Continued volatility in the global financial markets has led to increased pressure and scrutiny on the financial services sector. Regulators have initiated stringent reforms such as Basel II/III for Banking. There has never been a greater need for uniform risk management analytics to ensure consistency and transparency while meeting regulatory requirements. However, risk data is typically created and stored on disparate technology stacks, produced in different geographies and organized by different risk classes such as credit/operational/market risk. This often hinders efforts to produce insightful, accurate and actionable risk analytics.
Qlik‘s analytics app supports regulatory credit risk management, with predefined methodologies and pre-configured reporting capabilities. You are fully supported for the Basel credit risk approaches with regard to the calculation of risk-weighted assets, credit risk mitigation, parameter estimation, stress testing, reporting, audit trails, transparency and documentation.
- Combining data for risk-weighted assets and amount of categorized capital from different systems in one application and automatically calculate the minimum common equity that is required and what should be the part of any category of capital (Tier 1 capital, Tier 2 Capital and so on). The data in the app is loaded automatically so we have actual information at any point in time.
- Automatically calculate the required capital buffers - capital conservation buffer and countercyclical buffer. The automatic reload provides actual information at any time.
- Automatically calculate the value of the leverage ratio and do comparative analyses – actual vs. required ratio and other analyses and reports. The data in the app is loaded automatically so we have actual information at any point in time.
- Automatically calculate the required liquidity ratio and have actual value at every time.
Background and logical architecture
- Create one or more scenarios:
- Economic states based on macro-economic factors like GDP, Unemployment etc.
- Typically create stressful economic situations (e.g., Unemployment of 10%, House-price index down 20% etc.)
- Apply these scenarios to a bank’s financial statements via the use of several models
- Loss prediction in bank’s loan portfolios, project profits
- Combine output of models into a financial statement including Capital projections
- Output submitted to regulators
The Use of Analytics in Stress-Testing
- Consideration of stress-test results across multiple scenarios can enable bank management to better interpret strengths and weaknesses in their portfolios, particularly to rare but potentially damaging economic situations
- Understanding of models can enable bank management to better explain the results of their stress-tests (including results for individual portfolios) to bank regulators
- Running stress-tests against multiple scenarios can provide detailed information of sensitivity of financial results against particular economic factors
- Reverse-Stress Testing involves determining a particular of scenarios that causes capital ratios to go below regulatory thresholds
- Analytics on detailed data helps question whether the projections incorporate unrealistic assumptions or unintuitive behavior
- Analysis: Detailed model output in ad-hoc data environment enables understanding of portfolio and model operation.
- Drillable: Loan level details available before and after stress runs by location, product type and loan characteristic.
- Significantly Improve speed and performance of stress-test process
- Improve control in stress-test process