Presentation Transcript

1. Financial Services Global Business Unit Analytics and Big Data Ambreesh KhannaVP, OFSAA Product Management FSGBU
2. Program Agenda Big Data – what does it have to do with OFSAA? Customer Analytics Fraud Default Correlation for Securitized Bond Prices
3. Oracle Financial Services Analytical Applications FSDF
4. OFSAA and Big Data Relationship Pricing NBO Reputational Risk Fraud, AML, TC/BC Valuations for Credit Risk Payments Analytics Unified Data Model Use cases
5. OFSAA – Current Architecture
6. OFSAA High Level Architecture
7. Use Case – Customer Attrition 2 Event Customer gets married Customer Id: 12345 Name: Jane Doe Marital Status: Single Owns house: N No. of children: 0 CASA account Bi-weekly Direct deposit Avg. Balance: $10K Gold card Limit: $10K Balance: $7K 1 3 Event Customer buys a house Gets mortgage from competing bank 4 Event Customer has a baby Opens 529K with competing bank 5 Event Customer consolidates accounts Moves all accounts to competing bank
8. Use Case – Customer Retained with Better Insights 3 2 Customer Id: 12345 Name: Jane Doe Marital Status: Single Owns house: N No. of children: 0 CASA account Bi-weekly Direct deposit Avg. Balance: $10K Gold card Limit: $10K Balance: $7K Bank updates customer record Runs propensity models for NBO and makes time-bound loan offer for $50K for wedding at next point of customer interaction 1 Event Customer socially announces intent to get married 4 5 Event Customer searches for mortgage on bank website Bank preapproves customer for mortgage Makes offer at next point of customer interaction due to high propensity score 6 Event Customer announces pregnancy and eventually birth of child 7 Bank analyzes purchase pattern and predicts change in status; Augments score with data from social networks Makes 529K offer at next point of customer interaction as per propensity score
9. Customer Attrition Functional Flow Weblogs, emails, call records Core Banking, CRM User or segment matched
10. Use Case – Trader and Broker Compliance, Internal Fraud 1 TC/BC/Fraud software monitors patterns of trading activity 3 Models to find co-relation between events such as large institutional trades and personal calls, or employee accessing a articular customer activity on a regular basis 2 Additional data points to be provided to TC/BC/Fraud software Emails, SMSs, IMs, weblogs, social updates
11. Use Case – Payments Fraud 1 Wire Transfer transaction through Bank 2 Real time fraud detection engine does rule matching and machine learning models try to enhance patterns 3 Enhanced user profiles and history kept on HDFS Behavior detection models run on Map Reduce Approval/Denial response 5 Transaction persisted for detailed analytics 4 Additional data points User, address, geo-location previously known? Any known information from outside the bank about originator or destination?
12. Use Case – Anti Money Laundering 1 Monetary transactions 2 Graph analysis is extremely relevant to fraud detection Extremely large graphs cannot be analyzed with traditional means – order of complexity is likely non-probabilistic in time and space Some of these problems are hadoop-able AML software monitors Large cash transactions (CTR) Patterns to identify money laundering (SARs) KYC (checks against negative lists) 4 Graph analysis to detect patterns (vertices are entities, edges are transactions) Co-relation between SARs 3 Additional data points to be provided to AML External information about the customer
13. Fraud Discovery / adhoc Analytical Reporting ODBC d Technical Architecture *M/R “Sqoop” Batch process d HiveQL Endeca / OBIEE *M/R – Map Reduce or EID Endeca Information Discovery FSDF (DB 11.2.0.2+ with ORE) Collective-Intellect move to structured store additional /enriched attributes CI BDA (HDFS/ Cloudera ) Hive/NoSQL Unstructured Data Blogs Newsfeeds Watch List Scans Financial / Marketing /Trade data providers/channels Trxns a Source Systems a I C++ Pipes OCI / JNDI-JDBC I b ORE native connectivity HiveQL b HiveQL Batch R-connector for Hadoop *M/R Native *M/R OLTPSystems Stochastic Modeling subsystem (with ‘R’ support & ORE connectivity) AAI I I b I c AAI I II b Scenario Definitions (metadata) SOAP AAI Web-services interfaces included (WSDL) c Behavior Detection Inline-Processing Engine Post-Processing (pluggable services framework) AAI MSG queues
