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Modelling Operational Losses: A Bayesian Approach

The exposure of banks to operational risk has increased in recent years. The Basel Committee on Banking Supervision (known as Basel II) has established a capital charge to cover operational risks other than credit and market risk. According to the advanced methods defined in ‘The New Basel Capital Accord’ to quantify the capital charge, in this paper we present an advanced measurement approach based on a Bayesian network model that estimates an internal measure of risk of the bank. One of the main problems faced when measuring the operational risk is the scarcity of loss data. The methodology proposed solves this critical point because it allows a coherent integration, via Bayes' theorem, of different sources of information, such as internal and external data, and the opinions of ‘experts’ (process owners) about the frequency and the severity of each loss event. Furthermore, the model corrects the losses distribution by considering the eventual relations between different nodes of the network that represent the losses of each combination of business line/event type/bank/process and the effectiveness of the corresponding internal and external controls. The operational risk capital charge is quantified by multiplying the value at risk (VaR) per event, a percentile of the losses distribution determined, by an estimate of the number of losses that may occur in a given period. Furthermore, it becomes possible to monitor the effectiveness of the internal and external system controls in place at the bank. The methodology we present has been experimented as a pilot project in one of the most important Italian banking groups, Monte dei Paschi di Siena. Copyright © 2004 John Wiley & Sons, Ltd.