Operational Risk Management using a Fuzzy Logic Inference System

Operational Risk (OR) results from endogenous and exogenous risk factors, as diverse and complex to assess as human resources and technology, which may not be properly measured using traditional quantitative approaches. Engineering has faced the same challenges when designing practical solutions to complex multifactor and non-linear systems where human reasoning, expert knowledge or imprecise information are valuable inputs. One of the solutions provided by engineering is a Fuzzy Logic Inference System . Despite the goal of the Fuzzy model for OR is its assessment, it is not an end in itself. The choice of a fuzzy model results in a convenient and sound use of qualitative and quantitative inputs, capable of effectively articulating risk management’s identification, assessment, monitoring and mitigation stages. Different from traditional approaches, the proposed model allows evaluating mitigation efforts ex-ante, thus avoiding concealed OR sources from system complexity build-up and optimizing risk management resources. Furthermore, because the model contrasts effective with expected OR data, it is able to constantly validate its outcome, recognize environment shifts and issue warning signals.
International risk management practice for financial institutions focus on three main risk categories: Market Risk (MR), Credit Risk (CR) and Operational Risk (OR). The first two categories have a broad literature and, despite the recent financial turmoil, there exists some degree of consensus about the main characteristics a management model should fulfill in order to be considered useful. Meanwhile, in spite of being present in all financial institution’s activities and notwithstanding the fact that it accounts for some of the biggest losses in history (Moosa, 2007; Gallati, 2003), there is less progress and consensus about what an OR management model should be.

References
McNeill, M. and Thro, E. (1994) Fuzzy Logic: A Practical Approach, AP Professional. Moosa I. (2007) “Operational Risk: A Survey”, Financial Markets, Institutions & Instruments, Volume 16, Number 4, NYU-Stern.