Whoever came up with the old adage “let bygones be bygones” must not have had a sound understanding of loan portfolio analytics. This concept of learning from underwriting mistakes may seem obvious; however, financial institutions too often become so set in their comfortably outdated underwriting ways, that they do not take the time nor make the effort to learn from their mistakes.
Underwriting mistakes primarily come in two forms – applications that were approved that should have been denied, and applications that were denied that should have been approved. This article provides examples of how both types of underwriting mistakes can be avoided through analytics. I am not implying that following these suggestions will result in your institution never occurring another loss because outliers will always exist, and the term analytics is not synonymous with crystal ball. However, looking at patterns and trends and performing loan profitability analysis will result in better underwriting decisions and an improved bottom line for your institution.
Application Approval Mistakes
Learning which applications should not have been approved in the first place begins with identifying which loan characteristics correlate with future loss. Start this process by aggregating funded loan data by a common loan characteristic (e.g. original credit score, original LTV, original DTI Ratio, loan type), and then calculating charge-off ratios by that respective characteristic.
You are likely to notice that lower quality loans have higher loss ratios—the question is how high is too high? Should a loan type with a 5% charge-off ratio be discontinued? Does a loss ratio of 4% mean that you should stop lending to individuals with a credit score below 550? The answer is that it likely depends on your risk tolerance and the interest rate being charged – often times loan segments with the highest loss ratios are also the most profitable.
How can you know if the loss ratio is too high? If the sum of the one year charge-off ratio, operating expenses, and cost of funds is greater than the weighted average interest rate for any given segment (e.g. credit score tier, product type), then it’s likely time to consider either hiking the rates or halting lending within that segment altogether. The table below shows an example of a loan profitability analysis by original credit score grade.
As shown, all of the segments have a positive net yield and this portoflio appears to be priced appropriately for risk.
Application Denial Mistakes
Financial institutions leave money on the proverbial table every day by denying loans to individuals deemed too “risky” because of a low credit score or a high debt to income ratio or some other metric. A line needs to be drawn somewhere, but where should the line be drawn? Visible Equity has designed loan portfolio software to help you with this daunting task.
The ‘What If’ Analysis report first shows you basic statistics (e.g. count, balance, average credit score, average APR, average DTI Ratio, average LTV, average probability of default) on the applications your institution both approved and denied within any given time frame, and for any set of loan types. Additionally, this report shows both the gross revenue and expected return for the sets of approved and denied applications. This expected return figure takes loss incurred from charged-off loans into consideration, using Visible Equity’s probability of default model*.
Before you spend too much time lamenting the millions of dollars left on the table, build various ‘What If’ scenarios to see how your loan portfolio would have performed under different underwriting criteria. The example below shows how a pool of real estate applications received in 2014 would have changed if every application with a credit score above 640 would have been approved. The results are interesting – an additional $51.9M booked (50% increase) and an additional $1.5M of expected return, all while only marginally increasing the risk within the portfolio.
Many scenarios should be tested, tweaking multiple underwriting factors (e.g. credit score, DTI ratio, LTV) to better understand how changing underwriting standards will impact the portfolio’s growth, risk, and bottom line.
While there is certainly more to consider than maximizing return, taking this sort of approach to analyzing application data can help you avoid the underwriting mistake of allowing other financial institutions to approve and reap the rewards of the loan applications that you denied.