Artificial intelligence has officially moved from experimentation to execution in lending departments across banks and credit unions. As we enter 2026, financial institutions are no longer asking whether to adopt AI, they’re working to implement it strategically across consumer, commercial, and mortgage lending operations. According to a recent Celent study commissioned by Zest AI, 83% of lenders plan to increase their generative AI budgets in 2026, with 41% anticipating increases exceeding 5%. More significantly, two-thirds of lenders have already completed or will implement GenAI strategies by 2026, a faster adoption rate than previous lending technology innovations.
This quarterly series focuses on practical, real-world AI applications in financial services. For our first edition, we’re examining lending, a department where AI is delivering measurable impact today.
Where AI Is Making an Impact
1. Automated Underwriting and Credit Decisioning
AI-powered underwriting systems are transforming how lenders evaluate creditworthiness. Unlike traditional models that rely on fewer than 20 data points, modern AI platforms analyze hundreds or thousands of variables to create more accurate risk assessments.
Examples of Tools in Action:
- Zest AI: Credit unions and banks are using Zest AI to enhance credit decisioning with machine learning models trained on alternative data. The tool analyzes payment patterns, cash flow trends, and longer credit histories to approve 25% more loans at lower interest rates without increasing default risk. VyStar Credit Union, an early adopter, reports that over 60% of loans using AI can be instantly approved, compared to about 30% with traditional digital lending.
- Upstart: This fintech partners with banks to originate and service personal loans, auto loans, and credit cards using AI models that evaluate 1,000+ data points. One bank added Upstart to its loan policy to grow its personal loan portfolio, noting differences in underwriting parameters because Upstart’s AI looks at far more variables than traditional methods.
- HomeVision (MIRA): In mortgage lending, Newrez invested in HomeVision to develop an industry-first, AI-powered mortgage underwriting platform. The MIRA system uses advanced machine intelligence to read loan documents and make decisions across collateral, income, assets, and credit, doubling operational efficiency in collateral underwriting. The technology is expected to roll out in 2026.
Why It Works: AI underwriting uses supervised machine learning to identify correlations across massive datasets that traditional rule-based systems miss. The models provide real-time financial analysis, integrate directly with credit bureaus and banking APIs, and deliver explainable credit decisions required for regulatory compliance.
2. Document Processing and Automation
Manual document review, such as extracting data from tax returns, bank statements, and pay stubs, has historically been one of the most time-consuming aspects of lending. AI-powered business lending software now uses optical character recognition (OCR) and document intelligence to automatically extract, validate, and organize data.
Example Tools In Action:
- Ocrolus: Ocrolus ingests unstructured documents (e.g., bank statements, pay stubs, W‑2s, tax forms), converts them into structured, regulatory‑grade data, and feeds results into the loan origination system, combining AI extraction with human‑in‑the‑loop validation to boost accuracy and catch potential fraud.
Outcomes include Better Mortgage processing over 95% of its documents through Ocrolus to scale underwriting, and an Entech client seeing ~60% faster per‑application document processing after integrating Ocrolus for income verification. - Additionally, Google Cloud’s Document AI for Lending offers pretrained models for mortgage/lending documents (tax statements, asset docs), accelerating data capture while supporting compliance controls (e.g., data residency, customer‑managed encryption keys).
Why It Works: Faster loan processing, fewer data entry errors, lower operational costs, and better consistency across underwriting teams. This automation allows loan officers to focus on complex cases rather than routine data entry.
3. Customer Service and Support
AI chatbots and virtual assistants provide 24/7 customer support, handling routine inquiries and guiding members through processes without human intervention. UnitedFCU, for instance, implemented Finn’s AI chatbot in its online banking and mobile app; the bot handles 80% of customer call drivers identified as “basic service requests,” reducing the need for live agent interaction.
Challenges and Risks: What Lenders Must Navigate
While AI offers significant benefits, financial institutions face substantial challenges in implementation and ongoing management:
Regulatory Compliance & Explainability
The Consumer Financial Protection Bureau (CFPB) has made clear that there is “no AI exception” to consumer protection laws. When AI denies a loan, lenders must provide specific, accurate reasons that comply with the Equal Credit Opportunity Act (ECOA) and provide an Adverse Action Notice.
