All companies, and especially financial institutions, have to identify and measure risks. They have to find models for predicting such risks by analysing historic information and using expert knowledge. Future unavoidable, or sometime deliberately taken losses, need to be anticipated to balance against accruals or equity. Rules and regulations have been established to standardise risk measurement and predictions for financial institutions e.g. SolvV, KWG, MaRisk etc. The regulations are being continually revised (see Basel III, CRD III and CRD IV).

Market risk / scenario generation:
A business needs 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, as well undertake scenario analysis, simulation has to be undertaken 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 lead to competitive disadvantages. Furthermore appropriate methods for measuring internal risk and forecasting its adequate implementation often prove to be complex and challenging.

Data base 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

BearingPoint offers a broad portfolio with proven approaches and project procedures. These include:

• CRM (Credit Risk Management) – Analysis and illustration of complex capital market products within the credit portfolios, assistance with the development of appropriate and effective reporting tools to supply information for decision templates, supervision of the risk situation as well as surveillance of the effectiveness of risk management.
• SLR (Special Lending Rating) – Cash flow simulations allow banks to calculate an internal rating for special financing. The use of state-of-the-art techniques makes the relevant risk drivers transparent and objectively comprehensible.
• Life cycle support of risk models – Development and conceptual adjustment of robust risk models, as well as their integration into the IT landscape, ensures quality data and subsequent validation and support.