AI is ubiquitous in payments today, with applications ranging from fraud detection and risk scoring to chatbots and personalisation. Every vendor claims it’s “transforming” the industry.
But here’s the problem: numbers can glow green while merchants still bleed red.
Yes, AI is delivering results. Mastercard’s Decision Intelligence processes over 160 billion transactions annually, spotting anomalies in real time and reducing false positives. Google tested AI-driven scam detection in GPay India, resulting in a 21% boost in scam enforcement. Tangible improvements exist—but the hype hides trade-offs.
If a fraud model lowers chargebacks but drives up refunds or false positives, merchants may actually lose more money even as the “fraud rate” improves. Too often, providers showcase metrics that please the schemes but don’t protect merchant margins.
Where AI Helps?
AI delivers measurable value in a few areas:
Fraud detection & risk scoring – catching patterns traditional rules miss.
- Scam interception – analyzing conversations and behavior to spot social engineering.
- Operational efficiency – reducing manual reviews and support workload.
- Customer experience – faster dispute handling, chatbots, smoother flows.
These improvements are real—but they are not without cost or friction.
Where Hype Outruns Reality?
- False positives & lost sales – blocking legitimate transactions can cost more than fraud itself.
- Refunds & appeasements – moving disputes out of chargebacks into refunds may make dashboards look good but erodes margins.
- Privacy & compliance – richer data improves AI, but increases regulatory and reputational risks.
- Legacy infrastructure – many payment systems can’t fully support modern AI, limiting actual deployment.
The result? Lots of announcements, but fewer net benefits once hidden leakage is considered.
What to Measure Instead?
Leaders should look beyond fraud rates:
- Net disputes – chargebacks + refunds + appeasements, not just chargebacks alone.
- Margin leakage – total cost of fraud, goods lost, re-shipping, refunds, and support.
- Conversion & friction – revenue impact from false positives and extra verification steps.
- Explainability – clarity of AI decisions for customers, regulators, and auditors.
Things to ponder
- If fraud drops 50% but false positives rise 10%, are we really ahead?
- Are we tracking margin leakage or just compliance?
- How much customer friction is acceptable for fraud prevention?
- Can we explain AI decisions if regulators or customers ask?
- What happens when fraudsters start using AI at scale?
The Road Ahead
The real impact of AI comes from balance: stopping fraud without losing good customers, improving compliance without hurting trust, and increasing efficiency without shifting costs elsewhere. Key trends:
- Privacy-preserving AI – federated learning and differential privacy.
- Explainable AI – regulatory transparency.
- Generative AI – operations, dispute handling, and risk narratives.
- Agent-based payments – LLMs initiating transactions in testing.
Bottom Line
AI in payments is real and effective—but only if measured correctly.
Until we focus on net disputes, margin leakage, and customer friction, we risk celebrating dashboards while undermining the very businesses that payments are meant to serve.