HOW TRENDED DATA CREDIT REPORTS IMPROVE LOAN ORIGINATION DECISIONS

Modern technology surrounding the collection and analysis of data has made possible the ability for businesses to make decisions based on data. These days, collecting data from retail purchases, online web browsing, manufacturing processes, and just about any other activity that generates high volumes of data is easy. When properly analyzed, this “big data” can be utilized to create insights that help lend clarity as to why certain events occur. These insights can then be applied to improve products, services, and processes. This analysis is most often done in conjunction with machine learning algorithms, which help stakeholders identify trends that would otherwise go undetected.

In the lending industry, analysis of this data reveals trends that describe the past in order to help predict the future. Using this trended data, credit reports that traditionally were used to develop credit scores can now offer a means to better understand seasonal cycles, outliers in the data, an applicant’s creditworthiness, and other useful information. In fact, traditional credit bureaus such as TransUnion now offer trended credit data reports. In addition to their universally-used credit scores, these now include up to 30 months of credit card data. These trended credit data reports show an applicant’s credit limits, balances, minimum payments due, actual payments, late payments, and amounts past due for each of the months reported. Together with an applicant’s credit score, a trended credit data report paints a more accurate picture of a borrower’s financial position by detailing their spending habits and ability to repay a loan.

How the Use of Big Data Enables Trended Credit Data Reports

The information collected throughout the end-to-end loan process is what enables lenders to best utilize trended credit data. Reports rely on the amount, speed, variety, and reliability of this big data to make their loan processes more efficient and reliable. The use of this data enhances the ability of lenders to develop insights by automating processes that then support better decision-making.

This big data can be either structured or unstructured. While the use of big data can certainly add to the value of trended credit data reports, it also creates challenges as well, especially when it comes to unstructured data.

Predefined and formatted information that has a set arrangement is known as structured data, which is put into precise fields within a database. When it comes to using trended credit data, reports on applicants tend to focus on subsets of this structured data. This often includes demographic information about the applicant, such as age and where they live, along with information on late payments, the existence of other loans, and other sanctioned data. In fact, much of this type of structured transactional data goes unused, though according to a 2019 study using payment data would allow lenders to detect twice as many defaulters among their riskiest loan applicants.

Unstructured data is more ambiguous. It includes audio from customer interactions, PDF documents that include regulatory documents and loan applications, email communications, and a host of other relevant documents like pay slips or mortgages. This type of data is typically underutilized, but lenders can use it to address gaps in marketing or processes, customer preferences, unmet needs, and other valuable information.

While automating the analysis of structured data is already successfully being done, because of its complexity and irregularity, analyzing unstructured data still often requires humans. But with this unstructured trended credit data, reports on credit can better take into account applicants’ financial behaviors.

Trended Credit Data Reports Reveal Financial Behaviors

Revealing applicant behavior in regards to payments is invaluable to lenders. This is why an applicant with an excellent traditional credit rating will often receive an automatic loan approval at the best possible terms, while one with a very poor traditional rating will be automatically declined. Good scores often undergo a review by an underwriter who will help review an appropriate loan structure for an applicant.

It’s with subprime candidates and the higher interest rates they pay that a lender can perhaps best use this trended data to minimize risk to the lender while also increasing the strength of their portfolios. Basing decisions only on credit scores still means lenders will assume some hidden risk while also missing out on lending opportunities. Trended credit data reports improve loan origination decisions by providing detailed insight into applicants’ financial behaviors. 

Let’s look at three potential situations where trended credit data can add value for lenders during the origination process.

We will look at:

1. An excellent score
2. A good score
3. A poor score

Excellent Score, On the Decline

Take, for example, a trended credit data report on an applicant with an excellent traditional credit rating. The report indicates that during the past 18 months, the monthly balance on one line of credit increased an average of $540 per month, while the borrower has only been making the minimum payment each month. Another line of credit shows a similar trend, though with a less steep monthly increase. Together, these payment trends contrast starkly with the applicant’s payments in the 12 months prior, when the balances were paid in full each month.

Conclusion: The applicant is facing some financial headwinds and may present more risk than the credit score alone indicates.

Good Score, Staying the Course

A trended credit data report for an applicant with a good credit score shows consistency in credit card usage and payments over the past 24 months. With the except
ion of two late payments in the previous year, it shows monthly balances were paid in full.

 
Conclusion: The risk associated with the applicant’s credit score is confirmed with trended credit data. Reports match the applicant to the lender’s specific credit policies in order to offer a competitive deal.

Not as Poor as the Score

A trended credit data report for an applicant with a poor credit score shows the applicant’s financial position as quickly improving. The most recent 18 months of payment data indicate the applicant opened a new credit card through a branded airline. Monthly balances have been increasing by an average of $175, and the cardholder pays the required amount in full each month.

Conclusion: The applicant is a much better risk than indicated by the credit score. A lender can offer better terms than the credit score alone would dictate.

Explore a few additional examples of the value and benefits of trended credit data reports by watching the How to Use Trended Data in Your Loan Approval Process webinar now.

Trended Credit Data Reports Reduce Risk and Increase Opportunity

By combining information from a traditional credit score with a trended credit data report, lenders gain a better understanding of an applicant’s current financial behavior. Trended credit data reports can confirm whether an applicant’s credit score is accurate or if there’s a discrepancy between the credit score and their current financial standing.

In all cases, trended credit data reports reduce the risk inherent in making lending decisions, allowing lenders to structure deals more closely with the actual risk an applicant presents. For applicants with poor credit scores, trended credit data reports can increase lending opportunities by revealing an applicant’s improving financial position, letting lenders extend credit that would have otherwise been denied based solely on a credit score.