Financial services were using machine learning long before generative AI made headlines. Fraud detection and credit scoring have relied on statistical models for decades. Today, banks, insurers, payment companies and fintechs are adding generative AI to customer service, internal knowledge work and software development. Here is where AI is used — and why finance is one of the most carefully regulated areas for AI.

Fraud detection and anti-money laundering

Every card payment can be scored in milliseconds for fraud risk based on spending patterns, location, device and merchant. Machine learning models catch fraud patterns that fixed rules miss and adapt as criminals change tactics. In anti-money laundering, AI helps prioritize alerts and reduce false positives that overwhelm compliance teams.

Credit decisions

Lenders use models to estimate the probability that borrowers will repay. Machine learning can use more data and capture more complex patterns, potentially extending credit to people with thin credit histories. But it raises fairness and transparency concerns. In the US, the Consumer Financial Protection Bureau has emphasized that lenders using complex algorithms must still give consumers specific, accurate reasons when they deny credit. In the EU, AI used to evaluate creditworthiness is classed as high-risk under the AI Act.

Customer service

Banks and fintechs deploy AI assistants for account questions, payments and disputes. Payment company Klarna reported in 2024 that its AI assistant handled two-thirds of customer service chats in its first month — a widely cited example, though the company later said it would keep human agents available as well.

Trading and investment

  • Algorithmic trading has long used quantitative models; machine learning helps analyze market data and signals.
  • Research assistants summarize filings, earnings calls and news for analysts.
  • Wealth management uses AI to support advisors with portfolio suggestions and client communication.

Compliance and operations

  • reviewing contracts and regulatory documents;
  • monitoring communications for misconduct;
  • automating reconciliation and reporting;
  • assisting software developers who maintain banking systems.

Risks specific to finance

  • Fairness and discrimination in lending and pricing.
  • Explainability: regulators and customers expect reasons for decisions.
  • Model risk: errors can cause large financial losses; banks have model risk management frameworks for validation and monitoring.
  • Market stability: many firms relying on similar models could amplify market movements, a concern raised by financial authorities.
  • Fraud by AI: deepfake voices and documents are used against banks and customers.
  • Data protection and outsourcing to AI vendors.

Generative AI inside banks

Beyond customer-facing chatbots, much of the generative AI adoption in banking is internal:

  • Knowledge assistants that let employees search policies, product rules and procedures in natural language.
  • Relationship manager support: summarizing client histories and preparing meeting notes.
  • Code assistants for the large engineering teams that maintain core banking systems, often written in older languages.
  • Document processing for loan applications, trade finance and know-your-customer checks.
  • Regulatory reporting: drafting and checking reports against requirements.

Banks typically start with internal uses because they carry lower risk than customer-facing advice: a human employee reviews the output before it affects a customer. Many large banks have rolled out internal AI assistants to tens of thousands of employees, with access controls limiting which data each user can query.

How banks govern AI models

Banks already have model risk management processes, originally developed for credit and market risk models. These require independent validation before use, documentation of assumptions and limits, ongoing performance monitoring, and an inventory of all models. Supervisors expect these practices to extend to AI and machine learning, including third-party models from vendors.

What to watch next

  • Supervisory guidance on generative AI from banking regulators.
  • Enforcement on algorithmic credit decisions and adverse action reasons.
  • AI agents that execute payments and transactions on users’ behalf.
  • Defenses against deepfake-enabled fraud in banking.

Key takeaways

  • Fraud detection and credit scoring are long-standing machine learning applications in finance.
  • Lenders must still explain credit denials even when using complex algorithms.
  • Creditworthiness evaluation is high-risk AI under the EU AI Act.
  • Generative AI is spreading to customer service, research and compliance.
  • Model risk, fairness, explainability and market stability are key concerns.

Frequently asked questions

Can a bank refuse me credit because of an AI model?

Banks use models in credit decisions, but laws require them to treat applicants fairly and, in many jurisdictions, to explain the main reasons for a denial.

Is AI trading available to retail investors?

Many apps offer AI-powered insights or automated portfolios. Be cautious of products promising guaranteed returns, which are a common scam.

How do banks protect against deepfake fraud?

Through stronger identity verification, call-back procedures, multiple approvals for large transfers and detection tools.