Compliance Requirement: Lenders must implement Explainable AI (XAI) layers that generate legally justified reasons for credit decisions, such as “denial due to account balance volatility (a drop of more than 40% over the last 30 days)”.
Fair Lending & Bias Prevention
AI models must be absolutely blind to protected characteristics (race, gender, age, marital status) and cannot use “proxy” variables that indirectly correlate with these characteristics. For example, if an AI uses smartphone model, geolocation, or purchase history, lenders must prove these aren’t proxies for racial or social characteristics.
The CFPB now requires dynamic monitoring, continuous testing to ensure models don’t “drift” and begin issuing discriminatory decisions. Static audits at launch are no longer sufficient.
Data Quality & Security
AI systems require high-quality, accurate data and strong security measures to protect sensitive customer information. Poor data quality undermines model accuracy, while data breaches carry both financial and reputational costs.
Vendor Responsibility
Financial institutions bear the responsibility for their AI agent’s mistakes, even if errors result from external model updates by providers like OpenAI or Anthropic. This “non-delegable responsibility” means lenders cannot blame the base model provider.
Integration with Legacy Systems
Many institutions struggle to integrate AI tools with existing core banking platforms, loan origination systems, and data warehouses. Seamless integration is critical for AI to access the data it needs and deliver value.
Practical Guidance: Implementing AI in Your Lending Department
For financial institutions considering AI adoption, industry experts and early adopters recommend the following approach:
1. Start with High-ROI, Lower-Risk Use Cases
Begin with AI applications that deliver clear return on investment and have faster deployment timelines, such as fraud detection, automated member support through chatbots, or document processing. These use cases allow institutions to build confidence and infrastructure before tackling more complex applications like full underwriting automation.
2. Prioritize Data Governance
Ensure data quality, centralization, and security before deploying AI. Your AI depends on good data. Implement strong data management practices covering data quality, data security, and seamless integration with existing sources.
3. Build Explainability from Day One
Don’t treat explainability as an afterthought. Implement Explainable AI (XAI) architecture from the start to ensure you can provide specific reasons for every credit decision.
4. Establish Cross-Functional Teams
Safe AI adoption requires collaboration among model, technology, legal, compliance, and risk management teams. Create cross-functional governance structures to oversee AI implementation and ongoing monitoring by establishing AI Committee’s or task forces.
5. Choose Experienced Vendors
Work with partners who have deep experience with financial institutions and understand the regulatory environment. Look for vendors who can support implementation, training, and ongoing maintenance, and who provide supervised machine learning models with explainability built in.
6. Implement Continuous Monitoring, Not One-Time Audits
Regulators are beginning to expect continuous auditing to detect model drift. Implement parallel monitoring systems that run control test groups through your AI models regularly to ensure fair and consistent decisions across all population segments.
7. Test Models Through Credit Cycles
Since most AI models were developed during benign economic conditions, institutions should closely monitor how models perform during changing economic times, including rising delinquency rates and inflation.
8. Train Existing Staff
Rather than hiring entirely new teams, focus on training existing staff to expertly utilize AI tools. Loan officers remain vital for reviewing complex cases, higher-risk loans, and exceptions. AI is a tool that supports decision-making, not a replacement for human judgment.
Looking Ahead: The Future of AI in Lending
As we move through 2026, the conversation has shifted from experimentation to practical, measurable transformation. The institutions that thrive will be those that start with targeted projects, build strong data foundations, prioritize explainability and compliance, and continuously monitor their AI systems across economic cycles. AI in lending is no longer a competitive advantage, it’s rapidly becoming the competitive baseline.
If you have any questions or are interested in learning more, we are here to help. Please do not hesitate to reach out to discuss your specific situation.
This material has been prepared for general, informational purposes only and is not intended to provide, and should not be relied on for, tax, legal or accounting advice. Should you require any such advice, please contact us directly. The information contained herein does not create, and your review or use of the information does not constitute, an accountant-client relationship.
