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Artificial Intelligence Based CD-WAR Project

Artificial Intelligence Based CD-WAR Project

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Artificial Intelligence Based CD-WAR Project

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  1. Artificial Intelligence Based CD-WAR Project November 2018 | Los Angeles

  2. Table of Contents • ProgressiveApproach to Governance • A StrategicVision • Creating a CompetitiveAdvantage • Drivers UnderlingtheCompetitiveAdvantage • AI Enhancement of KYC and MonitoringProcesses • The ChallengeDefined • CurrentParadigm • Project Objective • New Paradigm: Cooperative Data WarehouseforAnalysis and Reporting (“CD-WAR”) • Developmentand ImplementationConsiderations of CD-WAR

  3. I. Progressive Approach to Governance Old Paradigm New Paradigm Regulatory Management Risk Management Local Legislation Foundation Global Best Practices Reactive – Compliance with Existing Legal Requirements Management Model Proactive - Dual Objective: Transform & Mitigate Risk Internal Control Model Check-the-Box: Reporting Requirement Based Risk-based Approach: Ops., Customers, Counterparties Static: Regulatory Requirements Drive Change Dynamic: Evolving Risk Environment Drives Change Creates Obstacles to Achieving Business Objectives Creates Competitive Advantage and Increases Enterprise Value

  4. II. A Strategic Vision Level of Development FI “D” FI “C” FI “B” Environmental Shift FI “A” Time

  5. III. Creating a Competitive Advantage Equation Competitive Advantage New Trend Line for “B” Investment in Governance Model Subject “B” Environmental Change Growth Typical Growth Trend Line Opportunity Cost Subject “A” Time

  6. IV. Drivers Underling the Progressive Approach • The current complexity of Regulatory Reporting Requirements creates three (3) overarching, complementary goals for Regulators and Banks: • Streamlined and Simplified Reporting Processes – Regulators and Banks • Reduction of Redundancies and Inefficiencies (Cost Savings) in Supervisory Oversight • Creation of Operational Efficiencies (Cost Savings) for Banks • Globally, financial authorities continue to increase the demands on financial institutions (“FI”) by promulgating heightened levels of regulatory requirements (AML, Cyber, Financial Reporting and Integrity (Basel)). • FIs must dedicate additional resources (employees, budget, time) to comply with the ever increasing level of regulatory requirements. • Globally, FIs spend between 6% and 10% of revenue on compliance. • The U.S. has a progressive & aggressive approach to the theglobaregulatory supervision framework. • Over the past two (2) decades, The U.S. Has lead global initiatves to fortfy all areas of regulatory supervision, including: • Financial Crimes & Sanctions, • Financial performance of FIs, • Early problem detection. • Prompt corrective actions, • Resolution process, • Financial crime, • data security, and • financial systems.

  7. V. AI Enhancement of KYC and Monitoring Processes Current AML Approaches ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ALERTS ALERTS ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! Legacy Systems Expert-driven Rules +

  8. V. AI Enhancement of KYC and Monitoring Processes Transforming AML ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ALERTS ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! Machine Learning and Ensembles New Techniques Robotic Process Automation Identity Resolution and Relationship Detection • Generate logic and models based on data and continuous feedback • Combination of unsupervised and supervised learning • Layer multiple techniques to create “ensemble” models Natural Language Processing and Understanding Advanced Analytics • Data retrieval from systems • Extracting data from forms • Documenting audit trail • Generating narratives • Identify concepts in negative news • Identify people, companies and places in transaction details • Extracting information from documents • Understanding disposition reasons in case notes • Identify different potential instances of the same entity • Global name matching • Fuzzy, probabilistic matching of various attributes • Identify non-obvious, hidden relationships • Dynamic segmentation • Behavioral analytics • Network analysis • Graph analytics • Anomaly detection

