AI and machine learning in fraud detection: Staying ahead of threats

Financial fraud has always been a moving target. From cheque fraud to phishing, criminals consistently adapt their methods to exploit weaknesses in the financial system. Yet in 2025, the contest between fraudsters and financial institutions is being transformed by AI and machine learning (ML).

Both sides are increasingly turning to the same tools. For banks, payment providers and regulators, the challenge is how to ensure that AI becomes a shield rather than a sword.

The Evolving Fraud Landscape

AI and machine learning in fraud

The sophistication of financial crime is accelerating. Faster payment systems, both domestic and cross-border, have reduced transaction times to seconds. While a boon to consumers and businesses, this immediacy offers fraudsters fewer obstacles and less time for intervention.

Generative AI (GenAI) is compounding these challenges. Fraudsters now use AI to clone voices, produce deepfakes and automate phishing content.

PwC and Stop Scams UK found evidence of early cases: celebrity impersonations in investment scams, cloned voices tricking employees into making transfers, and synthetic chatbots conversing with victims.

Andrew Bailey, Governor of the Bank of England, recently warned that AI will “very likely drive an increase in the number and sophistication of fraud threats” and emphasised that the time to act is now.

For financial institutions, the stakes are high. Fraud losses in the UK rose 22% between 2021 and 2022, with 90% originating online.

Traditional rule-based defences cannot keep pace with AI-enabled threats. The industry’s shift to intelligence-led, proactive detection is no longer optional—it is existential.

From Reactive to Predictive

Historically, fraud detection has relied on static, rule-based systems. While simple to implement, these generate high false positives and struggle to scale. As transaction volumes and channels grow, rule-based methods are being outpaced.

Machine learning changes the paradigm. By ingesting vast datasets, supervised models can recognise patterns linked to known fraud, while unsupervised models can flag anomalies that deviate from expected behaviour.

Combining the two enables systems to identify emerging schemes, not just repeat offences.

As Gemma Martin, Product Manager for Fraud and AML Analytics at Experian, puts it: “Machine learning has become an invaluable tool in the fight against fraud, helping companies move from reactive to proactive by highlighting suspicious attributes or relationships that may be invisible to the naked eye.”

Behavioural Biometrics: The Human Fingerprint

A particularly promising field is behavioural biometrics. These tools analyse patterns such as typing cadence, mouse movement or how a person holds their phone. Unlike passwords or even fingerprints, behavioural traits are dynamic and harder to steal.

For instance, if a fraudster gains access to a customer’s credentials, the deviation in their behaviour during a session—hesitation in entering familiar data, or keystrokes inconsistent with the account holder—can raise red flags.

Integrating behavioural biometrics into payment systems strengthens customer due diligence without adding friction. The challenge, however, is balancing privacy concerns with the need for granular behavioural monitoring.

Real-Time Risk Assessment

In the age of instant payments, fraud detection must be equally instantaneous. Delays of even a few seconds can mean the difference between intercepting a fraudulent transfer and losing funds forever.

Modern AI systems employ real-time risk scoring. Each transaction is assessed on variables such as location, device, amount, and historical behaviour. Transactions flagged as high risk may be blocked, delayed for review, or trigger additional verification steps.

Some banks are experimenting with predictive warnings for customers. For example, if patterns of communication suggest social engineering, the system can alert the user before they complete a transfer. Such interventions echo spam filters in email—quietly reducing risk while preserving usability.

AI Against AI

Fraudsters’ use of AI forces defenders to respond in kind. AI is now being deployed to identify synthetic content—detecting manipulated images, cloned voices, or chatbot-driven scams.

One UK bank recently piloted AI-powered chatbots to proactively engage with fraudsters, gathering intelligence such as mule account details. Automating this capability could scale disruption efforts significantly.

As one Head of Fraud observed, “Automating scam disruption using AI-powered chatbots could enable disruption at scale and provide valuable intelligence to identify fraudsters operating within our financial systems.”

This “AI fighting AI” dynamic will increasingly define the next phase of fraud defence.

