A practical guide to explainability in lending

Picture this: A bank once skeptical of AI in lending hesitated to move beyond traditional underwriting models. Concerns over regulatory scrutiny, bias, and opaque decision-making held them back. 

However, after facing increasing pressure from regulators and market competition, the bank implemented an explainable AI framework into their underwriting process. 

The result? Improved approval rates, enhanced compliance, and greater customer trust.

The stakes in AI-driven lending have never been higher. The 2024 Consumer Financial Protection Bureau report highlighted patterns of bias in automated credit decisions, reinforcing the urgency of explainability. As AI adoption accelerates in financial services, the pressure is increasing for institutions to prioritize transparency—not just as a compliance necessity, but as a strategic advantage.

This article explores the hidden risks of black-box AI, the role of explainability in fair lending, and a roadmap for successful implementation.

The Hidden Costs of "Black Box" AI in Lending

Black-box AI models create significant challenges for financial institutions that extend beyond regulatory risks. The lack of explainability in lending decisions can be damaging to both brand and business, eroding trust as customers and regulators increasingly demand concrete reasons for lending decisions. In some cases, this can lead to the obvious threat of customers turning towards more transparent competitors. 

The U.S. Department of Treasury’s report in December 2024 warns that AI models without explainability built into them risk reinforcing historical biases in decisions, creating the potential for heightened regulatory penalties and reputational damage. 

Additionally, bias in AI-driven lending can result in mispriced risk, poor loan performance, and the exclusion of qualified applicants – missed optimal lending opportunities.

Breaking Down AI Explainability 

Explainable AI ensures that AI models used in credit risk assessment and cash flow underwriting are understandable, fair, and justifiable. While lenders ultimately make the lending decisions, AI-powered credit risk models must provide clear insights into how credit risk is assessed and why certain borrowers may pose higher or lower risks. Transparent, explainable AI models can help lenders document how each factor influences credit risk evaluations, enabling them to make informed and defensible lending decisions.

AI-powered underwriting can incorporate alternative data sources—such as rental history and utility payments—to expand credit access, but these factors must be clearly explainable to both regulators and consumers. Explainability ensures that lenders can justify the use of alternative data and demonstrate that their risk models do not inadvertently introduce bias.

Real-time API integration plays a crucial role in making explainability scalable. By embedding modern explainable AI solutions into existing workflows, lenders can gain deeper insights into credit risk while maintaining operational efficiency.

A Hypothetical Scenario: Innovation Bank and the Case of Unintended Bias 

Imagine a mid-sized lender called Innovation Bank. They roll out an AI-powered credit risk model with high hopes for efficiency and fairness. But soon, they realize something’s off—certain borrowers are being disproportionately denied, and they can’t immediately pinpoint why. 

The bank discovered that its algorithm disproportionately weighed zip codes, inadvertently disadvantaging borrowers from certain areas. 

By implementing explainable AI tools, the institution was able to identify and correct these biases – Innovation Bank was able to trace the issue back to an over-reliance on zip codes, which unintentionally penalized applicants from specific neighborhoods.

After adjusting the model, they not only corrected the bias but also improved approval rates without increasing their credit risk exposure. 

This is just one example that demonstrates how AI explainability in credit risk assessment and cash flow underwriting can play a vital role in ensuring fairness, meeting regulatory requirements, and equipping lenders with the insights needed to make more equitable and informed decisions.

Alternative Data and Explainability

Alternative data is reshaping credit risk assessment and lending decisions, providing new opportunities for financial inclusion. However, its effectiveness hinges on explainability—lenders must be able to clearly justify how these data points influence risk assessments and credit decisions. 

With the rise of open banking, lenders now have access to more comprehensive financial data, enabling a clearer picture of a borrower’s creditworthiness. Rental history, utility payments, and cash flow data can provide a more nuanced view of a borrower’s financial stability, helping lenders assess risk beyond traditional credit scores. However, lenders must ensure that these data sources are used transparently, providing clear documentation on how they influence credit risk assessments. 

