COVID-19 is prompting banks to re-imagine risk management with data and analytics

By Manish Chopra, global risk and analytics leader, Genpact

COVID-19 has increased demand for financial support from banks to both businesses and individuals. And these banking customers need economic relief fast. But more requests from customers and a greater need for speed means banks are taking far more risk today than they were just a few months ago. They are also operating in a new environment for which their existing risk controls, compliance practices, and business processes were not necessarily designed.

As a result, banks’ chief risk officers (CROs) must take a more sophisticated approach to risk management, supported by the emerging digital, artificial intelligence (AI), and analytics capabilities. And, in particular, CROs need to pay special attention to credit risk and operational risk.  

Managing credit risk to maintain liquidity

Banks are already seeing a significant rise in consumer and commercial customers seeking credit. For example, Berenberg Bank research revealed that in December 2019, companies around the world used only 8% of available credit, a figure that jumped to 46% in March 2020, and to 78% for those companies struggling the most with the impact of the pandemic.

And the real figure for 2020 is likely far higher, as the analysis could not possibly include all small to medium businesses looking for credit. At the same time, cash-strapped consumers are reaching out for personal financial assistance, including overdraft increases, mortgage forbearance, and payment holidays, to help them cover their bills. This trend will continue as the recession worsens, putting stress on credit loss reserves. In addition, banks are likely to see a surge in delinquencies.

Most banks have a stronger foundation of capital today than they did when the 2008 global financial crisis hit. But the current situation is unique and brings its own issues. Managing credit risk, for example, may be more complex as demand increases and concepts of what constitutes financial hardship change. In this environment, banks must find a careful balance—ensuring they offer credit to customers who need it and remain solvent by sustaining necessary liquidity and capital ratios.

To gain a better understanding of their customers’ circumstances and take advantage of dynamic risk calculations, CROs will need to accelerate the implementation of digital technology, and data and analytics capabilities. For instance, using data analytics to conduct ongoing end-to-end portfolio monitoring enables CROs to predict risk by quickly identifying business clients with vulnerable supply chains or a high risk of over-concentration. Data analytics also can help with collateral monitoring, which will become increasingly important as customers default on loans. Advanced machine-learning tools can assist banks in determining loss mitigation actions and collections strategies that can help reduce charge offs, and therefore improve capital reserves and recoveries.

In addition, CROs should continuously stress test their own bank’s position to ensure they are confident in its liquidity, especially amid the rapid and continuing changes in government procedure. AI allows banks to model an enormous number of complex, evolving scenarios and consider multiple variables such as the potential duration of the crisis. These models generate insights that empower banks to introduce more nimble processes and policies, adapt to the changing situation, and establish defined priorities for their employees.

 Managing heightened operational risks of fraud, cyber, and business processes

The current pandemic has exposed organisations’ vulnerabilities across their operations, work force, and technological infrastructure. This chaos is creating a perfect ecosystem for opportunists to exploit various threat vectors, including internal vulnerabilities.

Businesses are gradually moving from the reactive to adoptive stage and are defining the “new normal,” such as work from home (WFH) environments that began in haste but many financial institutions are now considering as sustainable operating models with inherent benefits. Adoption of a WFH model attracts its own set of risks, beyond information technology, which banks must assess carefully as each firm’s risk appetite will vary all the way down to individual role levels. Companies can use advanced risk-scoring models to calculate the enhanced and residual risk of remote working, and balance the controls against risk severity and the potential impact.

The unprecedented shift in business processes and reactions is creating a need to review the existing risk profiles of organisations as they deal with a surge in activities, such as both inbound and outbound customer contact as customers look for support and banks actively inform them of new policies. These rapid changes are challenging existing business models. To enhance productivity, banks are boosting staff levels and exploring new communication channels. For example, automated solutions such as chatbots using machine-learning generated scripts suited to the current situation can help address queries in a fast, consistent, and cost-efficient way.

The plethora of regulatory actions to support economic crisis also is creating a need to design monitoring controls. Banks should examine existing processes and systems originally designed to manage business-as-usual operations to consider if they still meet evolving business models. Financial institutions can consider a framework of detective controls, trigger breaches, and monitoring cockpits to manage the transient phase.

Most banks already use fraud models based on historical and transactional data to establish patterns, identify anomalies, and signal risks. In the current climate, however, these models may be less effective given substantial changes in customer behaviour in recent months, including large cash withdrawals and increased usage of online and digital banking services. Risk leaders are building a two-pronged response strategy and adopting advanced data and analytics tools to gain deeper insight into the customer’s context and evolving habits, enabling them to more effectively spot suspicious activity. Leveraging data, analytics, and the right digital intervention is key to developing a responsive strategy aligned with the business goals to minimise risk impact in the imminent and more sustainable future.

Accelerating transformation to better support customers

Banks play a vital role in supporting the economy and wider society during the pandemic. This demands an accelerated approach to digital transformation; many of the advancements that were once seen as longer-term solutions are needed now. Financial institutions that increase their ability to use data more effectively and utilise sophisticated analysis with assistance from AI and machine learning to respond quickly will be able to better manage credit and operational risk. In the long run, they also enhance service to provide greater support to their customers in their time of increased need, as well as once the crisis passes.