June 2020

In a macroeconomic context hit by COVID-19, CRIF Group has renewed its commitment to provide its clients with timely updates on the regulatory context and on the appropriate tools to help manage changes in the lending sector. As acknowledged by the EBA and SSM, the economic impact of the spread of COVID-19 requires extraordinary measures to provide financial institutions with adequate flexibility and protect them from excessively procyclical effects.

CRIF has produced its first position paper, in collaboration with Andrea Resti, CRIF Senior Advisor and Professor at Bocconi University, summarizing some of the key measures defined by the main European institutions (ECB, EBA, ESMA, IFRS, EU Commission) as a result of the current extraordinary situation, and some initial insights into the resulting evolution of credit risk management systems.

Institutions are shifting on a number of fronts, which is why CRIF has initiated timely monitoring in order to understand, now more than ever, the different interrelations between regulations and the relative impacts. The aim is to monitor the rapid evolution of regulations and to quickly understand the possible constraints/opportunities that will have to be taken into account in order to ensure even more timely monitoring of the difficulties, while at the same time intercepting early signs of recovery.

For CRIF, the strategic approach, aimed at dealing with these circumstances, must focus on the evolution of risk models, the improvement of collection processes, and digitalization. A substantial increase in credit applications is expected, so banks will need to implement “smart” origination processes to speed up lending process, reduce fraud, and benefit from government-backed guarantees. Moreover, financial institutions will quickly need to improve digital services, because as usage grows, the user experience will become a key parameter in choosing one bank over another. Another important aspect of origination will be the integration of customer portfolio analytics with external data through artificial intelligence services.

In terms of monitoring, a large increase is expected in counterparties with early warning signals. In this regard, the challenge is to adapt early warning models to capture signs of stress more effectively and reduce the transition of loans to Stage 2. This may require the implementation of new triggers based on internal and external data relating to counterparties that have previously benefited from payment holidays.