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The defi Team Automation, defi INSIGHT

credit risk management automation

For many lenders, credit risk management depends on years of experience. Their credit risk expertise is a mix of shrewd loan portfolio analysis and an innate intuition about borrower risk. While that may have worked in the past, credit risk management is shifting from human judgment to automated, data-driven lending decisions that assess credit risk far more accurately.

Credit Risk Management Automation 

Three fintech advancements—fraud detection, trended credit data, and alternative credit data—are accessible via integration with modern, cloud-based lending solutions. These features automatically and consistently assess borrower risk and drive the loan origination process. Credit risk management automation accelerates loan decisioning, lets lenders focus on applications likely to become deals, and lets them price for risk more accurately. 

Fraud Detection Eliminates High-Risk Applicants

Unscrupulous individuals and fraud rings use a variety of underhanded methods to improve their chances of obtaining credit, better terms, or committing outright theft. They use identity theft, income and employment misinformation, and synthetic identities on loan applications, and hope the lender misses any subtle clues that indicate fraud. If fraudsters get the loans, they typically make the first few payments and then default.

Fraud detection is tough when you’re processing high volumes of loan applications and relying on underwriters to review applications. However, fraud detection services can now review loan application data automatically and analyze it for inconsistencies that may indicate fraud. The analysis is based on machine learning algorithms applied to tens of millions of loan applications—legitimate and illegitimate—to identify fraud patterns.

When the analysis detects fraud, it indicates the fraud type and also assigns a score that a lender can use to determine the next steps. A high score could automatically receive an auto-decline, a mid-range score could be sent to an underwriter for further review, and very low scores can move immediately into an approval queue. Fraud detection lets lenders eliminate risk before it has a chance to damage portfolio performance.

Trended Credit Data Provides a Better Picture of Financial Strength

Credit scores alone don’t always paint an accurate picture of a borrower’s changing financial strength. Trended credit data gives lenders a far more accurate understanding of an applicant’s ability to pay. Up to 30 months of recent credit card payment data can automatically be accessed and analyzed, showing if an applicant’s financial situation is stable, improving or declining. An applicant with a prime score who shows an increasing unpaid monthly balance for the past 2 months may be a slightly greater risk than credit score alone indicates. A subprime applicant who shows increasing monthly balances that have been paid in full for the past 18 months merits better terms than credit score alone would indicate.    

Alternative Credit Data Goes Beyond Bureau Scores

It’s difficult to assess the creditworthiness of applicants with thin or non-existent credit histories. An applicant could be high-risk or high-value, but you would only know for sure if you had a better estimate of their financial position. Alternative credit data solves that problem with non-traditional financial information such as payment histories for rentals, cell phones, utilities, bank accounts, real estate, and residences. A recent college graduate with thin credit may nonetheless have a spotless record for rental, utility, and cell phone payments for the past 18 months, making her a good risk. Whereas another applicant with numerous changes of address and a history of late rental payments is clearly a client to avoid.

Cloud-Based Integration for Credit Risk Management

Modern, cloud-based loan origination solutions enable credit risk management automation via pre-integrated calls to these services. Using configuration menus, a lending professional can specify where in the loan origination workflow an automated call should be applied, such as:

  • Fraud detection to identify applications likely to result in defaults;
  • Trended credit data to obtain a more accurate assessment of an applicant’s financial strength; and
  • Alternative credit data to avoid risky applicants and identify opportunities that may otherwise have been declined.

Business rules can then be applied to the results of these calls to determine next steps in the lending process. Credit risk management automation lets lenders assess loan applications for risk, decline high-risk applicants, and obtain a more accurate and current picture of applicants’ financial strength to price according to risk.


Getting Started

defi SOLUTIONS loan origination software experts welcome the opportunity to show you how to employ automation to manage credit risk more efficiently. Take the first step toward automated risk reduction and new lending opportunities by contacting our team today or registering for a demo of defi LOS.


Get in touch with us today and get a demo!


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