AI can give digital lenders an advantage not only by automating application steps, but by helping them make and update decisions across screening, scoring, pricing, fraud detection, document review and servicing. That advantage is conditional: it depends on reliable data, sound lending expertise and effective controls. Evidence so far ranges from a study of Italian banks to supervisory observations and individual vendor case studies; it does not show that AI makes every lender faster, fairer or more profitable.
How is AI changing digital lending?
AI can be applied at several points in the lending lifecycle. Some uses automate work; others help staff identify patterns, assess information or tailor decisions. Adoption and maturity differ by institution and jurisdiction.
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Screening, scoring and pricing
Models can support borrower pre-screening, application scoring and credit-risk assessment, and can inform pricing or provisioning. The Bank of England’s 2025 assessment says some firms use AI techniques at multiple credit-risk stages, while aggregate use in credit-risk management remained at an early stage in its supervisory intelligence.
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Fraud detection and monitoring
AI systems can look for patterns across transactions or applications and support ongoing monitoring. The European Central Bank (ECB) Banking Supervision reported increased use cases for credit scoring and fraud detection among significant European institutions between 2023 and 2024. In the ECB’s observations, fraud models could support real-time monitoring and pattern recognition; that describes potential capabilities, not a guarantee that a particular model will catch fraud.
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Document analysis and customer service
Generative AI can help extract and organize information from loan documents, while customer-service chatbots can assist with routine interactions. In a 4 April 2025 speech, Federal Reserve Governor Michael S. Barr described document analysis as a potential support for credit underwriting and noted that chatbots were already assisting in banking. More complex tasks—such as helping customers understand options—remain a potential use, not proof of improved lending outcomes.
These tools can complement, rather than simply replace, human judgment. AI can process codifiable information at scale; lenders still need people and established controls to interpret context, check outputs and take responsibility for decisions.
How can AI create a competitive advantage?
The potential edge comes from better decisions and more responsive service, not just fewer manual steps. A lender may use more of the data it already holds, move information between stages more effectively, or adjust an offer to a borrower’s assessed needs. Those benefits are meaningful only if the resulting decisions are reliable and appropriate.
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- Decision quality: Predictive tools may help identify patterns that conventional processes miss. The relevant test is validated lending and risk performance, not whether a model is new or complex.
- Speed and service: Automating document review or routing can reduce waiting and repeated requests for information. A faster answer is useful only if customers receive a clear, sound decision.
- Fraud and portfolio monitoring: Pattern recognition may help flag suspicious activity or changes in risk. Models need monitoring as data and economic conditions change.
- More tailored offers: Scoring and analytics can help lenders shape offers around a borrower’s circumstances. Personalization does not by itself establish that credit access has widened fairly.
- Reusable data capabilities: Extracted information, audit trails and integrations can support several workflows, rather than solving one isolated processing task.
McKinsey’s analysis of AI and core banking frames the shift as a move from transaction-processing systems toward systems that continually optimize decisions across areas such as credit, pricing, servicing, fraud and risk. It offers illustrative potential impacts—including 5–15% more approved lending volume with similar risk profiles, 20–40% less manual processing effort and 10–20% lower fraud losses. These are industry-analysis ranges, not universal measured outcomes or promises for an individual lender.
What do the reported results actually show?
There is evidence of useful applications, but it comes from different kinds of sources and should not be treated as one comparable body of proof. Supervisory observations describe reported use and practice; vendor case studies report results from specific engagements; the Italian-bank study examines lending outcomes in a particular setting.
A bank transformation case: delivery and customer journey
Deloitte’s case study describes a large European bank’s cloud-based lending transformation, aimed at improving the customer journey and positioning the bank in a broader ecosystem of services for small and medium-sized businesses.
- The bank reached first commercial use of a minimum viable product in 13 weeks.
- In the case, customers could receive clarity and a committed offer in 15 minutes, compared with weeks previously.
- The bank built an automated application process for a new COVID-related loan product in under three weeks.
Deloitte does not establish that AI alone produced these outcomes. The case also involved a cloud lending system, later API-based integrations and new products, so the reported speed reflects a broader technology and operating-model transformation.
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EY’s case study describes a retrieval-augmented generation system that processed approximately 11,000 documents across 145 commercial loan facilities for 13 regulatory reporting elements. It extracted information, applied domain-specific rules and recorded audit trails and rationales. The process used iterative validation against manually checked data.
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- EY reported approximately 85% overall accuracy, up from 65% against manually validated data in the engagement.
- Review time fell from six to eight hours to 15 to 30 minutes in the described engagement.
These are vendor-reported results from one case, not an independently established benchmark for commercial lending as a whole. The example is relevant because it shows how document extraction can combine speed with traceable outputs and validation, rather than treating automation as the only objective.