Explainable AI tools can help lenders document how alternative data factors into credit risk, ensuring that AI-powered credit risk assessments remain transparent and defensible. Borrowers and regulators alike need to understand how data is being used in credit risk assessments and decision-making, and explainability in AI frameworks can foster clearer communication, increased confidence and greater trust and acceptance in the use of AI in lending. 

Challenges Lenders May Face with Adopting Explainable AI

For lenders—including banks, credit unions, and fintechs—adopting explainable AI means addressing several institutional barriers. New AI regulations require that credit decisions be well-documented and auditable, which can be challenging for lenders that lack the necessary expertise or resources.

Competitive pressures also push financial institutions of all types to modernize, but many lenders face uncertainty about how to effectively adopt explainable AI while maintaining business efficiency. 

Implementing explainable AI frameworks into existing lending processes can require significant investment in technology, data governance, and compliance processes. It requires a shift in strategy, investment in the right tools, and a clear framework for implementation

Additionally, many financial institutions may struggle with the need for specialized expertise not currently available in-house to ensure seamless adoption. 

Without proper implementation, AI-powered automation in credit risk assessments and underwriting can create new operational burdens rather than streamlining workflows.

Another key challenge is staff adoption. Employees must be trained to understand AI-powered insights, ensuring they can fully leverage and interpret lending outcomes for stakeholders. Without proper training, even the most advanced applications of AI can face resistance from internal teams.

The Implementation Roadmap for Explainable AI in Lending

Successfully adopting explainable AI requires a clear, structured approach. This can help to ensure that the implementation of new, modern technology into legacy infrastructures is less disruptive and not only cost-effective and compliant, but also efficient and effective. 

Generally, when planning to make explainable AI a seamless, integrated part of their credit risk assessment and underwriting processes, lenders might start with these 4 considerations: 

• API Integration: AI models must integrate smoothly with existing workflows to provide real-time, explainable insights. Seamless API integration ensures that transparency is embedded at the required stages of the underwriting process without disrupting operations.
• Maintaining Explainability at Scale: As AI models evolve, lenders will need to consider how to proactively ensure transparency remains intact. This might involve continuous monitoring and bias audits to detect and correct unintended biases before they impact lending decisions, in addition to strong model governance frameworks to track and audit any changes. 
• Staff Training and Adoption: Employees will need to be equipped to interpret and communicate AI-powered insights effectively. Training programs could focus on understanding AI models, addressing customer concerns, and ensuring compliance with regulatory standards.
• Measuring Success: Clear KPIs will aid in tracking the impact of explainable AI. Lenders might look at improvements in approval accuracy, reductions in manual reviews, regulatory compliance adherence, and increased customer trust to gauge the effectiveness of their explainability efforts. 

Key Takeaways

As AI continues to reshape lending, financial institutions must prioritize explainability to remain compliant, competitive, and trusted by customers. The risks of opaque, black-box models are too great—regulatory scrutiny is increasing, and borrowers demand greater transparency in credit decisions. By adopting explainable AI, lenders can ensure fairness, improve decision-making, and future-proof their operations.

Key reasons why explainability in AI-powered lending is essential:

• Strengthening compliance: With regulators focusing on AI bias and fairness, explainability ensures credit decisions are transparent, well-documented, and audit-ready.
• Enhancing trust: When borrowers and regulators understand how lending decisions are made, financial institutions can build stronger relationships and improve customer retention.
• Optimizing risk management: Explainable AI allows lenders to make more accurate credit assessments, reduce bias, and refine risk models for better portfolio performance.

Institutions that embrace explainability now will lead the industry in fairness, efficiency, and innovation—positioning themselves for long-term success in an increasingly AI-driven world.

Carrington Labs makes it effortless for lenders to integrate explainable AI-powered credit risk assessments into legacy systems through flexible, real-time API solutions. Our technology ensures compliance, enhances transparency, and streamlines decision-making without disrupting existing workflows. Contact us today to learn how we can help modernize your lending strategy.