  9. V. AI Enhancement of KYC and Monitoring Processes Addressing the Inefficiencies of KYC Due Diligence Operations Challenges & Needs How Cognitive Can Help Robotic Process Automation coupled with various analytics minimize workloads and delivers greater standardization, consistency, and efficiency High operating costs and complexity from disparate legacy systems Automating input from external data aggregation avoids unnecessary customer requests, reducing friction while speeding up processes Manual processes for KYC records are costly and lead to negative customer experiences False positives related to negative news bogs down analysis Cognitive analytics, whichquickly identifies more relevant articles, and context annotation accelerate analyst decision making

  10. V. AI Enhancement of KYC and Monitoring Processes Initial Results at Top 20 US Bank Enhanced Due Diligence on High Risk Customers RESULTS: 60% faster completion of external research activities Original System: 13:31 min per dossier IBM System: 5:20 min per dossier Feedback from Bank EDD Analysts With FCI

  11. V. AI Enhancement of KYC and Monitoring Processes Increasing Effectiveness and Efficiency of AML Transaction Monitoring Challenges & Needs How Cognitive Can Help Effective transaction monitoring with cognitive accelerators that augment current transaction monitoring investment Legacy, manual AML transaction systems hinder the ability to meet evolving regulatory requirements Faster identification of suspicious activity and with accurate insights including contextual information and analysis High volume of false positives impedes efficiency Lack of a holistic view of data, systems, and domain expertise Leverage automation, machine learning, cognitive capabilities, and Promontory domain expertise for an enhanced view of risk

  12. V. AI Enhancement of KYC and Monitoring Processes Current Systems Generate Noise Phased Application of Cognitive Capabilities Deliver Increasing Value Apply Cognitive Analytics ToImprove Efficiency & Effectiveness of Managing Alerts Use Insights for more efficient operational handling of alerts Intelligent Routing, Accelerated Reviews, Explainable Results TM PEP Sanctions !!!!! !!!!! !!!!!!!!!! !!!!!!!!! !!!!!!!!!!!!!!! !!!!!!!!!!!!!! !!!!!!!!!!!!!!!!!!! !!!!!!!!!!!!!!!!!!! TM PEP Sanctions

  13. V. AI Enhancement of KYC and Monitoring Processes Initial Results at Top 20 US Bank Triage on Retail AML Transaction Monitoring alerts RESULTS: Multiple customer ID linked to 9.7% of alerts Customers with more IDs are escalated more 10% workload reduction by consolidating alerts for customers with more than one ID 0% escalations in six customer segments generated by dynamic behavioral clustering; identified three segments with 20%+ escalations

  14. Initial Results at Top 4 Chinese Bank Triage on AML Transaction Monitoring alerts in US Correspondent Banking Division RESULTS: 65% of alerts scored as likely false positives, with NO SARs 72% of SARs identified in top 5.2% of alerts identified as high risk

  15. V. AI Enhancement of KYC and Monitoring Processes Initial Results at Top 4 Australian Bank Triage on Sanctions Alerts RESULTS: Sample of Findings 50% of alerts identified for special handling Identified entity mismatches (people, companies and places) and exonerating personal details as likely false positives Bundled hits for likely related entities to reduce workload Other Examples

  16. VI. The Challenge Defined • Based on the intrinsic importance of FIs to the health of global and local economies and commerce, coupled with the global trend of increasing financial regulation, a strong argument exists that financial regulation will continue to increase. This will: • (a) place additional, significant regulatory requirements on FIs, and • (b) require regulators to dedicate additional budgetary resources to supervise subject FIs. • The additional regulatory requirements will increase the demand on regulators’ already limited resources. • FIs’ current response to new regulatory requirements is to dedicate additional resources. This approach to regulatory compliance is not sustainable over time. • Regulators & FIs must identify & implement a technology-based solution that will lead them into the future and: • (a) ensure the effective implementation of programs by FIs to comply with new regulation, • (b) provide operational efficiencies to such FIs, • (c) reduce the cost of supervisory oversight programs to the regulatory authorities, and • (d) Must have support of FIs and Regulators.

