Method and apparatus for operational risk assessment and mitigation

Abstract

Risk in business management is analyzed based on a probabilistic network approach which quantifies the impact of operational risk on financial metrics such as Value-at-Risk (VAR) and / or Potential Losses (PL). This approach provides further capability to determine the optimal placement of one or more countermeasures within a system to minimize the impact of operational risks.

Description

BACKGROUND OF THE INVENTION [0001] 1. Field of the Invention [0002] The present invention generally relates to modeling operational risk for business management and, particularly, to an approach of assessing the impact of economic impact of risk and optimizing risk control countermeasures. [0003] 2. Background Description [0004] Organizations are increasingly interested in robust systems for assessing and managing operational risk. The growing interest in operational risk management has been driven by a variety of factors, including the introduction of new regulations requiring businesses to quantify and manage operational risk, such as the New Basel Capital Accord, known as Basel II (see “The New Basel Capital Accord”, Bank for International Settlements, April 2003). [0005] A prevailing definition of operational risk is given by the Basel Committee on Banking Supervision as “the risk of loss resulting from inadequate or failed internal processes, people or systems or from external ...

AI Technical Summary

Problems solved by technology

The drawback with the statistical approach is that very limited data is available on operational risk events.

The drawback with the Bayesian network approach in the literature is that: (i) Inferencing problem in Bayesian networks is in general a computationally hard problem, i.e. NP-hard problem, which means that the computational effort grows exponentially as a function of input parameters such as risk events etc.

The implication of this is that this is not an efficient approach for operational risk modeling.

(ii) There is no systematic method known to construct these networks linked to business processes (e.g., of a financial institution).

Moreover, these also suffer from the limitation in data on operational risk events, which will hamper the calibration and updating of these models.

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