Methods and Models for predicting risks

Like all companies, especially financial institutions, you have to identify and measure risks. This involves finding models for predicting risks by analyzing historic information and using expert knowledge. Future losses that are unavoidable, or sometimes deliberately taken, need to be anticipated to balance against accruals or equity. Rules and regulations have been established to standardize risk measurement and predictions for financial institutions, for example SolvV, KWG, MaRisk. The regulations are being continually revised (see Basel III, CRD III and CRD IV).

Market risk/scenario generation:
You need to calculate the market risk to accurately price the risk for each transaction. This is done by evaluating risk factors. To effectively model complex financial instruments and analyze scenarios, you need to undertake simulation to predict the future trends of these risk factors.

Entry threshold/degree of coverage for the IRB-approach:
Banks have to prove that 50 % (in the future 80% and 92%) of the portfolio is compliant with IRB. Therefore, risks have to be adequately displayed for all business units via appropriate methods.

Risk prediction for complex credit products:
The application of regulatory predetermined values and approaches usually leads to competitive disadvantages. Furthermore, appropriate methods for measuring internal risk and forecasting its adequate implementation often prove to be complex and challenging.

Database for validations:
In recent years, many banks have developed internal risk forecasting methods which are subject to validation. Due to the absence of a consistent system landscape, and the implementation of standalone solutions, requirements for segment spanning and efficient validation are necessary.


How BearingPoint brings value

At BearingPoint we offer a broad portfolio with proven approaches and project procedures.

These include:


  • Philippe Binet

  • Franz Hiller


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