade Finance Early Warning Signals Threshold Optimization Enterprise Data Orchestration Alert Triage Case Management & Workflows Ops and Management Reporting Fraud and Financial Crimes Management Platform Advanced Analytics & Machine Learning 1

19 Anti Money Laundering 1

20 Predictive Models / Advanced Analytics Social Network Analysis
Fraud Business Rules Anomaly Detection Predictive Models / Advanced Analytics Social Network Analysis Text Mining 1

21 AML & Fraud 1

22 AML & Fraud www.actuariesindia.org AI / ML Technique Application Area
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 AI / ML Technique Application Area Business Value Name Entity Context Recognition Individual Profile Violation Peer Profile Violation New Scenario Detection Suppression Rules Development Financial Crime Prediction Alert Investigation Optimization 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 1

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 3:57 pm Scarf 3:46 pm Jeans 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 1

24 Behind the Scenes www.actuariesindia.org Check for Card w/Headroom
Check for Deposit Account Advise Mobile App or Branch Offer Personal Loan Offer Credit Card Offer New Overdraft Encourage Card Use Recommend Transfer Offer Overdraft Pre-Approved Overdraft? Run Credit Risk Check Warning! No Offer Evaluate Potential Offers 1

25 Information Retrieval Meets Intrusion Detection
Cyber Analytics Information Retrieval Meets Intrusion Detection System bases identification of security incidents and system failures on event log messages 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 Text analytics based Parsing, topic generation and term frequency and inverse frequency calculated Rarity Detection Spatial Temporal Analysis Existing investments are rules, threshold or signature-based, helping attackers evade detection with smart planning Lack of visibility of any network traffic that doesn’t trigger alerts “Analytics currently limited to perform historical analysis on a data lake or analyze incidents/alerts in the SIEM Text Analytics Based Intrusion Detection Intrusion detection systems is currently an active field of research in information systems and security Most information retrieval systems use a ranking method for generating results An advantage of the TF-IDF method of log analysis is that the weight calculations can be done very quickly. The TF-IDF adds a level of sophistication to the frequency analysis that will increase the number of actual Rarely related terms received and minimize the common and typical cybersecurity terms Spatial Temporal Analysis Uncovered a tremendous knowledge base of intrinsic patterns hidden in the data set, which records the time-dependent frequencies of attacks over a relatively wide range of consecutive IP addresses Spatiotemporal patterns in the underlying cyberattacks can uncover the hacker’s attack “fingerprints” Target selection scheme by identifying the very limited number of unique spatiotemporal characteristics over the consecutive IP addresses Spatio-temporal data mining integrated with machine learning approach could automate the process of finding these patterns and potentially aid in predicting and mitigating these large-scale global cyber-attacks. 1

26 Key Takeaways www.actuariesindia.org
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 1


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