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The path to a Modern Data Architecture in Financial Services


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Delivering Data-Driven Applications at the Speed of Business: Global Banking AML use case.

Chief Data Officers in financial services have unique challenges: they need to establish an effective data ecosystem under strict governance and regulatory requirements. They need to build the data-driven applications that enable risk and compliance initiatives to run efficiently. In this webinar, we will discuss the case of a global banking leader and the anti-money laundering solution they built on the data lake. With a single platform to aggregate structured and unstructured information essential to determine and document AML case disposition, they reduced mean time for case resolution by 75%. They have a roadmap for building over 150 data-driven applications on the same search-based data discovery platform so they can mitigate risks and seize opportunities, at the speed of business.

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The path to a Modern Data Architecture in Financial Services

  1. 1. © Hortonworks Inc. 2011 – 2016. All Rights Reserved The Path to a Modern Data Architecture in Financial Services Vamsi Chemitiganti GM for Banking & Financial Services, Hortonworks @Vamsitalkstech © Hortonworks Inc. 2011 – 2016. All Rights Reserved Lee Phillips Sr. Director, Product Management, Attivio
  2. 2. © Hortonworks Inc. 2011 – 2016. All Rights Reserved Speakers Lee Phillips Sr. Director, Product Marketing Attivio Vamsi Chemitiganti GM, Financial Services Hortonworks Part of the Product Marketing team and responsible for analyst relations at Attivio, Lee brings over 35 years of experience in product, marketing, and business development in software and information solutions. His background includes MSE, management, and senior management positions for market innovators such as Lotus, Borland, Ziff-Davis, FAST, and NewsEdge. Vamsi is responsible for driving Hortonwork's technology vision from a client business standpoint. The clients Vamsi engages with on a daily basis span marquee financial services names across major banking centers in Wall Street, Toronto, London & Asia, including businesses in capital markets, core banking, wealth management and IT operations.
  3. 3. Agenda •  Introductions •  Trends in Financial Services Risk & Compliance •  Trends in the AML Space •  Why Open Enterprise Apache Hadoop for Modern Data Architectures •  Architectures & Work Streams •  An AML Case Study •  Q & A
  4. 4. Page 4 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Big Data in the Financial Services Industry Page 4 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
  5. 5. Hortonworks Key Focus Areas in Financial Services Common Focus AreasSegments of Banking Risk Mgmt Cyber Security Fraud Detection Predictive Analytics Data AML Compliance Digital Banking 360 degree view Customer Service Capital Markets Corporate Banking and Lending Credit Cards & Payment Networks Retail Banking Wealth & Asset Management Stock Exchanges & Hedge Funds +
  6. 6. Demand drivers for Big Data in Retail Banking & Capital markets Catalyst Definition Example Larger data sets Larger data sets allow analysts to query and conduct experiments with fewer iterations Omnichannel data, Tickers, price, volume and longer time horizons. Social media/ third party data New types of data New data types that need to be synthesized for traditional relational databases Business process data, Social Data, Sensor & device data. OTC contracts and public filings. Analytics and visualization More powerful analytics and visualization tools to explain and explore patterns – Fraud, Compliance & Segmentation Complex Event Processing (CEP), predictive analytics. Portfolio and risk management dashboards Tools and lower-cost computing Open source software tools. Lower server and enterprise storage costs Hadoop, NoSQL. Commodity hardware. Elastic compute capacity.
