The Landscape
Fraud prevention is the most mature AI category in financial services, and for good reason. Global payment fraud losses are projected to exceed $40 billion annually by 2027. Every payment processor, issuer, and fintech platform needs fraud detection, and the legacy rules-based systems that dominated for two decades are being replaced by machine learning models that can adapt in real time.
The current generation of AI fraud tools goes beyond simple transaction scoring. Behavioural biometrics, device intelligence, network graph analysis, and pre-transaction risk signals are now table stakes for enterprise-grade platforms. The best tools combine multiple detection layers and can explain their decisions to compliance teams, not just flag transactions.
The competitive landscape is crowded. We focus our reviews on tools that demonstrate genuine AI capability rather than marketing claims, with particular attention to how well they serve the payments and fintech ecosystem.
Reviewed Tools
Sardine — 4/5
$145M raised. 300+ enterprise customers including FIS, Deel, and Brex. Sardine's core innovation is pre-transaction behavioural intelligence — scoring fraud risk before the user clicks submit. Their device and behaviour SDK captures thousands of signals during the session, feeding ML models that detect fraud patterns invisible to traditional tools. New agentic AI investigation features automate analyst workflows.
Read the full Sardine review →
Sift — 4/5
A pioneer in digital trust and safety with over 15 years in the market. Sift processes billions of events annually across its global network, using that data advantage to power ML models for payment fraud, account takeover, and content abuse. Their Digital Trust & Safety Suite offers a unified platform approach, and real-time decision engines can process transactions in under 100 milliseconds.
What Sets This Category Apart
Fraud prevention AI has the clearest ROI case of any category in this directory. The metrics are direct: fraud losses prevented, false positive rates reduced, manual review volumes decreased. This makes it the most data-driven purchasing decision in the stack, and the category where vendors are most willing to prove their claims with measurable outcomes.
The emerging challenge is agentic transactions. As AI agents begin making purchases on behalf of consumers, fraud detection systems built to identify anomalous human behaviour will need to adapt. The tools that solve agent-initiated fraud detection first will have a significant competitive advantage.
Related Coverage
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What to Watch
- Real-time payment fraud. As instant payment networks expand globally, the window for fraud detection shrinks from hours to milliseconds. Tools that can score risk pre-authorisation will dominate.
- Agent-initiated transaction fraud. Purpose-built detection for machine-speed, machine-initiated transactions. A fundamentally different problem from human fraud detection.
- Regulatory pressure on false positives. Consumer protection regulators are increasingly scrutinising false decline rates. Fraud tools will need to balance prevention with customer experience.
- Generative AI in social engineering. Deepfakes, AI-generated phishing, and synthetic identities are creating new attack vectors that current models were not trained to detect.
Charlie Major is a Product Development Manager at Mastercard. The views and opinions expressed in Major Matters are his own and do not represent those of Mastercard.