The loose monetary and fiscal policy put in place by major central banks during the pandemic has driven irrational exuberance in 2021. Studying the last few financial crises has taught us that when the party ends the effects can be sharp and nonlinear.
Bank risk managers need to be ready if, as now seems likely, the music stops playing in 2022.
Credit intermediation remains the lifeblood of the economy. This is particularly so in Europe, where the corporate sector is reliant on bank lending more than capital markets. If Europe’s status as the world economy’s sick man is to change, healthy bank credit intermediation will be crucial.
Almost two years on from the outset of the COVID-19 pandemic, corporate credit defaults are at record lows. In Europe, the proportion of nonperforming loans has fallen from 7% in early 2015 to just over 2% in 2021. Economic growth is strong, and the stock market is near record highs.
But the dispersion in share price performance between individual companies illustrates wide divergences in current and future expected financial performance owing to disruption from technology innovation, inflation, supply chains and climate change. Individual company selection relative to macro portfolio views also will be increasingly important for credit risk managers.
Central banks are questioning the huge variations in provisioning across the banking sector during the pandemic. The European Central Bank, recognizing the short-term benefit of macro policy and new emerging risks, has highlighted the need for improved early-warning systems at banks. On Dec. 4, it stated that “it is becoming increasingly important for significant institutions to ensure that risk is adequately assessed, classified and measured on their balance sheets … thus minimizing and mitigating any cliff effects where possible.”
The global revenue pool for corporate lending is fragmented in comparison to trading and investment banking. There are national and regional champions. When the credit cycle turns, we are likely to see more differentiation between good and bad credit risk managers. We saw a similar trend in the world of trading more than a decade ago during the financial crisis.
Corporate credit risk analysis has relied on an individual company’s historical financial data, credit-rating downgrades and market indicators such as credit defaults swaps. Now, though, acting on red flags appearing in these indicators is often a case of closing the stable door after the horse has bolted.
The boom in unstructured data available online offers tremendous opportunities to find new red flags. It also increases the challenge of managing the noise-to-signal ratio.
Many traditional early-warning signals of a probability of default in current bank risk systems are proving to be meaningless because of out-of-date financials and other flawed information being used to underpin watchlist triggers, such as leverage ratios and covenant breaches. Hence, banks are understandably fearful of a surge in false positives if they widen credit risk assessment to capture a greater number of data sources. The low level of actual credit losses has meant a lack of urgency for bank senior management, in contrast to financial market and anti-money-laundering surveillance.
New data sources have to be carefully selected. These could provide signals on material changes in company earnings expectations, competitors, suppliers, weather patterns, information on subsidiaries, and other areas.
Examples of
The way banks monitor credit risk has lagged the adoption of technology in other parts of financial services whether it be trading, investing or client user interfaces in payments. Successful digitization of processes can provide economies of scale and speed that can drive competitive advantage and superior returns on equity.
Technology alone is not a magic bullet. Banks that will win have business leaders who know what data is relevant, how this data needs to be classified to generate useful insights and how this usage should be governed. The technology function can’t be an island where developers are not focused on business outcomes. Both business and technology leaders in banks need to understand what parts of the value chain need algorithms and which parts need human supervision.
Given the complex and rapidly changing environment, models will need to be calibrated carefully, with constant fine-tuning, and combined with expert judgments. Those credit risk managers who can manage this will be the ones that minimize both the number of false negatives and false positives.
