Tackling Payment Fraud: The industry is solving it the wrong way

The challenge is not just catching fraud when it occurs but also preventing it from becoming scalable.

Fraud in digital payments is no longer just an operational challenge—an industry-wide structural flaw that evolves faster than the solutions meant to stop it. As real-time payments (RTP) and UPI become the backbone of financial transactions, fraudsters have adapted, creating highly sophisticated and scalable fraud networks that bypass traditional risk models.

The industry talks a lot about stronger KYC, two-factor authentication (2FA), and AI-powered detection, but the truth is that we’re solving the wrong problem. Fraud isn’t just about stolen identities and unauthorized transactions anymore. It’s about networks of fraudulent accounts, carefully engineered to look legitimate at every step.

The challenge is not just catching fraud when it occurs but also preventing it from becoming scalable.

One of the fastest-growing fraud tactics today is synthetic identity fraud. Fraudsters no longer steal existing credentials—they create entirely new ones. These AI-generated identities pass KYC verification, open accounts, and have built a transaction history over the past months, making them indistinguishable from real users. In the first half of 2024 alone, synthetic identity fraud led to $3.2 billion in losses, up from $3 billion in the same period in 2023. The rise of fake merchants, fabricated business transactions, and embedded payment scams makes fraud more challenging to trace and stop.

Another rising threat is manipulating trust scores. Fraudsters don’t just create fake accounts—they age them. These accounts remain inactive for months, making small transactions appear legitimate, and then strike when they appear “trusted.” Most traditional fraud detection tools are designed to flag anomalies immediately after onboarding, but they fail when accounts are deliberately built to pass risk models over time.

The speed of real-time payments has also changed the equation. UPI and RTP systems process transactions in seconds, leaving no room for chargebacks or reversals. Fraudsters exploit this by layering transactions across multiple accounts, moving money through a web of seemingly legitimate transactions before anyone can flag it. By the time fraud is detected, the money has already disappeared.

Today, most risk models rely on post-transaction detection, which analyzes fraud after it happens. But by that point, the damage is already done. Instead of reacting to fraud, the industry needs to move towards pre-transaction intelligence, ensuring that only legitimate users and businesses can transact in the first place.

KYC alone is no longer enough. Fraud operates at a network level, so we need to move toward continuous verification that assesses not just individual users but transactional relationships across the ecosystem. Instead of just verifying a user once, risk scoring must be ongoing, dynamic, and adaptive to new behaviors.

Regulations also need to evolve beyond compliance checklists. Compliance does not equal security. While regulations play an important role, they must become risk-first rather than compliance-first. Fraud rings operate seamlessly across jurisdictions, making it critical for real-time fraud prevention to be a regulatory priority, especially in cross-border transactions.

The next era of fraud prevention isn’t about detection—it’s about making large-scale fraud structurally impossible.

AI-powered fraud prevention must evolve from just flagging anomalies to pre-emptively stopping fraud before initiating a transaction. This means integrating behavioral analytics, transactional pattern recognition, and network-wide fraud intelligence sharing between fintechs, banks, and regulators. Fraud detection must shift from an isolated company-by-company approach to an industry-wide ecosystem where threats are shared in real time.

Static risk models are becoming obsolete. Instead of assessing risk at a single point, fraud prevention systems need to use dynamic trust scores that adjust in real time based on user behavior, transaction context, and network-wide fraud signals.

The importance of business verification (KYB) is also increasing. Fake merchants and embedded finance fraud are rising, which means verifying businesses will become just as critical as verifying individuals. Expect to see real-time business identity scoring, multi-layered trust mechanisms, and decentralized verification techniques becoming the new standard.

The road ahead should involve a new standard for payment security. We are at an inflection point today. The payments ecosystem must decide whether to continue playing catch-up with fraud or redesign security from the ground up.

The next era of fraud won’t be about individual bad actors—it will be about fraud rings that operate invisibly at scale. The real challenge isn’t just how we detect them—it’s how we make it impossible for them to exist in the first place.

I am super excited for the future that can be built together.