The FICO score has a long and well-established history as a key metric in the determination of credit-worthiness. The FICO score has the power to influence whether a person can experience significant life events, like the purchase of their first car or home. Currently, it’s a major factor in credit union loan analytics.
However, as we rapidly enter the age of Big Data and loan analytics, does the FICO score utilize enough information to make an accurate determination of a borrower’s ability to pay? The wealth of data available to credit unions should augment their loan analytics.
A New Age of Loan Analytics
As I consider the future of credit unions, I believe the industry’s position on the significance of the FICO score in their underwriting process is an important issue. Is FICO a major determining factor, or is it merely one of many data points that can be used to predict probability of default for a given loan?
The mission of the credit union movement is to improve the lives of their members. While this is a very altruistic and admirable goal, it is only possible if credit unions can effectively assess and manage their loan portfolio risk. Current loan analytics strategies privilege the credit union over the member. At the end of the day, credit unions have a fiduciary responsibility to protect the assets entrusted to them by their members.
Credit unions are faced with delicately balancing two diametrically-opposed objectives when serving their members:
- Being more compassionate than the big banks when it comes to lending.
- Being “prudent,” as defined by NCUA guidelines, in their lending practices. For any loan application that is being processed by a credit union, the decision comes down to the FICO score and the Loan to Value (LTV), which is no different than the big banks.
Is there a better way to balance for loan analytics? The answer is a resounding, “yes.” Big Data and analytics is the new frontier for the retail lending industry.
If Others are Doing It…
Credit unions have access to volumes of internal data and the means to access external data. However, they lack the infrastructure and the culture to perform the loan analytics needed to improve their underwriting processes.
Expanded loan analytics platforms may have eluded credit unions, but others are leveraging more complete information. Lending Clubs are entering the retail lending market with lots of data (which credit unions also have) and loan analytics (an area where credit unions are behind the curve).
For example, if you have a teenage driver in your household, you know that your insurance agency will give you a discount for good grades. Insurance companies do this because there is a high correlation between high GPAs and safe driving records. However, a high correlation does not mean that all drivers with high GPAs will not have accidents (our household is a great example that this correlation is not 100%!). However, the correlation remains high enough that insurance companies are willing to provide discounts for teenage drivers with high GPAs.
Lending Clubs are taking a similar approach to the insurance industry by expanding the set of criteria used to evaluate a potential borrower. Their analytics platform takes into account far more critical factors than do traditional loan analytics. This is only possible because we live in times of unprecedented access to data. As a result, Lending Clubs are pushing against the boundaries of traditional lending risk management.
An Analytics Platform That Looks Past FICO
The problem with FICO scores isn’t just that they’re an incomplete metric for loan analytics. Even though they err on the side of caution for credit unions, they limit the kind of good, eligible customers that credit unions should want.
Fortunately, there is a plethora of data available today that will enable “analytic” based lenders to access a market of borrowers with great future potential who don’t have great FICO scores. Lenders who incorporate other relevant information and look beyond traditional loan analytics can increase their loan portfolio while still mitigating risk.
As a personal example, I applied for a car loan after I graduated from college. I was 1,500 miles away from home and walked into the local branch of a bank that my family had banked at for years. Yet, my auto loan application was rejected. The fact that I was a business school graduate with a well-paying job did not make any difference.
A week later, I walked into a credit union and met a loan officer named Louise. Louise not only approved my car loan, but also went on to set up a monthly savings plan. I remember her name to this day because she was able to see beyond the FICO score and used other data to assess my future potential as a reliable borrower.
In 2018, Lending Clubs are using Big Data and loan analytics to make fact-based assessments to determine loan risk, much like Louise did.
Moving Forward with New Loan Analytics Strategies
Both credit unions and potential borrowers can benefit from better loan analytics information. The fact that brand new drivers with little-to-no road experience reap the rewards from data correlation, but that adults with stable jobs don’t is alarming.
Without updating the old-fashioned loan analytics platform, credit unions will miss profitable loan opportunities. Just as importantly, by not offering loans to people with low FICO scores but who are otherwise qualified, credit unions can’t provide critical services to their best members.
Credit unions live in an unprecedented time. Never before have they been able to access this much data about their members both internally (data within the credit union) and externally (data outside the credit union). It is time for this industry to “think differently,” and look beyond FICO and LTV to embrace the upside potential of Big Data and loan analytics for improving lending practices.