  7. 7. Transformation --- Maturity Stages à OptimizationExplorationAwareness ---MaturityStagesà Peer Competitive Scale Standard among peer group Common among peer group Strategic among peer group New Innovations No Use Case Name 1 Single View of Ins/tu/on 2 Predict Risk Exposures 3 Predict Counterparty Default 4 Automa/on of Client Due Diligence for consumer onboarding 5 Enhanced Transac/on Monitoring 6 Enhance SAR Accuracy 7 Credit Risk Calcula/on 8a Regulatory Risk Calcula/ons – Basel III & CCAR 8b Regulatory Risk Calcula/ons – Basel III & CCAR 9a Calcula/ng VaR across mul/ple trading desks 9b Calcula/ng VaR across mul/ple trading desks 10 Calculate credit risks across a variety of loan porRolios 11 Internal Surveillance of Trade Data 12 CAT (Consolidated Audit Trail)/OATS Repor/ng 13 EDW Offload Corporate & IT Functions Trading Desks Retail Banking Use Cases are available at different levels of maturity Surveillance Security & Risk 2 8a 5 7 1 6 3 4 9a 10 11 12 8b 9b 13
  8. 8. ©2015 Attivio, | Proprietary and Confidential GOVERNANCE, RISK, AND COMPLIANCE TRENDS REGULATORY PRESSURE, ENFORCEMENT SCRUTINY Multiple frameworks increase the economic cost of monitoring A CRISIS FOR DATA MANAGEMENT Increasing volume, velocity, and variety of Risk & Compliance data QUEST FOR EFFICIENCY & EFFECTIVENESS Shift from manual to cognitive and automated processes
  9. 9. ©2015 Attivio, | Proprietary and Confidential THE MOST COMMONLY CITED CHALLENGES Global Inconsistency Absence of uniformity across jurisdictions raises regulatory scrutiny Lack of Cognitive Understanding Must make sense of an explosion in unstructured information Information Fragmentation Multiple silos, solutions, and sources create expensive friction
  10. 10. ©2015 Attivio, | Proprietary and Confidential Achieve Certain, Global Impact A single-view of the transaction or entity, across jurisdictions Correlate Information for Understanding Discovery of the structure inherent in unstructured information Unify Information Virtual integration across multiple silos, solutions, and sources REQUIRED: A HOLISTIC, COGNITIVE SOLUTION
  11. 11. Page 11 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Illustrative Use Case – Anti Money Laundering Page 11 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
  12. 12. General Trends in AML Trends •  Increasing levels of criminal sophistication •  Illicit activities span geographies, products and accounts •  Expert systems and rules-engine approaches are becoming less effective •  Inefficient investigation tools and processes aren’t keeping up Impacts for AML •  Programs must evaluate multiple, varied data sources •  Require a 360-degree view across much larger data sets •  Automated, predictive approaches must replace manual, reactive programs
  13. 13. The Current State of AML Data Analysis •  Investigators demand interactive, visually appealing user interfaces •  Data discovery and predictive analytics can show deeper customer trends •  Aging technologies and their supporting approaches should be retired •  Companies are adopting advanced risk classification approaches •  New technologies help reduce the number of “false positives”
  14. 14. ©2015 Attivio, | Proprietary and Confidential ANALYTICS DRIVE COGNITIVE SEARCH BEHAVIORAL ANALYTICS Surprise Factor Improbability Scores Outlier Detection IMPROVED RISK SCORING Rule Management Alert Logic Layered Scoring STATISTICAL EXTRACTION Stock Tickers Credit Card Numbers CUSIPS RUNTIME ENHANCEMENTS Extreme Scale Rapid Document Processing Immediate Rule Applications
  15. 15. Page 15 © Hortonworks Inc. 2011 – 2016. All Rights Reserved How Current AML Solutions Fall Short Page 15 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
  16. 16. What We Have Seen at Banks Fragmented Book of Record Transaction systems •  Lending systems along geographic and business lines •  Trading systems along desk and geographic lines Fragmented enterprise systems •  Multiple general ledgers •  Multiple Enterprise Risk Systems •  Multiple compliance systems by business line •  AML for Retail, AML for Commercial Lending, AML for Capital Markets… •  Lack of real time data processing, transaction monitoring and historical analytics Proprietary vendor and in-house built solutions •  Acquisitions over the years have built up a significant technological debt •  Unable to keep pace with the progress of technology •  Move to combine Fraud (AML, Credit Card Fraud & InfoSec) into one platform •  Issues with flexibility, cost and scalability
  17. 17. ©2015 Attivio, | Proprietary and Confidential AML: STRENUOUS CHALLENGES Speed, transparency, and auditability for each new framework Increased Expectations of Regulators Complexity Integrating Application & Data Silos Manual Process Wastes Millions in OpEx “Overtime reviewers made more than our Execs…” Chief Data Officer Typical case reviews involve over 125 facts from 20 sources
  18. 18. …And the Data Complexity Continues to Grow •  Tens of point-to point feeds to each enterprise system from each transaction system •  Data is independently sourced, leading to timing and data lineage issues •  Business processes are complicated and error-prone •  Reconciliation requires a large effort and has significant gaps Book of Record Transac/on Systems Enterprise Risk, Compliance and Finance Systems
  19. 19. ©2015 Attivio, | Proprietary and Confidential CLASSIC CONFIGURATION OF DATA SOURCES
  20. 20. ©2015 Attivio, | Proprietary and Confidential HOLISTIC COMBINATION OF DATA SOURCES GRC DATA UNIFICATION PLATFORM
