In digital private credit, where lending is predominantly unsecured, the core challenge isn’t avoiding defaults (losses)—it’s managing them effectively. Defaults are an inherent cost of doing business in this asset class. The key question is not whether losses will occur, but rather: how do actual losses compare to those expected at the time of loan underwriting?
Fintech has revolutionized loan underwriting by leveraging data science, machine learning, and automated decision-making, all at scale. This innovation was expected to keep the gap between actual and expected losses within a manageable range. And, for the most part, the leading loan origination platforms have delivered on that promise. The data we track show that, historically, variances between actual and expected losses have remained low, which supports our view regarding the asset class’s asymmetric risk-reward proposition.
However, this pattern broke for personal installment loans originated in late 2021 and 2022. In the worst of these vintages, actual losses came close to doubling the underwritten loss expectations—a deviation that significantly impacted investors.
To understand why this matters, let’s break it down:
Imagine a 3-year-term personal installment loan with a 20% APR (Annual Percentage Rate), originated to a borrower and sold to an investor. For the investor, the effective return isn’t 20% per annum; rather, it is the APR adjusted down by the probability of default (ignoring all other factors). If expected defaults are 8% annualized, the baseline expected return is 12%.
But what happens when actual defaults exceed the expected loss at underwriting? If defaults rise by 25% more than anticipated—jumping from 8% to 10%—the investor’s return should drop from an expected 12% to an actual 9.5% (ignoring all other factors). The math is nonlinear given (i) the amortizing nature of the loan and (ii) the statistical nature of loss behavior. If actual losses were to double, going from 8% to 16% annualized, the actual return would drop to 0.5%. This shift in defaults would have an outsized impact on portfolio economics, highlighting why managing the gap between actual and expected losses is critical.
Fast forward to today: How are actual losses tracking relative to expected losses? Based on our analysis of industry data, we see that the elevated variance from those difficult vintages in 2021 and 2022 has been steadily improving. Underwriting models that underperformed during that challenging period have since been recalibrated and are delivering significantly better results, with recent vintages experiencing little to no variance between actual and expected defaults.
Our observation is that recent underlying performance is more in line with the longer-term historical performance of the asset class than the outlier period of late 2021 and 2022.
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