  17. VII. Current Paradigm Operational Raw Data Regulatory Back-end Processes FRB Bank 1 Reporting Module Bank 1 1. AML 1. Aggregate Information OCC Bank 2 Reporting Module Bank 2 2. Liquidity 2. Assemble Reports FINCEN Bank 3 Reporting Module 3. Capital Bank 3 3. Develop Analysis FDIC Bank 4 Reporting Module 4. Cyber Bank 4 4. Decision & Response 5. Credit FCPB Bank 5 Reporting Module 5. Outreach Bank 5 BANKS REGULATORS Deficiencies in Current Process 500 Overlapping Reports / Errors and Fines 500 Reports, Each FI Manual Assembly and Validation Redundant Information Review and Analysis Manual process for Multi-dimensional view Errors and Fines Limited Budget – Staffing & Resources Continuous Up-dating

  18. VIII. Project Objective • Develop a technological solution, CD-WAR, that automates the extraction of raw data from FIs’ core systems, assembles into reporting formats for regulators, and validates the integrity of information & reports. • The solution will achieve the following common goals of the regulators and FIs: • Eliminate redundancies in the information and reports received by various regulators. • Decrease supervision cost by eliminating redundancies, streamlining the reporting process, and automating of report aggregation and analytical processes. • Create efficiencies for regulators in their review, analysis, and development of comprehensive reports. • Provide efficiencies to the FIs by eliminating the development of reports and extraction and validation of data, thereby allowing them to focus on the analysis of such information and reports. • Provide Fis and the Regulators with global best practices KPI and KRI reports and analysis. • Functionally, the solution will: • Provide an API for all FIs to connect their core system(s) to the CD-WAR for the provision of raw data. • FIs’ raw data will download from the FIs’ core system to the CD-WAR automatically or on a periodic basis. • Include tools to validate information and reports to reduce/eliminate manual intervention. • Convert automatically the raw data of FIs into the reports required by regulators, in addition to developing additional analytical reports for the FIs and regulators. • Ensure no impact on competition because the data available to each FI relates only to their FI and respective analytical reports.

  19. IX. Project Objective (cont’d) • Access to Information, Reports, and Analysis • Relevant Federal and State regulatory and supervisory agencies will have access to the same set of information, reports, and analytical reports, thereby eliminating discordant reporting information and the possibility for different conclusions. Such information will be available based on individual FI, a group of FIs, or all FIs. • FIs will have access to their reports, in addition to the core information and analytical reports that can drive strategic decision making processes. • Data and Information Security – The CD-WAR will be developed with the highest levels of data and information security due to the highly confidential and sensitive nature of the data, information, reports, and analysis that will be contained in such system. • Raise the level of KPI and KRI access, availability, analysis and decisioning.

  20. X. New Paradigm - CD-WAR Operational Raw Data CD-WAR (Global SOR) Regulatory Authorities extract reporting and analytical information directly from CD-WAR. Regulators can use CD-WAR as communication vehicle with FIs. Real-time or Periodic Financial Reporting Financial Crime Bank 1 Autofeed from SOR 1. AML FRP OCC Bank 2 2. Liquidity Autofeed from SOR FDIC 3. Capital Bank 3 Autofeed from SOR FINCEN 4. Cyber FCPB Bank 4 Autofeed from SOR 5. Credit Autofeed from SOR Bank 5 Module “X” Securities, Credit BANKS REGULATORS CD-WAR Efficiencies No Report Development or Manual Validation – No Redundancy Streamlined Reporting/No Redundancy/Manual Global Best Practices – KPI/KRI Analysis Automated Aggregation/Segmentation Analytical Reports Provided by CD-WAR Global Best Practices KPI & KRI Analysis System of Record for Reporting Purposes Automated, Multi-level/Division Analysis Homogenized Information & Reporting for FI Homogenized Information & Reporting for Regulator

  21. IBM’s Risk and Compliance Capabilities • Risk, process improvement, analytics and cognitive skills • Enterprise data management expertise for FIs and regulators • Industry platform capabilities IBM Global Business & Technology Services • Deep risk and compliance expertise • Controls and compliance testing, such as client on-boarding, transaction monitoring, and compliance reporting • Former regulators • Market leading cognitive and analytics technology • Cognitive regulatory compliance solutions • Secure, hybrid cloud data platform