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AI can give digital lenders an advantage when it helps them make better-informed decisions, detect fraud, tailor offers and use data more effectively—not simply when it removes manual steps. The value depends on reliable data, sound lending judgment and controls that keep models accurate, explainable and resilient.
Evidence so far is mixed in kind and scope: a study of Italian banks found benefits in normal economic conditions that did not carry over to the COVID crisis, while supervisory reports describe evolving uses and vendor case studies report results from individual projects. None establishes that AI makes every lender faster, fairer or more profitable.
How is AI changing digital lending?
AI is being applied at multiple points in the lending lifecycle, though adoption and maturity vary across institutions and jurisdictions. The Bank of England says some firms use AI techniques for credit-risk pre-screening, application scoring, pricing and provisioning, while aggregate use in credit-risk management remained at an early stage in its supervisory intelligence.
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Screening, scoring and pricing
Models can process codifiable information to support screening, estimate credit risk and inform pricing. The point is not to replace lending expertise, but to help assess relevant information consistently and quickly enough to support a decision. Predictive analytics may also help lenders shape offers to a borrower’s circumstances, but any apparent improvement needs to be tested against actual repayment and risk outcomes.
Fraud detection and monitoring
Fraud models can analyze patterns and support real-time monitoring, as described in the ECB’s supervisory observations. Monitoring can also continue after origination, helping lenders identify changes in risk or suspicious activity. Detection is not the same as proof: alerts need appropriate review, and models need to be checked for missed cases and false positives.
Document review and customer service
Generative AI can help analyze documents that are difficult to handle as structured fields. In a 4 April 2025 speech, Federal Reserve Governor Michael S. Barr said, “Gen AI has benefits for document analysis, which could be applied to improve credit underwriting.” He also described banking customer-service chatbots already assisting customers and the potential for models to handle multi-step tasks. Those comments describe possible applications, not evidence that such tools improve lending outcomes in every deployment.
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Where can AI create a competitive advantage?
More informed decisions, not just fewer keystrokes
A lender may gain an edge if it can combine structured and unstructured data to assess applications more accurately, apply expertise consistently and respond to useful information sooner. McKinsey describes a shift from transaction-processing cores toward systems that continuously optimize decisions across credit, pricing, servicing, fraud and risk. This is an operating-model proposition: the advantage depends on the decisions the system improves and whether the lender can validate those improvements.
McKinsey gives illustrative potential ranges of 5–15% more approved lending volumes with similar risk profiles, 20–40% less manual processing effort and 10–20% lower fraud losses. These are industry-analysis estimates, not universal or independently measured results for every lender. Separately, the Bank of England relays an external study’s estimate of up to 30% productivity gains over 15 years across banking, insurance and capital markets; that is neither a Bank of England forecast nor a lending-specific measured result.
Relationship lending and the limits of the evidence
A study of Italian banks using credit-register data around the COVID crisis found that AI use in screening and monitoring helped mitigate some rent extraction associated with relationship lending in normal times. For equivalent relationship durations, AI-using banks provided more credit at lower interest rates in that setting. The result did not hold during the COVID crisis: AI did not provide additional credit or interest-rate protection then. Read this as evidence about Italian banks in the study’s period, not as a promise that AI will improve access or terms in other markets or under stress. See the BIS Working Paper 1244 and the Banca d’Italia summary.
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Reusable data and connected services
AI can turn information extracted from documents into data that supports more than one task, such as underwriting, reporting and later portfolio review. A lender may also benefit when its lending platform connects reliably to other services and systems. Deloitte’s case study describes a cloud-based lending transformation for a large European SME lender, followed by API-based integrations and new products. That architecture and operating model matter to digital competitiveness, but the case does not establish that AI alone caused the reported customer or delivery outcomes.
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Individual case studies illustrate what a particular implementation achieved; they do not establish typical results across the industry. The publication dates were not stated on the reviewed Deloitte and EY pages.
| Case | Reported result | What the result represents |
|---|---|---|
| Deloitte digital lending transformation | First commercial use of a minimum viable product in 13 weeks; a clarity and committed offer in 15 minutes compared with weeks previously; an automated application process for a new COVID-related loan product built in under three weeks. | Results reported for one large European bank’s cloud-based transformation. The timeline and service results are not evidence of an AI-only effect. Deloitte case study |
| EY commercial loan document review | Approximately 11,000 documents across 145 commercial loan facilities and 13 regulatory reporting elements; approximately 85% overall accuracy, up from 65%; review time reduced from six to eight hours to 15 to 30 minutes. | Vendor-reported results from one engagement, compared with manually validated data in that engagement. The system used retrieval-augmented generation, domain rules, iterative validation and audit trails. EY case study |
The distinction matters when evaluating an investment: shorter processing time can be valuable, but it does not by itself show improved credit quality, fairer decisions or better customer outcomes.
