A recent financial institution survey by LendIt Fintech and Brighterion (Mastercard) showed high lender interest in the expansion of credit models to deal with the rapidly changing economy. Today, there is more data available and new methods of processing credit decisions that can improve a lender's credit underwriting and risk-based pricing models.
This blog will be focused on cosigned, private student loans: current challenges and underwriting opportunities.
Credit bureaus are removing bad debts
Credit Bureaus are increasingly under pressure from the Biden Administration and the CFPB to remove bad debt accounts that negatively impact a person's credit history. By removing bad debts, credit reports will appear better than they are while credit scores will become inflated.
At worse, this will lead to loan approvals that would have been denied if the bad debts were not removed. At best, approved loans will become underpriced. Both underwriting and loan pricing models will be compromised.
On March 21, 2022, the Wall Street Journal reported that medical debts will no longer appear on credit reports later this year. On April 13, 2022, the U.S. Department of Education stated that more than 7 million borrowers who defaulted on their federal student loans will have their default removed from their credit report.
This puts traditional credit models on shakier ground in an increasingly uncertain economic environment.
Other data sources for student borrowers
What can be done? Currently, traditional credit models take minimal data into account of the student borrower applicant. The focus is on the parent's employment income and track record of paying debts on-time.
Transcript data provides information to determine which student applicants are at risk of dropping out of college. However, transcript data is not standardized, which makes it difficult to manually underwrite with a human person. Requiring an applicant to upload the transcript increases the operational burden and slows turnaround time on credit decisions in a competitive lending environment.
Other relevant data includes total student loan debt and projected starting salary that are reliable.
Underwriting student borrowers at scale
A lender might be tempted to run manual processes to incorporate new data sources into its traditional credit decisioning platform. This would be a mistake given the complexities of the data, and the massive amount of data that would need to be processed. The operational challenges would cascade to underwriting errors, suboptimal staffing levels and extended application processing times.
Einstein Higher Edu Solutions has created the Einstein Risk Score that brings scale, consistency and reliability to underwriting student borrowers. The cloud-based Einstein Risk Scoring service brings together all of the elements needed to underwrite student loan borrowers:
👍Third party data integrations
👍Automation of data input processing
👍Instant credit decisioning
👍Loan origination platform integration with Einstein
