entire - Deutsche Bank Annual Report 2012

Deutsche Bank 01 – Management Report 103
Financial Report 2010 Risk Report
Measuring Our Operational Risks
In 2010 we have integrated into our operational risk management processes Sal. Oppenheim (except for those
parts which are in the process of being sold) and the commercial banking activities in the Netherlands acquired
from ABN AMRO as well as Dresdner Bank’s global Agency Securities Lending business. Although Postbank
manages its own operational risk, Postbank has also already been integrated into our economic capital calculation
on a basis consistent with Deutsche Bank methodology. Limitations in data availability, however, may lead to
portfolio effects that are not fully estimated and thereby resulting in over- or underestimation. The table below
shows the economic capital usages for operational risk of our business segments for the periods specified.
in € m. Dec 31, 2010 Dec 31, 2009
Economic capital usage (for operational risk)
CIB 2,735 2,822
PCAM 939 654
CI 8 17
Total 3,682 3,493
Economic capital usage for operational risk increased by € 189 million, or 5 %, to € 3.7 billion as of December
31, 2010. The higher economic capital usage driven by acquisitions (Postbank, BHF-BANK, parts of the commercial
banking activities in the Netherlands acquired from ABN AMRO and Sal. Oppenheim) was only partially
offset by lower loss frequencies due to proactive operational risk management.
We calculate and measure the economic and regulatory capital for operational risk using the internal AMA
methodology. Economic capital is derived from the 99.98 % quantile and allocated to the businesses and used
in performance measurement and resource allocation, providing an incentive to manage operational risk, optimizing
economic capital utilization. The regulatory capital operational risk applies the 99.9 % quantile. Our
internal AMA capital calculation is based upon the loss distribution approach. Gross losses adjusted for direct
recoveries from historical internal and external loss data (Operational Riskdata eXchange Association (ORX)
consortium data and a public database), plus scenario data are used to estimate the risk profile (that is, a loss
frequency and a loss severity distribution). Thereafter, the frequency and severity distributions are combined in
a Monte Carlo Simulation to generate losses over a one year time horizon. Finally, the risk mitigating benefits
of insurance are applied to each loss generated in the Monte Carlo Simulation. Correlation and diversification
benefits are applied to the net losses in a manner compatible with regulatory requirements to arrive at a net loss
distribution at the Group level covering expected and unexpected losses. Capital is then allocated to each of
the business divisions and both a qualitative adjustment (“QA”) and an expected losses deduction are made.