14. Using Big Data to Estimate Default Correlation Players involved in securitization transactions and their roles Rating Agencies Evaluate credit risk and deal structure, assess third parties, interact with investors, and issues ratings Financial Guarantor Asset Manager Insures tranches Trades assets Senior Funds Funds SPV Arranger Mezzaine Assets Pay outs Liabilities Pay outs Funds Pay outs Junior Originator Servicer Trustee Bonds with different ratings Collects & makes payments Monitors compliance Investors Funds Pay outs Prices of Bonds (i.e. tranches) are very sensitive to default correlation of loans We propose to use Big Data comprising of public and private information, Bloomberg and Reuters feeds, commercial transactions, analyst meets, and research reports to estimate default correlation Loans to Textile firms Loans to Energy firms Loans to Agricultural firms
15. Estimating Default Correlation and Securitized Bond Prices – Current State Analytical Applications Infrastructure (Data Quality, Metadata, Stress Testing, Modeling, Execution, Big Data Connectors etc) Loans to Energy firms Application-specific Processing Area Staging Area Results Area Treasury Systems Dashboards and Reports Credit Risk Engine Common Input area for analytical processing Loans to Agricultural firms Valuations Engine Core Banking Systems Default Metrics PD, LGD, EAD, Default Correlations Market Risk Engine Loans to Textile firms Company Specific Metrics Demographic, Geographic and Industry information Company Ratings Risky Bond prices floated by firms CDS spreads of the firms Balance Sheet structure and information Basel Engine Bond and Tranche Prices, Attachment and Detachment Points, Regulatory Reserves Front Office Systems (like CRM, RTD etc) Stochastic Models to estimate default metrics OBIEE Data Quality Checks, GL Reconciliations, Manual Data Adjustments Currently the estimation of default metrics like PD, LGD and Default Correlation only considers structured information Unstructured but rich information contained in Big Data sources like Bloomberg and Reuters feeds and news reports, Analyst comments and Research reports, News on commercial transactions etc. is completely ignored This results in poor default metrics and hence very poor and inaccurate Securitized Bond Prices Securitized Bond Prices are extremely sensitive to Default Correlation, and incorrect estimates of which was one of the main causes of 2008 market crash
16. Estimating Default Correlation and Securitized Bond Prices – Future State Using Big Data Sources Analytical Applications Infrastructure (Data Quality, Metadata, Stress Testing, Modeling, Execution, Big Data Connectors etc) Treasury Systems Application-specific Processing Area Staging Area Results Area Loans to Energy firms Dashboards and Reports Credit Risk Engine Common Input area for analytical processing Core Banking Systems Valuations Engine Loans to Agricultural firms Default Metrics PD, LGD, EAD, Default Correlations Market Risk Engine OBIEE Company Specific Metrics Front Office Systems (like CRM, RTD etc) Basel Engine Bond and Tranche Prices, Attachment and Detachment Points, Regulatory Reserves Loans to Textile firms Big Data Sources Bloomberg & Reuters feeds and news Analysts comments and Research reports Commercial Transactions Quarterly Investor meets, notes and public announcements Stochastic Models to estimate default metrics Data Quality Checks, GL Reconciliations, Manual Data Adjustments Augmenting traditional structured information with the new unstructured information from Big Data sources will result in better estimates of default correlation and PD, LGD, EAD Better estimates of default will result in more accurate prices of Bonds offered to investors via Securitization of assets Estimates of default can be updated quickly as new unstructured information becomes available
17. OFSAA at OpenWorld Monday, September 23 2:30-3:30 Making Sense of the Regulatory Challenges Facing Banks Today & Tomorrow Tuesday, September 24 10:30-11:30 Driving Business Growth by Unlocking Rich Customer Insights 5:15-6:15 Advanced Analytics for Insurance Wednesday, September 25 10:15-11:45 Big Data in Financial Services 4:15-5:15 Use-Case Driven Approach to Using OFS Data Foundation for Data Management Needs
18. Graphic Section Divider