Model Risk Management Solutions 2014

<p>Model risk is the sleeping giant of risk management. Everyone knows that the risk, pricing, and performance&nbsp;models that financial institutions rely on can fall prey to errors. All models can only be mathematical&nbsp;approximations of reality and therefore have only limited accuracy and reliability.</p>

<p>Although this is a well-recognized fact, model risk management has not been a major risk management&nbsp;discipline until recently. The sense of complacency that led to an over-reliance on models in the run-up to&nbsp;the financial crisis has begun to dissipate. As a result, firms are now looking for systems to manage the risks&nbsp;associated with their models.</p>

<p>Increased regulatory requirements: Multiple regulators are making model risk management mandatory,&nbsp;including requirements under Solvency II, Basel 3, and Dodd-Frank. Additionally, regulators, including the&nbsp;OCC, Federal Reserve, and PRA are requiring firms to demonstrate evidence of controls and governance for&nbsp;models.</p>

<p>Stakeholder pressure: Shareholders and other stakeholders are demanding improved model risk management&nbsp;in response to losses caused by inaccurate or misused models. In particular, they are pressuring boards and&nbsp;senior management to take an increased role in model risk management.</p>

<p>Internal credibility: Errors in models are causing users to doubt their effectiveness and are creating a drive for&nbsp;improved model risk management within financial institutions.</p>

<p>Senior management involvement: Model risk management has not previously been seen as a strategic priority&nbsp;and has been implemented disparately with little or no aggregation. Senior management members need to lead<br />
enterprise-wide model risk management.</p>

<p>Mix of quantitative and qualitative risk management needed: Model risk management is difficult because it&nbsp;requires a mixture of quantitative (e.g. validation and testing) and qualitative measures (e.g. model use policies),&nbsp;which must be used in combination with one another.</p>

<p>End-to-end model risk management: To be effective, model risk management must encompass the full model&nbsp;development lifecycle and the full risk management lifecycle, from risk identification to risk assessment.</p>

<p>This report covers these trends in depth and provides a picture of the competitive landscape for model risk&nbsp;management solutions. This report uses Chartis’s RiskTech Quadrant® to explain the structure of the market. The&nbsp;RiskTech Quadrant^® uses a comprehensive methodology of in-depth independent research and a clear scoring&nbsp;system to explain which technology solutions meet an organization’s needs.</p>

<p>This report covers some of the leading vendors offering model risk management solutions, including&nbsp;ClusterSeven, Empowered Systems, FICO, FINCAD, IBM, Numerix, Oracle, and SAS.</p>

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• Topics
• HPC/Calculation/Processing
• Model Management & Validation
• Risk & Data Technologies
• GRC and Operational Risk