  21. 21. Page 21 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Illustrative AML Use Cases and Work Streams Page 21 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
  22. 22. Leading AML Use Cases •  Large transfers across geographies •  Single view of a customer with multiple accounts •  Linked entity analysis •  Watch-list monitoring and data mining •  Credit card fraud detection
  23. 23. Major areas of activity around AML.. •  Automating Due Diligence around KYC data –  Simple information collected during customer onboarding –  More complex information for certain entities –  Applying sophisticated analysis to such entities –  Automating Research across news feeds (LexisNexis, DB, TR, DJ, Google etc) •  Efficient Case Management •  Applying Advanced Analytics (two sub Use Cases) –  Exploratory Data Science –  Advanced Transaction Intelligence
  24. 24. Stream Processing Storm/Spark ML Reference Architecture for Fraud/AML/Compliance Stream Flume Sink to HDFS Transform Dashboard UI Framework ELT Hive Storage HDFS/Spark ML Stream Kafka Stream to Kafka Stream to Flume Forward to Storm Monitoring / KPI NoSQL HBase Real-Time Index Search Solr ELT Pig Batch Index Alerts Bolt to HDFS Dashboard Silk JMS Alerts Interactive HiveServer Visualization Tableau/SAS/ETC Reporting BI ToolsBatch Load High Speed Real Time and Batch Ingest Real-Time Batch Interactive Machine Learning Improved Models Load to Hdfs SOURCE DATA Customer Account Data/ CRM/MDM Transaction Data Order Management Data Click Stream Log//Social Data Documents EDW File REST HTTP Streaming RDBMS Sqoop JMS
  25. 25. ©2015 Attivio, | Proprietary and Confidential AN EFFICIENT, SCALABLE, AND ANALYTIC ANSWER
  26. 26. ©2015 Attivio, | Proprietary and Confidential ANTI-MONEY LAUNDERING Case Study 26
  27. 27. ©2015 Attivio, | Proprietary and Confidential SOLUTION REQUIREMENTS Generates automatic case summaries and narratives from all relevant R&C systems, providing a consistent, holistic view of suspect transactions: •  Gathers relevant facts from every R&C solution or data source •  Provides multi-lingual text analytics that support key phrase detection, entity extraction, and synonym expansion in unstructured content sources •  Initiates alerts and triggers when specific words, phrases, or content are detected during processing •  Provides –best-in-class search capabilities that power forensic investigation Provides proactive monitoring and compliance across the entire organization
  28. 28. ©2015 Attivio, | Proprietary and Confidential “SINGLE-PANE” SOLUTIONS Assignment Investigation Narrative
  29. 29. ©2015 Attivio, | Proprietary and Confidential INTEGRATE & OPTIMIZE : RESOLVE CASES FASTER Challenge – Achieve a productivity breakthrough to reduce compliance cost Attivio Solution – Deliver all evidence to a single screen for review and reporting Outcome – 75% reduction in MTTR for case investigations
  30. 30. ©2015 Attivio, | Proprietary and Confidential INTEGRATE & CORRELATE : REDUCE “False Positives” Challenge – Reduce ‘false positive’ costs without missing true positives Attivio Solution – Deeper analytics adds risk scoring to violation screening Outcome – Reduced ‘rules’ footprint and over 85% decrease in ‘false positives’
  31. 31. ©2015 Attivio, | Proprietary and Confidential Achieve Global Impact Act With Certainty Crush Your DeadlineTransform Productivity $27M to $54M Instantiate consistency and improve accuracy Confidently seize opportunities and mitigate risks by considering the right information in context Unify and enrich all evidence silos to save time Immediately discover and provision new evidence, when needed, for timely insight $2M to $3M $29M to $34M $8M to $9M THE VALUE : $66mm - $100mm ANNUALLY •  Discover, profile and correlate all internal and external data for agile insight §  Reduce time for Investigators to review, research and gather to close cases more quickly •  Reduce reliance on IT to provision data •  Connect or modify evidence sources as regulatory frameworks evolve •  Use outcomes analysis to increasing alerting accuracy- reduce ‘false positives’ §  Protect the brand and reduce risk resulting due to inaccurate or delayed reporting of suspicious activity §  Scale AML solution globally §  Expedite access to case information to efficiently assign, research and close cases §  Uniform risk-scoring §  Close all cases; eliminate sampling and backlogs
  32. 32. ©2015 Attivio, | Proprietary and Confidential PRINCIPAL BENEFITS Increases investigation throughput by up to 300% Transforms Investigator Productivity Reduces Complexity by Integrating All Sources Reduces Risk to Brand Value Close 100% of cases, even the most complex Provide all evidence on a ‘single-screen’
  33. 33. The Advantages of Big Data AML Solutions •  Hortonworks Data Platform (HDP) is a linearly scalable platform already in use at many of the world’s largest financial services companies •  Hortonworks takes a 100% open-source approach to Connected Data Platforms that manage data-in-motion and data-at-rest •  Partnering with an open source vendor gives banks more options than choosing a proprietary software platform •  Regulators are streamlining their regulatory practices by adopting a Big Data approach Contact Hortonworks to discuss your journey to actionable intelligence for AML
  34. 34. Questions? Page 34 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
  35. 35. Thank You Vamsi Chemitiganti GM, Banking & Financial Services, @Vamsitalkstech Hortonworks Lee Phillips Sr. Director, Product Marketing Attivio Page 35 © Hortonworks Inc. 2011 – 2016. All Rights Reserved