As financial transactions become increasingly digital, fraudsters are evolving in lockstep. The same cutting-edge technologies that are being used to protect payment systems are also being used to exploit them.
Because, while AI has huge potential for improving financial services operations – such as catching fraud cases and understanding customer preferences – it’s also increasingly being used for fraud. So much so that 85% of senior payment professionals identified fraud detection and prevention as the primary use case for AI.
While AI, coupled with machine learning (ML), is offering transformative promise in the payments and banking sector, it’s also introducing unprecedented challenges.
The New Fraud Landscape
Traditional fraud detection systems are no longer enough. Static, rules-based systems – built on “if this, then that” logic – are proving far too rigid for today’s dynamic threat environment. Fraudsters are deploying AI-powered tools to create synthetic identities, deepfakes, automated phishing attacks, and social engineering tactics.
And it’s only getting started. Deepfake-related identity fraud surged by over 780% in some Europe between 2022 and 2023, while payment card fraud is expected to rise by over $10 billion by 2028 in the US. With real-time payments growing and digital interconnectivity increasing, the scale and speed of fraud are only accelerating.
How AI Is Changing the Game
AI’s core advantage lies in its base ability to detect anomalies across vast datasets in real time. Unlike traditional systems, AI models learn continuously, identifying new discrepancies in payments as they emerge. This makes AI invaluable in protecting against increasingly agile and elusive threats.
Banks and payments firms are already deploying AI at scale. Visa processes over 500 million transactions daily using more than 500 AI applications to flag suspicious activity. Mastercard has invested billions, including acquiring cybersecurity firm Recorded Future, to power its AI-driven threat intelligence systems.
These systems minimise false positives and reduce operational costs while enhancing the customer experience. A smoother transaction process, with fewer disruptions caused by misidentified fraud, builds trust while keeping security tight.
Several institutions have already seen dramatic improvements. Major Thai credit card provider, KTC, uses AI to score transaction risks in milliseconds, preventing high-risk payments while allowing
legitimate transactions to proceed seamlessly. One global bank, partnering with Cognizant, saved $20 million by using AI to detect fraud in check verification processes, analysing patterns in signatures and payment behaviour.
AI and ML can transform fraud detection and prevention
AI and ML offer transformative benefits across the financial ecosystem, particularly in fraud detection, compliance, and operational efficiency. These technologies enable systems to process vast volumes of data in real time, identifying patterns, anomalies, and emerging threats with far greater accuracy than traditional methods.
AI and ML working together can automate complex tasks – such as transaction monitoring, identity verification, and regulatory reporting – freeing up human resources and reducing the risk of manual errors. Their predictive capabilities allow institutions to anticipate fraud before it occurs, enhance customer experiences through personalised services, and make faster, data-driven decisions. As these technologies continue to evolve, they provide a scalable, adaptive foundation for navigating an increasingly digital and risk-sensitive financial landscape.
The Challenges Ahead
Despite its benefits, AI brings new complications. Fraudsters are weaponising generative AI to create fake documents, craft convincing phishing emails in localised languages, and produce real-time voice and video deepfakes that can fool biometric systems.
The result is a continuous escalation: as AI strengthens defences, adversaries develop better tools to breach them. This dynamic demands constant investment in AI capabilities – industry forecasts predict financial sector AI spend will rise from $35 billion in 2023 to $97 billion by 2027.
Moreover, AI systems themselves require oversight. Bias in AI decision-making, lack of transparency, and risks to data privacy are major concerns. Firms must invest in privacy-preserving technologies such as federated learning and differential privacy, which allow machine learning without compromising user confidentiality. The human element cannot be ignored as well, being able to develop a strong relationship and understanding of AI tools is crucial to implementing good systems that can overcome the rise in fraud.
Fraud in 2025
Ultimately, AI is not a silver bullet. The most effective fraud prevention systems will combine AI’s real-time analytical power with human insight and oversight. Much like the evolution of autonomous vehicles, fraud prevention is moving from human-controlled to intelligent hybrid systems – capable of rapid adaptation, but still reliant on the judgment and contextual understanding that only people can provide.
We are at an inflection point. The old ways of fighting fraud are rapidly becoming obsolete. In today’s high-speed, AI-powered environment, organisations that fail to innovate risk falling behind.
To protect financial security, maintain consumer trust, and ensure compliance, firms must treat AI not as a tool, but as a strategic imperative – one that evolves just as fast as the threats it aims to stop.
