When one Arizona credit union sought to personalize their loan experience and grow membership, it began by exploring artificial intelligence (AI). The goal: improve the accuracy of credit decisioning to automate the credit worthiness of 70%-80% of their consumer applicants. Through AI-powered credit scoring, they quickly improved their credit decisioning and were able to lend to a target market further down the credit spectrum—without compromising profitability.
For regional banks across the United States, AI-powered credit scoring potentially represents more than just technological advancement—it’s a strategic opportunity to expand their customer base while making more-informed, equitable lending decisions. By harnessing the power of machine learning algorithms and alternative data sources, these institutions are discovering new ways to serve previously underbanked populations while maintaining prudent risk management practices.
Leading the alternative data revolution
Traditional credit scoring models have long relied on a narrow set of financial indicators, primarily focusing on payment history, credit utilization and length of credit history. While these metrics provide valuable insights, they often fail to capture the complete financial picture of potential borrowers, particularly those with limited credit histories or non-traditional financial patterns.
AI-powered systems are changing this dynamic by incorporating alternative data sources that provide a more comprehensive view of borrower creditworthiness. Utility payment history, rent payments, telecommunications bills, and even banking transaction patterns can now be analyzed to assess financial responsibility and stability. These data points, previously invisible to traditional scoring models, offer crucial insights into an individual’s financial behavior and reliability.
Perhaps the most significant impact of AI-powered credit scoring lies in its ability to serve previously underbanked populations. Young adults establishing credit, immigrants with limited U.S. financial history and individuals who primarily use cash-based transactions, who’ve traditionally faced barriers to accessing credit, through conventional channels, can now establish their credit worthiness.
Streamlining the underwriting process
The integration of AI into credit assessment doesn’t just expand the pool of eligible borrowers—it also dramatically streamlines the underwriting process. Traditional manual reviews that once took days or weeks can now be completed in minutes, with AI systems processing vast amounts of data to generate comprehensive risk assessments. Machine learning algorithms can simultaneously analyze hundreds of variables, identifying subtle patterns and correlations that human underwriters might overlook. These systems continuously learn and improve, refining their accuracy as they process more applications and observe long-term outcomes.
This efficiency translates into tangible benefits for both banks and customers. Faster approval times improve customer satisfaction and reduce operational costs, while more accurate risk assessment helps banks optimize their loan portfolios and pricing strategies.
Risk management and regulatory considerations
While AI-powered credit scoring offers significant opportunities, it also requires careful attention to risk management and regulatory compliance. Banks need to ensure their AI models comply with fair lending regulations and don’t inadvertently create discriminatory outcomes. Successful implementations involve continuous monitoring of AI model performance, regular bias testing, and transparent documentation of decision-making processes. Leading regional banks are establishing AI governance frameworks that include human oversight, explainability requirements and regular model validation procedures.
Looking ahead
Regional banks adopting AI-powered credit scoring are gaining significant competitive advantage. They can approve qualified borrowers that competitors might reject, make faster decisions and capture market share in underserved segments. This also helps build stronger community relationships by providing financial access to previously excluded populations.
Moreover, the improved accuracy of AI-driven risk assessment enables better loan pricing and portfolio management, potentially leading to improved profitability and reduced default rates. As AI continues to evolve, its impact on credit scoring will likely expand further.
Integration with real-time data sources, enhanced predictive capabilities and advanced risk modeling will create additional opportunities for banks to serve their communities while maintaining sound lending practices.
Ajay Bhandari is SVP, U.S. Banks & Mortgages Sub-Vertical Head at Infosys.
