Improving Your Bank’s Credit Grading System

Better Credit Grade Granularity

Lots of banks have a limited number of credit grades. For the most part, the average bank has eight different categories, the first four of which are pass grades. If this is your bank, you are ripe to get hurt both in risk management and in loan marketing. Larger banks that use an infinite number of grades in their pricing model or at least use a two-tier system composed of probability of default and loss given default are at a material advantage when competing against a community bank that has a limited number of credit grades. At a minimum, banks should move to a 24-grade system with nine or ten passing grades. Most community banks effectively use only two grades for new loans – loan grades three and four. In this article, we will look at the risk that this creates and how to correct the problem.

 

Using a Limited Number of Grades

 

If your bank effectively just has two credit grades and you are competing against another bank with just two effective grades, then it doesn’t matter. However, if you go up against a bank with more credit grades, then you have a problem with adverse selection.

 

Let’s take a three bank example in a fairly typical commercial loan competition. Bank A has two effective grades that 98% of their loans fall into (typical). Bank B is a competing bank also with eight total grades and effectively grades three and four are where most of their loans fall into. Bank A also competes with Bank C across town that uses a more granular twenty-four grade system. This system is composed of ten pass grades, seven of which (grades three through nine) are used 95% of the time. Graphically, Bank B (top) and Bank C (bottom) are presented below. 

 

More Granular Credit Grades

 

What Happens in Real Life

Below is a chart showing Bank B and Bank C. We will assume a typical probability of default (POD) scale for both banks and have mapped each to their corresponding credit grade. We will also assume a typical quarterly loan volume that represents loans where both Bank A and B are in competition and both banks deliver term sheets. Bank A has a fairly uniform acceptance rate of around 60%. When competing head-to-head against Bank B, they win approximately $41.4mm for the quarter.

 

Now, if Bank A were to compete head-to-head with Bank C, the outcome is different. Here, Bank A retains a lower percentage of the quality loans in each grade and a higher percentage of loans in the riskier spectrum of each grade.  In this competition, Bank A only wins $34.6mm for the quarter, or almost 20% less.  

 

Credit Grades

 

The Real Risk

 

However, not having granular enough credit grades not only results in lower loan volume but loan volume of weaker credit quality. The situation is even worse than Bank A understands.

 

Apply the one-year probability of default percentages and assume a 30% loss given default across all credit grades. Here, you can see what happens to the expected loss number, which is analogous to a bank’s reserves. Despite booking a lower amount of commercial loans, Bank A now needs to hold 4% more reserves. On an expected loss percentage basis, that is the difference between holding 1.60% in an allowance for loan loss status and holding 1.99%.

 

Bank A likely doesn’t realize what is happening and is likely attributing the lower booked loan volume against Bank C to “dumb pricing.” Bank A likely only focuses on the loans they lost in the better credit quality segments of each loan grade. Unknown to Bank A they were adversely selected by Bank C which not only took the best loans but now has Bank A 24% under-reserved.  Bank A, not realizing the negative skew on their new credit quality, holds the typical 1.60% in loan loss reserve where they should be holding 1.99%.

 

Other Benefits

 

We didn’t want to complicate the above analysis more than it already is, but if we added pricing, in the form of a credit spread and rate, you would see that Bank C has a 20%+ better risk-adjusted ROE. Add lifetime value, and Bank C beats Bank A by almost two times. That means that Bank C’s ten-year risk-adjusted customer contribution to value is almost double Bank A’s. Bank C has more liquidity in its loans and is likely to have both a cheaper cost of capital and trade at a multiple significantly higher than Bank A.

 

Not only that, Bank C now has more accurate credit migration tracking which means a better allocation of resources and allocation of capital to credit problems.

The final benefit is earlier detection of credit problems as it takes 593 basis points of POD movement for a loan to move out of Grade Four for Bank A before they recognize that they have a problem. That could take multiple years. In contrast, Bank C would know within an average of 198 basis points of movement (depending on loan’s initial credit grade).

 

Putting This into Action

 

As your risk management culture evolves, consider going to a more granular credit grading system. We like an infinite number of grades that a risk-adjusted credit model allows, but if not, then a system with fifteen to twenty-four grades is usually sufficient to stratify pricing and credit enough to compete with almost any bank in the marketplace today.

 

Getting more granular with credit, particularly in a market characterized by high competition and increasing credit risk will help your bank not only win over more loans but dramatically improve both risk management and profitability.