This paper seeks to establish good practice in setting inputs for operational risk models for banks, insurers and other financial service firms. It reviews Basel, Solvency II and other regulatory requirements as well as publicly available literature on operational risk modelling. It recommends a combination of historic loss data and scenario analysis for modelling of individual risks, setting out issues with these data, and outlining good practice for loss data collection and scenario analysis. It recommends the use of expert judgement for setting correlations, and addresses information requirements for risk mitigation allowances and capital allocation, before briefly covering Bayesian network methods for modelling operational risks.

This paper explores dependencies between operational risks and between operational risks and other risks such as market, credit and insurance risk. The paper starts by setting the regulatory context and then goes into practical aspects of operational risk dependencies. Next, methods of modelling operational risk dependencies are considered with a simulation study exploring the sensitivity of diversification benefits arising from dependency models. The following two sections consider how correlation assumptions may be set, highlighting some generic dependencies between operational risks and with non-operational risks to assist in the assessment of dependencies and correlation assumptions. Supplementary appendices provide further detail on generic dependencies as well as a case study of how business models can lead to operational risks interacting with other risks. Finally, the paper finishes with a literature review of operational risk dependency papers including correlation studies and benchmark reports.

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