IBM Watson Financial Services The Future of Risk Management Dr Neil Dodgson Risk & Compliance Innovation Forum 24 May 2017 2016 IBM Corporation
Regulatory Concerns Domestic & Geo-Political Risks Model Risk Capital Reforms Cyber Crime Surveillance Interest Rate Risk Stress Testing Credit Risk
The Burden of Regulatory Compliance $99B annual spend addressing compliance 1. Reduce costs of addressing compliance, freeing up funds to invest in growth 2. Address overwhelming amount of regulatory content and constant change $250B in sanctions & fines Addressing Client Pain Points 3. Reduce the significant risks associated with lack of adherence to regulations 4. Reduce duplicate spend across organizations in the industry 300M pages regulation by 2020 5. Leverage experienced compliance talent to focus on the highest-risk customers and activity
IBM Watson FS Solutions Portfolio Risk & Compliance Financial Crimes Customer Insight Sales Performance Management Aligned Industry Platforms Units Regulations Management GRC Reg Compliance Analytics Counter Fraud/AML/KYC Banking Banking Incentive Compensation Management Blockchain Safer Payments Wealth Management Promontory Financial Risk Commercial Payments Insurance Surveillance Insights Other IBM Software, GBS, Watson & Cloud Platform, GTS
Financial Risk & Capital Management Credit Risk Real Time exposure management, Credit Value Adjustment, Credit Origination Balance Sheet Risk Asset & Liability Management, Net Interest Income, Liquidity Risk, CCAR Capital Management & Shareholder Value Market Risk Regulation : Fundamental Review of the Trading Book, Stress Testing Asset Management Investment Analytics, Portfolio construction, Benchmarking, Pension & Fund management REGULATORY COMPLIANCE Solvency II Insurance Model Risk Governance Model Validation & Governance, Inventory management, Model dependencies, Model criticality, Audit
OpenPages Governance Risk & Compliance Platform Reputational Risk Predictive Analytics Regulatory Change Model Risk Design Thinking Key capabilities Audit Vendor Risk IT Risk Operational Risk Capital Modeling Conduct Risk Business Continuity Policy Management Advanced Visualizations Risk and Control Self Assessments (RCSA) Loss Event Management Reporting Policy Management Key Risk Indicators Issues Management Scenario Planning Workflow Comprehensive API
Regulatory Compliance Management Internalize & Assess Regulatory Obligations Map regulatory requirements to internal taxonomies & business structure Organize data into logical groupings across relevant business areas Distribute obligations to owners through regulatory compliance assessments Identify gaps/issues & evaluate level of compliance through reporting & analysis Step 2 7
What does the Future hold for Risk Management?
What does the Future hold for Risk Management? Aggregation Big Data Cognitive
Future of Bank Risk Management McKinsey reports on six trends that are shaping the role of the risk function of the future. Big Data technology has the potential to help banks mitigate the profitability impacts of regulation, make unbiased business decisions, and reduce operational costs. The future of bank risk management Trend 1: Regulation will continue to broaden and deepen Trend 2: Customer expectations are rising in line with changing technology Trend 3: Technology and advanced analytics are evolving Big data Machine Learning Crowdsourcing Trend 4: New risks are emerging Model risk Cybersecurity risk Contagion risk Trend 5: The risk function can help banks remove biases Trend 6: The pressure for cost savings will continue Source: http://bit.ly/2dahqfg, pg 4-7 10
A Brief History of Risk JP Morgan 4.15pm Report Basel I Credit Risk 1988 1 1 VaR is born RiskMetrics VCV 1994 Market Risk Amendment: Standardised & Internal Model 1996 Basel II Credit Risk Op Risk 2004 Basel II.5 Basel III FRTB!!! Default Risk Liquidity Expected Shortfall Stressed VaR CVA 2009 2010 2016
Aggregation Algo Aggregation for FRTB Reporting delivers highly flexible reporting with an FRTB reporting configuration that efficiently aggregates data from multiple systems and presents the results in a dynamic dashboard 1 2
TRIM - Model Risk Governance 2.2 Measurement of model risk across the internal models of the group10 7. An institution should have a model risk management framework in place that allows it to identify, understand and manage its model risk11 as it relates to internal models across the group. This framework should include the following. 1.(a) A model inventory that allows a holistic understanding of their application and usage. 2.(b) Guidelines on identifying and mitigating the areas where measurement uncertainty and model deficiencies are known. In particular the elements that relate to qualitative aspects of model risk (such as model misuse or implementation error) should be considered. This methodology should be applied consistently to the internal models across the group (e.g. within subsidiaries or regions). 3.(c) Definitions of roles and responsibilities. 4.(d) Definition of policies, measurement procedures and reporting.
Big Data Big benefits for ALM/LR Interfaces which deliver the on-demand results for improving strategies, must be built upon a highly efficient infrastructure. Big Data technology is widely recognized as delivering high performance at a low cost Big Data Potential Better and faster at a lower cost Source: https://www.dbresearch.de/prod/dbr_internet_en-prod/prod0000000000334340/big_data_-_the_untamed_force.pdf
Big Data IBM Client innovation in ALM/LR Data granularity IBM Algo One + Traditional Data Management 350K pooled positions Processed to reduce granularity from 6M source positions 6M source positions IBM Algo One + Big Data technology Interactivity with reporting results What-if Analysis Limited drill-down Liquidity risk reporting generated as part of the overall batch run is limited, where drill down into the underlying data requires other interfaces Full re-run required Adding new What-if stress scenarios requires a re-run of the batch process Full drill-down to source Reporting generated supports drill down and slice and dice all the way down to interpreting Algo One RiskWatch inputs, outputs and log files Incremental updates New What-if stress scenarios can be computed by the user adding additional scenarios to the scenario table Runtime 2 hours 20 minutes
Cognitive Cogniti ve Identifi cation of obligat ions
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