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Freedom251 › Blog › AI in banking

AI in Banking: What It Can Do and How to Judge Its Role

By Freedom251 Team · · 4 min read

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AI may help a bank spot suspicious transactions, assess credit applications or answer routine questions, but it is usually software the institution uses behind the scenes—not a separate AI product inside your account. Its role and maturity vary by bank and region, and it does not guarantee safer accounts or fairer decisions.

Where AI may show up in everyday banking

Banks can use AI to find patterns in large volumes of activity, support decisions and automate some customer interactions. The European Central Bank (ECB) reported increased use cases among significant euro area banks between 2023 and 2024, including fraud detection and credit scoring. Its supervisory reporting covered 107 significant institutions in 2023 and 110 in 2024; those figures describe the institutions covered, not the share of all banks using AI. The ECB also drew on workshops with 13 selected banks for some observations, so those findings should not be treated as representative of every institution. ECB Banking Supervision, 20 November 2025

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Transaction fraud monitoring

Fraud systems can look for activity patterns that appear unusual and support real-time monitoring. That can help a bank flag a transaction quickly, but a flag is not proof of fraud. A legitimate purchase may be blocked or challenged, while poor-quality information or an opaque decision can make it difficult to understand why the system reacted. AI does not by itself ensure that an account is protected.

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Credit assessment

AI-assisted predictive analysis may contribute to credit assessment, influencing a lender’s decision about an application. The outcome can depend on the information available and how a system uses it. Data quality and bias are consumer concerns: an automated process may produce a flawed assessment, and an explanation that is hard to interpret may leave an applicant unsure what affected the decision. The US Government Accountability Office (GAO) identifies biased lending and data quality among risks associated with AI in financial services. GAO, May 2025

Automated customer service

A bank chatbot may handle a straightforward question, such as a basic account inquiry, without a conversation with an employee. More complicated issues can exceed what a chatbot handles well. The US Consumer Financial Protection Bureau (CFPB) warns of wasted time, frustration, inaccurate information and junk fees, as well as privacy and security risks. It estimated that about 37% of the US population had interacted with a bank chatbot in 2022; that is a dated estimate cited in a 2023 report, not a current usage rate. CFPB, 6 June 2023

What the evidence can—and cannot—tell you

Reported adoption, past chatbot interaction and willingness to try a new kind of AI measure different things. They cannot be used interchangeably to predict what is happening in your account or what you will choose to use.

  • The ECB describes increased AI use cases among significant euro area banks from 2023 to 2024, but its findings do not establish that every bank uses the same systems or that those systems perform equally well.
  • The CFPB’s roughly 37% estimate concerns US residents who had interacted with a bank chatbot in 2022.
  • A Financial Conduct Authority (FCA) survey conducted in April 2026 found that 20% of more than 5,000 UK retail financial services consumers said they were likely to use AI able to act autonomously within preset goals. That is stated willingness, not observed use or a forecast. Respondents also expressed concerns about trust and control. FCA, 6 July 2026

These findings cover the euro area, the United States and the United Kingdom. They do not establish a universal consumer right to a particular explanation, human review or remedy; rules and recourse depend on jurisdiction.

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How to assess an AI-related banking interaction

If a transaction is challenged, a credit decision seems wrong or a chatbot cannot resolve a problem, focus on the bank’s process rather than assuming the system is either reliable or unreliable.

  • Identify the task. Ask whether automation flagged a transaction, contributed to a credit assessment or answered a service question. These are distinct uses with different consequences.
  • Ask how to reach a person. For a disputed or complex matter, find out how to contact a human representative and submit relevant details through the bank’s official channel.
  • Request an understandable explanation. For a credit decision or account action, ask what information influenced it and how to report an error. What explanation or review is available depends on the institution and applicable law.
  • Check the information involved. If the decision appears inconsistent with your records, identify the specific information you believe is wrong and ask how it can be corrected.
  • Protect account information. Use the bank’s official app, website or phone channel; do not disclose credentials or security codes to an unverified chatbot or contact.
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Why explanations and human oversight matter

An explanation can help a customer identify inaccurate information or challenge an outcome, but more detail does not automatically make a system easier to check. FCA research on explanations for AI-assisted credit decisions found that explanation formats affected consumers’ ability to identify errors differently depending on the type of error. More information about how a model worked was well received and increased confidence in disagreeing, yet additional information could also make some errors harder to spot. FCA research on explainability

For financial firms, oversight also includes managing operational risks, data quality, privacy and cybersecurity. GAO says US regulators rely mainly on existing laws, regulations, guidance and risk-based examinations, alongside some AI-specific guidance and continuing assessment of risks. That describes the US regulatory approach, not a single global standard. GAO, May 2025

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