A Dynamical Approach to Operational Risk Measurement

TL;DR

This paper introduces a dynamical model for operational risk in banks that captures the evolution and correlations of losses across processes, improving risk estimation and forecasting capabilities.

Contribution

It presents a novel dynamical framework for operational risk measurement that incorporates process interactions and temporal correlations, validated with real loss data.

Findings

Model accurately estimates parameters from historical data

Demonstrates improved forecasting of operational losses

Captures time evolution and correlations of losses

Abstract

We propose a dynamical model for the estimation of Operational Risk in banking institutions. Operational Risk is the risk that a financial loss occurs as the result of failed processes. Examples of operational losses are the ones generated by internal frauds, human errors or failed transactions. In order to encompass the most heterogeneous set of processes, in our approach the losses of each process are generated by the interplay among random noise, interactions with other processes and the efforts the bank makes to avoid losses. We show how some relevant parameters of the model can be estimated from a database of historical operational losses, validate the estimation procedure and test the forecasting power of the model. Some advantages of our approach over the traditional statistical techniques are that it allows to follow the whole time evolution of the losses and to take into account different-time correlations among the processes.