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Does AI make loan decisions faster or more accurate?
It can, for some tasks and under some conditions. Document extraction may reduce time spent on repetitive review, while a well-validated scoring model may improve consistency or prediction. But an automated workflow can also move errors through the process more quickly, and a model that performs well on historical data may weaken when applicant behavior or economic conditions change.
Measure speed and quality separately. Useful measures include time from complete application to decision, application completion and service responsiveness, alongside validated predictive performance, repayment outcomes, fraud losses and the rate of errors requiring correction. Compare like with like: the same product, applicant population, risk policy and observation period. A faster result is not a better lending decision unless its risk and customer consequences are acceptable.
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Can AI help banks lend to more people?
Potentially, if better information helps identify creditworthy applicants who are poorly served by conventional assessments, or if tailored offers improve access without weakening underwriting. The evidence here does not establish that AI universally expands fair access. The Italian-bank study is bounded to its market and period, and the ECB describes possible predictive and customer-service benefits rather than a sector-wide inclusion result.
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Access should therefore be assessed alongside disparate outcomes and model performance across relevant groups. A broader set of data can add useful signal, but it can also reproduce historical patterns or introduce new proxies for sensitive characteristics. More approvals alone do not demonstrate fairer lending.
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Bias, data quality and model performance
Incomplete, inaccurate or historically skewed data can produce unreliable assessments or unequal outcomes. Models can also drift as markets, products or applicant behavior change. ECB supervisory observations describe banks mapping higher-risk use cases, checking data quality and maintaining feedback loops; these are practices observed in a limited sample, not a complete compliance checklist.
Explainability and accountability
Lenders need to understand and document how outputs inform a decision, preserve relevant data lineage and provide a reasoned basis for review. An AI-generated rationale is not sufficient if it does not reflect what actually drove the outcome. The EY document-review case illustrates one approach—recording audit trails and rationales and validating extracted information against manually checked data—but its reported results are specific to that engagement.
Security, privacy and third-party risk
Barr’s 2025 speech identifies immature technology, hallucinated or inconsistent answers, information-security threats and possible exposure of customer or proprietary information. Sensitive lending data requires suitable access controls and careful handling, particularly when tools or services are supplied by third parties.
Oversight depends on jurisdiction. A 2025 U.S. Government Accountability Office report describes financial regulators’ reliance primarily on existing laws, regulations, guidance and risk-based examinations, alongside some AI-specific guidance and examinations. It also notes then-current limitations in the National Credit Union Administration’s model-risk guidance and authority over technology service providers used by credit unions. These are U.S.-specific observations, not a statement of current law everywhere. See GAO-25-107197.
How should a lender evaluate an AI investment?
- Start with a lending problem. Specify whether the aim is better risk assessment, fraud detection, document handling, customer service or another measurable outcome. Avoid adopting a model simply because it is new.
- Establish a baseline. Record current decision times, error rates, risk outcomes, customer experience and manual effort for the relevant product and applicant group.
- Check the data and integration path. Confirm that information is fit for the intended purpose and that the system can connect to lending workflows, retain traceable records and handle exceptions.
- Validate against lending outcomes. Test predictive performance and customer impacts on appropriate data, monitor for drift, and assess performance under changing conditions rather than assuming ordinary-period results will persist in a crisis.
- Assign accountability before launch. Define who reviews outputs, handles overrides and complaints, monitors errors, manages security and oversees vendors. Keep human expertise in the process where a decision requires judgment or an explanation.
- Scale only when the evidence supports it. Compare results with the baseline and expand only if benefits hold without unacceptable deterioration in risk, fairness, service or control quality.
As Barr put it in his 4 April 2025 speech, “Gen AI may have benefits for consumers and businesses through better, cheaper, and faster financial services; however, to harness the upsides of Gen AI, banks, fintechs, and regulators all have a role to play in helping to ensure that the risks are managed.”
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