Operational Efficiency: The Augmented Investigator

Fraud detection is not just about technology but also people. Investigation teams often spend hours collating and cross-referencing data, much of it routine and low-value. AI can automate these tasks, freeing human analysts to focus on high-priority cases.

The concept of the “augmented investigator” is gaining traction. Here, AI copilots assist skilled analysts by integrating data, providing decision support, and even suggesting investigative leads. Feedback from investigators then retrains detection models, creating a virtuous cycle of learning.

Leading firms report that ML deployments have reduced poor-quality alerts by 30–40%, significantly lowering investigator workloads and enabling more focus on complex risks.

Generative AI: Friend and Foe

Generative AI exemplifies the double-edged nature of technological innovation. On one hand, fraudsters are using GenAI to craft phishing messages without spelling errors, generate fake ID documents, or produce deepfake audio that can bypass voice biometrics.

On the other, financial institutions can deploy the same tools to detect synthetic content, simulate attack scenarios, and harden systems against emerging threats. Safeguards by design—such as watermarking AI-generated content—may help, but these are unlikely to deter bad actors operating outside regulated frameworks.

The arms race is already underway, and speed of adaptation will determine outcomes.

Regulation and Collaboration

Supervisors globally are recognising AI’s role in fraud prevention. The FCA in the UK has promoted innovation through sandboxes and tech sprints, while the Bank of England and PRA stress the need for governance of data and models. In Asia, regulators from Hong Kong to Singapore are actively encouraging AI deployment in anti-money laundering (AML) and fraud monitoring.

Yet regulators are also alert to risks of bias, explainability, and data privacy. Balancing innovation with fairness is crucial. As PwC’s report stresses, “Fraud controls will need to be constantly evolved to combat new fraud types”.

Cross-sector collaboration is equally vital. Stop Scams UK, a coalition of banks, telecoms and tech firms, is spearheading rapid response capabilities to share intelligence across industries. No single institution can outpace AI-enabled fraudsters alone.

Challenges and Limitations

While AI offers transformative capabilities, it comes with challenges:

• Data dependency: ML models require vast, high-quality datasets. Incomplete or biased data risks skewing detection.
• False positives: Overly sensitive models can overwhelm investigators and frustrate customers.
• Hallucination: Language models may generate fabricated insights if not carefully constrained.
• Implementation complexity: Integrating AI into legacy systems demands significant investment.
• Bias and fairness: Unchecked, AI may inadvertently discriminate, leading to regulatory and reputational risk.

The solution lies in governance frameworks that emphasise transparency, explainability, and continuous validation of models.

The Road Ahead

AI and ML are not a silver bullet, but they represent the most promising tools available to tilt the balance against fraudsters. The convergence of predictive analytics, behavioural biometrics, and real-time monitoring will redefine fraud prevention in the next five years.

Future developments may include:

• Multi-modal risk scoring: Combining text, audio, image and behavioural data to assess threats holistically.
• Adaptive learning at scale: Systems that evolve dynamically as fraud patterns shift.
• Integration with digital identity: Using AI to authenticate individuals across payments, healthcare, and government services.
• Public education: As synthetic content proliferates, consumers will need training to spot fraudulent cues.

The ultimate measure of success will be whether AI-driven defences can outpace AI-enabled fraud. The race will not end, but institutions that invest early and collaborate widely will stand the best chance of staying ahead.

Redefining Fraud

AI and machine learning are redefining fraud detection. They allow institutions to move beyond static, reactive systems towards proactive, predictive defence. Yet the very features that make AI powerful for good—scale, speed, and realism—also empower fraudsters.

For financial institutions, the task is twofold: adopt cutting-edge AI for prevention and detection, while building governance and collaboration structures robust enough to withstand evolving threats.

As Nicholas Holt of Marqeta has noted in another payments context, “The future of payments is digital.” The same can be said of fraud and its prevention. AI will dominate both sides of the equation. The question is not whether criminals will use it—they already are—but how effectively the industry can harness the same tools to safeguard trust.

The financial system has entered an era where algorithms duel in real time. Staying ahead requires vigilance, innovation, and partnership. Those who invest now will shape not just the future of fraud detection, but the integrity of digital finance itself.