Towards an optimal bank balance sheet after Covid



We explain why challenger credit models are important to capture the impact of Covid on loan performance especially where champion models are not equipped to reflect the extensive government support measures.


The Covid-19 pandemic has dominated the agenda for banks for most of 2020 and a number of economic sectors are experiencing seismic shifts. The use of historical data in risk modelling, stress testing and portfolio allocation has been questioned in a crisis that differs fundamentally from previous periods of economic stress. Bank stress test models are often linked to GDP growth and the unemployment rate and for the severe recession experienced during 2020 these models predict dramatic increases in loan defaults and losses. However, the expected increases in defaults and losses have not yet materialised due to government support measures and payment moratoria that econometric models do not capture. In this article, we discuss how reporting tools and advanced analytics with alternative challenger models can be used to create forward-looking performance indicators of bank loan portfolios. We focus on lending to European non-financial corporates and the asymmetric impact of Covid on different industry sectors. We explain how such projections can help optimise bank lending including the use of expert overrides to better account for the uniqueness of the current crisis. Once forward-looking measures of risk and return are in place, portfolio theory can be used for optimal portfolio allocation and management.

“Delayed recognition and poor management of deteriorating asset quality could easily clog up bank balance sheets with non-performing loans for a fairly long period of time, making it more difficult for the banks to support viable customers and underpin a faster economic recovery.” Andrea Enria, Chair of the ECB Supervisory Board

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Towards an optimal bank balance sheet after Covid