Automated vs. Manual Underwriting: Impacts Credit Scores

The Consumer Financial Protection Bureau (CFPB) and five federal agencies began investigating how financial institutions use artificial intelligence (AI) in risk evaluations for loan approvals. This inquiry ties into previous discussions on the various credit scores and how credit reporting errors and creditor data affect automated decision-making.

The Impact on Consumers

How does AI’s use in credit evaluations affect consumers? The accuracy of creditor data is crucial, as errors can significantly influence loan approval outcomes. We’ll explore this through the experience of a borrower named Sam, illustrating the consequences of incorrect data on credit reports.

Sam’s Story: A Case Study

Sam, after years of avoiding debt repayment, decided to rehabilitate his loan with the U.S. Department of Education. Despite completing a rehabilitation program and improving his financial habits, Sam encountered obstacles when applying for an auto loan due to discrepancies in his credit report.

The Credit Reporting Dilemma

Sam’s credit report displayed two adverse accounts related to his student loan, impacting his credit score negatively. This situation raises questions about the fairness and accuracy of credit reporting and the role of duplicate account reporting.

Automated Underwriting and AI Scrutiny

The use of AI in automated underwriting processes is under review. The efficiency of AI in predicting consumer creditworthiness based on provided data is critical, yet the system’s fairness is in question when inaccuracies in credit reports are not addressed.

Challenges in Correcting Credit Report Errors

Correcting errors on credit reports is vital for fair credit evaluation. However, the process is complex, and the governing rules by credit bureaus and the Department of Education sometimes fail to rectify issues adequately.

The Role of Manual Underwriting

Manual underwriting becomes necessary when automated systems fail to accurately assess a borrower’s creditworthiness. It allows for a more detailed examination of factors beyond the credit score, such as debt-to-income ratio and employment stability.

Sam’s Denied Auto Loan

Despite efforts to rectify his credit report, Sam was denied an auto loan. This highlights the limitations of automated underwriting systems and the importance of accurate data reporting by creditors.

Conclusion: The Need for Fairness and Transparency

The adoption of AI and automated underwriting processes has potential benefits but also poses challenges, especially when inaccuracies in credit reporting affect consumer outcomes. Ensuring fairness and equality in credit evaluations is essential to prevent financial harm to consumers.