A Structural Model for Estimating Losses Associated with the Mis-selling of Retail Banking Products

Abstract

In this paper, a structural model is presented for estimating losses associated with the mis-selling of retail banking products. This is the first paper to consider factor-based modeling for this operational/conduct risk scenario. The approach employed makes use of frequency/severity techniques under the established loss distribution approach (LDA). Rather than calibrate the constituent distributions through the typical means of loss data or expert opinion, this paper develops a structural approach in which these are determined using bespoke models built on the underlying risk drivers and dynamics. For retail mis-selling, the frequency distribution is constructed using a Bayesian network, while the severity distribution is constructed using system dynamics. This has not been used to date in driver-based models for operational risk. In using system dynamics, with elements of queuing theory and multi-objective optimization, this paper advocates a versatile attitude with regard to modeling by ensuring the model is appropriately representative of the scenario in question. The constructed model is thereafter applied to a specific and currently relevant scenario involving packaged bank accounts, and illustrative capital estimates are determined. This paper finds that using structural models could provide a more risk-sensitive alternative to using loss data or expert opinion in scenario-level risk quantification. Further, these models could be exploited for a variety of risk management uses, such as the assessment of control efficacy and operational and resource planning.