Let’s Get Real: Managing Consumer Credit Risk in a Time of Inflation
Amid volatile global markets rife with high interest rates and rising inflation, banks issuing loans today face myriad risks that present unique loss-forecasting challenges. But there are also growth opportunities for disciplined lenders – particularly those with risk modelers who understand the steps necessary to minimize loss exposures and more accurately capture default risk.
Friday, July 1, 2022
By Cristian deRitis
Consumer credit lenders need to prepare for the impact of inflation and higher interest rates on their portfolios. However, loss forecasting models trained on recent history may be blind to both forces, given the low-rate environment during the Great Moderation.
While more aggressive tightening of monetary policy, demand destruction and easing of supply chains should slow inflation later this year, continued disruption in energy and agricultural markets, along with the rising cost of shelter, will likely keep prices high until 2023 or 2024.
To properly assess the impact of inflation on credit loss, forecasters will want to distinguish inflation’s effects on their existing loan portfolios from effects on new originations. While much of the focus will be on loss mitigation, turning points in the business cycle often present opportunities for disciplined lenders.
Inflation Drivers and Loss-Forecasting Obstacles
Conventional wisdom says that inflation reduces the credit risk of fixed-rate borrowers. Assuming wages and salaries rise with inflation, fixed monthly payments should take a diminishing share of consumers’ budgets over time, thereby lowering their default risk.
While this logic is superficially reasonable, the source and size of inflation matters. If prices on essential goods (such as rent, food and energy) are rising faster than other prices, households may have limited resources for debt payments — even with a significant increase in their incomes.
The situation is analogous to the overreliance on unemployment rates for loss forecasting. During the early stages of the pandemic, many risk modelers realized that forecasting losses with unemployment rates was insufficient. Although historically the correlation between consumer credit losses and unemployment has been tight, it broke down in 2020 as large stimulus checks and unemployment benefits more than replaced lost wage and salary income.
The current environment provides a similar lesson, as even measures of gross income are proving insufficient to capture default risk. The true driver of borrowers’ ability to pay their debts is the residual income available to them after spending on necessities.
Under ordinary conditions, residual income is highly correlated with unemployment and total income, making these measures reasonable proxies for the underlying risk driver. However, in present times, it’s necessary to consider the fundamentals affecting behavior. Firms that rely too heavily on machine-learning algorithms to detect correlations may be particularly exposed to environmental shifts.
Borrowers don’t default simply because the unemployment rate is high. They are driven to default by shortfalls in income.
The order of the payments they prioritize often reflects Maslow’s hierarchy of needs. Spending on food, clothing and shelter are first, with transportation being critical to getting to and from work. For this reason, risk managers need to have a broad understanding of household balance sheets, rather than focusing solely on their own individual exposures.
It is important to recall that mortgage lenders felt they had nothing to worry about going into the Great Recession, because of the necessity of shelter. Borrowers, however, quickly prioritized auto payments over their mortgage loans, as they tried to hold on to their jobs.
Risk modelers should re-examine their loss-forecasting models to ensure that they formally include income drivers that can respond to changes in the overall financial position of their borrowers. At a minimum, they should monitor the broader economy to identify situations where existing models may break down and develop contingency plans for adjusting their loss forecasts as conditions warrant.
Originating Loans in a Time of Inflation
Loans initiated during a periods of either high unemployment or elevated inflation bring their own unique set of risks. Modelers need to account for factors (like income and prices) that impact one’s ability to pay, while also considering other behavioral aspects of the business cycle.
Specifically, the act of seeking credit during a time of rising interest rates or unfavorable economic conditions may itself be a signal of increased credit risk. Borrowers caught off guard by rising prices may have limited options, forcing them to apply for credit.
Credit-seeking behavior isn’t an automatic red flag, but lenders will want to proceed with caution to ensure their processes for detecting fraud are up to date. Simultaneously, well-capitalized institutions should keep an eye on the actions of their weaker competitors, should opportunities to expand open up in a contracting market.
Contrarian lending behavior can, indeed, be highly profitable. As a case in point, some of the best performing loan vintages of the past two decades were originated at the height of the Great Recession, when many lenders either went out of business or scaled back their operations.
To assess future default risks more accurately, forecasting models should include vintage effects, such as the state of the economy at the time of loan origination. By tying loss forecasts to the business cycle, modelers can control for the credit quality of current vintages while forecasting the behavior of future originations. This is particularly useful for helping senior managers set lending strategies.
In addition to these modeling approaches, lenders may also want to consider strategies for mitigating inflation risks. Adjustable-rate mortgages (ARMs) are an example of one common strategy for sharing interest-rate risk with borrowers.
For well-qualified borrowers, this arrangement may be mutually beneficial. However, borrowers who apply for ARMs solely for the purpose of lowering their monthly payments to an affordable level may find the product choice leads to higher credit risk. One need only look at the high default rates of negatively amortized ARMs to see evidence of this risk.
Given high levels of inflation, lenders might also consider offering inflation-indexed loans. As with Treasury Inflation Protected Securities, rates on a “real” mortgage or loan would be set at the rate of inflation plus a margin. Today’s borrowers for inflation-indexed loans might face high interest rates in the immediate term, but they would benefit should inflation return to historical levels.
While inflation-indexed loans may seem a perfect risk mitigation strategy, caution is needed, as lenders may simply be reducing their own inflation risk at the cost of potential credit default – unless they impose higher down payments or other requirements.
Given the long period of moderation they experienced, lenders and investors may have assumed that inflation was no longer an issue. That assumption has quickly changed, as interest rates on long-term Treasury bonds and mortgages have reached their highest levels in years.
Today, the strength of the labor market may help lenders avoid large nominal credit losses, but they may still incur a financial loss due to the lower value of received payments. There are several actions that risk managers should take immediately to minimize their losses and position themselves for future growth.
Diversifying strategies for existing and new portfolios is critical to this exercise. By returning to “risk management first” principles, lenders can minimize their loss exposures and prepare for new opportunities.
This type of approach will benefit both lenders and the broader economy. A continuous flow of credit is essential for reducing the duration and severity of the next recession when it arrives.
Cristian deRitis is the Deputy Chief Economist at Moody's Analytics. As the head of model research and development, he specializes in the analysis of current and future economic conditions, consumer credit markets and housing. Before joining Moody's Analytics, he worked for Fannie Mae. In addition to his published research, Cristian is named on two US patents for credit modeling techniques. He can be reached at email@example.com.