AI in Finance: Fraud Detection and Smart Banking

Banks and payment companies use AI to score transactions as they happen, spot unusual account behavior, flag synthetic identities and cut down the manual work in anti-money laundering (AML) checks. This article explains how those systems work, where they are being used and what limits them.

How Widely Is AI Used in Finance?

A 2024 survey of 118 UK banks, insurers, investment firms and payment companies by the Bank of England and Financial Conduct Authority found that 75% were already using AI, up from 58% in 2022, with a further 10% planning to adopt it within three years. Fraud detection, AML and cybersecurity were among the use cases where firms saw the largest current benefits.

The pressure is rising on the other side too. Deloitte’s Center for Financial Services projects that generative AI could push US fraud losses to $40 billion by 2027, up from $12.3 billion in 2023, a compound annual growth rate of 32%.

How AI Detects Fraud

1. Real-Time Transaction Scoring

Traditional fraud systems rely on fixed rules, such as blocking transactions above a set amount from a new country. Machine learning models instead score each transaction against patterns learned from historical data, so they can flag fraud that does not match any existing rule. Scoring happens in milliseconds, before the payment is approved, which means fraud can be stopped rather than only detected afterwards.

2. Behavioral Analysis and Anomaly Detection

AI systems build a profile of normal behavior for each customer: typical spending amounts, merchants, login times, devices and locations. Deviations from that profile raise the risk score.

This matters for account takeover, where a fraudster logs in with a customer’s real credentials. The login itself looks valid, but a new device, an unusual time of day or a sudden change in payees can reveal that someone else is using the account.

3. Synthetic Identity and Deepfake Detection

Synthetic identity fraud combines real and fabricated details to create a person who does not exist, then uses that identity to open accounts or take out loans. Generative AI has made this easier. In November 2024 the US Financial Crimes Enforcement Network issued an alert on deepfake media after seeing more suspicious activity reports describing fake identity documents built with generative AI to get past identity verification.

Detection models cross-check application data against multiple sources, look for inconsistencies between documents and flag signs of manipulated images or video. Controls such as phishing-resistant multifactor authentication and live audio or video verification add a further layer of protection.

4. Anti-Money Laundering (AML)

Rule-based AML monitoring produces large numbers of false positives, each of which an analyst has to review. Machine learning can prioritize alerts by risk, and network analysis can map relationships between accounts to expose layering and other patterns typical of organized financial crime. The result is fewer low-value alerts and faster investigations of the ones that matter.

5. Predictive Risk Scoring

Using historical data, models can estimate the likelihood that an account, merchant or application will be involved in fraud, so institutions can apply extra checks before losses occur rather than reacting after the fact.

Real-World Examples

• Visa: In March 2024, Visa said its fraud prevention systems helped block $40 billion in fraudulent activity, nearly double the previous year. It also introduced deep learning models for card-not-present and account-to-account payments, and said it had invested $10 billion in technology over the previous five years.
• DBS Bank: According to its 2024 annual report, DBS’s data analytics and AI/machine learning initiatives delivered more than SGD 750 million of economic value in 2024, over double the previous year, using more than 1,500 models across more than 370 use cases.

Benefits of AI in Finance

• Fraud is scored and blocked in real time rather than found after losses occur
• Fewer false positives in fraud and AML alerts, reducing manual review
• Faster credit and risk decisions
• More personalized customer service

Challenges and Risks

• Data privacy and security: models depend on sensitive customer data that must be protected and governed.
• Regulatory compliance: decisions on fraud, credit and AML must be explainable to regulators and customers.
• Transparency: in the Bank of England and FCA survey, 46% of firms said they had only a partial understanding of the AI technologies they use.
• Third-party dependence: the same survey found a third of AI use cases were third-party implementations, up from 17% in 2022.
• Integration: connecting models to legacy core banking systems is often the slowest part of a project.
• AI-enabled fraud: criminals use the same generative tools to produce deepfakes and synthetic identities.

What Comes Next

Expect wider use of real-time fraud prevention, stronger identity verification to counter deepfakes, and AI agents handling routine back-office tasks under human oversight. Institutions that invest in data quality, model governance and explainability will be best placed to use these tools safely.