By: Joel Pruis
One might consider this topic redundant to the last submission around application requirements and that assessment would be partially true. As such we are not going to go over the data that has already been collected in the application such as the demographic information of the applicant and guarantors or the business financial information or personal financial information. That discussion like Elvis has “left the building”.
Rather, we will discuss the use of additional data to support the underwriting/decisioning process – namely:
- Personal/Consumer credit data
- Business data
- Scorecards
- Fraud data
Let’s get a given out in the open. Personal credit data has a high correlation to the payment performance of a small business. The smaller the business the higher the correlation.
“Your honor, counsel requests the above be stipulated in the court records.”
“So stipulated for the record.”
“Thank you, your honor.”
With that put to rest (remember you can always comment on the blog if you have any questions or want to comment on any of the content). The real debate in small business lending revolves around the use of business data.
Depth and availability of business data
There are some challenges with the gathering and dissemination of business data for use in decisioning – mainly around the history of the data for the individual entity. More specifically, while a consumer is a single entity and for the vast majority of consumers, one does not bankrupt one entity and then start a new person to refresh their credit history. No, that is actually bankruptcy and the bankruptcy stays with the individual.
Businesses, however, can and in fact do close one entity and start up another. Restaurants and general contractors come to mind as two examples of individuals who will start up a business, go bankrupt and then start another business under a new entity repeating the cycle multiple times. While this scenario is a challenge, one cannot refute the need to know how both the individual consumer as well as the individual business is handling its obligations whether they are credit cards, auto loans or trade payables.
I once worked for a bank president in a small community bank who challenged me with the following mantra, “It’s not what you know that you don’t know that can hurt you, it is what you think you know but really don’t that hurts you the most.” I will admit that it took me a while to digest that statement when I first heard it. Once fully digested the statement was quite insightful. How many times do we think we know something when we really don’t? How many times do we act on an assumed understanding but find that our understanding was flawed? How sound was our decision when we had the flawed understanding? The same holds true as it relates to the use (or lack thereof) of business information. We assume that we don’t need business information because it will not tell us much as it relates to our underwriting. How can the business data be relevant to our underwriting when we know that the business performance is highly correlated to the performance of the owner?
Let’s look at a study done a couple of years ago by the Business Information group at Experian. The data comes from a whitepaper titled “Predicting Risk: the relationship between business and consumer scores” and was published in 2008. The purpose of the study was to determine which goes bad first, the business or the owner. At a high level the data shows the following:
If you’re interested, you can download the full study here.
So while a majority of time and without any additional segmentation, the business will show signs of stress before the owner.
If we look at the data using length of time in business we see some additional insights.
Figure: Distribution of businesses by years in business
Interesting distinction is that based upon the age of the business we will see the owner going bad before the business if the business age is 5 years or less. Once we get beyond the 5 year point the “first bad” moves to the business.
In either case, there is no clear case to be made to exclude one data source in favor of the other to predict risk in a small business origination process. While we can look at see that there is an overall majority where the business goes bad first or that if we have a young small business the owner will more likely go bad first, in either case, there is still a significant population where the inverse is true.
Bottom line, gathering both the business and the consumer data allows the financial institution to make a better and more informed decision. In other words, it prevents us from the damage caused by “thinking we know something when we really don’t”.
Coming up next month – Decisioning Strategies.