Supervisory observations in Europe
The ECB’s 2025 supervisory material drew on data covering 107 significant institutions in 2023 and 110 in 2024. Its accompanying workshops involved 13 banks, and the ECB explicitly cautions that those workshop takeaways are based on a small sample and should not be generalized to the full sector. Banks in that sample reported benefits for process efficiency and customer service, as well as potential for predictive analytics and tailored offers.
Productivity estimates are not lending results
The Bank of England’s 2025 publication relays an external study’s estimate of up to 30% productivity gains over 15 years across banking, insurance and capital markets. This is neither a Bank of England forecast nor a lending-specific measured result. It should not be read as a prediction of what an individual lender will achieve.
Can AI help banks lend to more people?
It may help lenders assess information more efficiently or identify borrowers whose circumstances are not well captured by simpler processes. But extending access is not an automatic consequence of using AI. Broader approvals can also bring different risks, and a model trained on biased or incomplete data can reproduce or amplify unfair patterns.
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The clearest empirical evidence in the supplied studies is specific to Italian banks. BIS Working Paper 1244 examines banks’ investment in AI for credit scoring using credit-register data around the COVID crisis. In normal times, the study found that AI use in screening and monitoring helped mitigate some rent extraction associated with relationship lending. The Banca d’Italia summary says that, for equivalent relationship durations, AI-using banks provided more credit at lower interest rates in normal times. That result did not hold during the COVID crisis: AI did not provide additional credit or interest-rate protection.
This finding shows why outcomes should be examined under different conditions. It is evidence from a specific study, country and period—not a universal causal promise that AI will increase lending or lower rates in every market. Whether a system expands access responsibly depends on its data, design, validation and ongoing performance.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What are the risks of AI in lending?
AI can introduce or amplify risks within decisions that affect people’s access to credit. Barr’s 2025 speech identifies immature technology, hallucinated or inconsistent answers, information-security threats and possible exposure of customer or proprietary information. Other material concerns include model risk, explainability, data quality, privacy and oversight of third parties.
- Bias and unequal outcomes: Historical data or proxy variables may disadvantage groups of applicants. Examine outcomes across relevant populations, not just overall accuracy.
- Model errors and drift: A system that performs well on past data may fail when borrower behavior or economic conditions change. Monitoring should continue after deployment.
- Weak explanations or auditability: Lenders need to understand the basis for outputs and retain enough information to review decisions and resolve disputes.
- Security and privacy: Sensitive financial information can be exposed through insecure systems, poor data handling or inappropriate access to third-party tools.
- Dependence on providers: Outsourcing a model or platform does not remove the lender’s need to understand its risks and oversee its use.
The U.S. Government Accountability Office’s 2025 report discusses potential financial-services benefits alongside lending bias and cybersecurity risks. It says U.S. federal financial regulators primarily rely on existing laws, regulations, guidance and risk-based examinations, with some AI-specific guidance and examinations. The report also identified then-current limitations in the National Credit Union Administration’s model-risk guidance and its authority over technology service providers used by credit unions. These are U.S.-specific observations from that report, not a statement of the law in every jurisdiction.
What should a lender evaluate before adopting AI?
Start with the lending problem and the outcome customers and the institution need—not with a model or an automation target. The following checks translate the evidence into a practical decision framework; they are not a complete compliance checklist.
- Define the decision and measure the baseline. Specify which stage AI would support—such as document extraction, fraud monitoring or application scoring—and record current decision quality, processing time, risk outcomes and customer experience.
- Check data suitability. Confirm that the data is accurate, representative, appropriately governed and usable for the proposed purpose. Include a plan to detect quality problems and changes over time.
- Validate the outcome that matters. Test predictive performance, consistency, risk and customer effects against a meaningful baseline. Examine results across relevant applicant groups and under different economic conditions.
- Make decisions reviewable. Determine whether staff can understand, challenge and document outputs, and whether data lineage, rationales and audit trails are available where needed.
- Plan for integration and operations. Check how the system connects to existing lending workflows, data sources and customer channels. The Deloitte case illustrates that architecture and APIs can be as important as the AI component in a digital transformation.
- Assign ongoing ownership. Establish who monitors model performance, handles exceptions, responds to security incidents and oversees external providers. The ECB observed banks in its sample adopting AI policies or committees, mapping high-risk use cases, maintaining feedback loops, checking data quality and strengthening oversight; these are observed practices, not a universal prescribed checklist.
A well-designed evaluation compares an AI-supported process with the lender’s actual alternative across decision quality, service speed, resilience, explainability, data architecture and customer outcomes. If a system saves time but weakens accountability or performs poorly under stress, the operational gain may not amount to a durable competitive edge.
When does AI become a durable advantage?
AI is most likely to matter when it improves a lender’s ability to make reliable decisions and serve customers across connected workflows—not merely when it reduces manual tasks. The available evidence supports specific applications and promising case results, but it also shows that effects depend on context and that reported vendor outcomes should not be generalized. A durable advantage therefore comes from combining useful models with strong data, lending expertise, integrated systems and governance that continues after launch.
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