The future of Fraud: what Banks must prepare for 2026–2030.

This transformation it’s both technological and structural. With upcoming PSD3 and PSR, the rules of liability are shifting and banks will no longer face this challenge alone. The next wave of fraud prevention depends on collaboration, not isolation. Instead of closed systems, the industry is moving toward interconnected trust ecosystems for sharing risk intelligence in real time.

The most exposed sectors remain banks and payments. Why? Because instant transactions and AI-powered systems create both opportunities and threats. But not only. Healthcare, retail and even digital identity services are seeing rising cases of impersonation, synthetic profiles, and cross-platform scams, as fraud tactics move between industries.

According to Juniper Research, global fraud losses in financial services are expected to surpass $58 billion by 2030, a 150% increase driven by AI-generated identities, real-time payments, and the blurred line between what’s real and what’s not.

And it’s not just about numbers, it’s about people too.

Nearly 70% of adults worldwide encountered a scam in the past year, and 13% face one every day (Gasa Global State of Scams 2025 Report). Global losses reached an estimated $442 billion, but the real damage is the human impact: 69% said the experience left them very or somewhat stressed by the experience (Gasa Global State of Scams 2025 Report).

So today’s challenge isn’t just catching fraudsters, it’s keeping up with how fast fraud itself is evolving. Understanding where fraud is heading next is the first step toward building resilience.

The Five Fraud Trends That Will Define 2026–2030

“We're entering an era where fraud prevention isn't about building higher walls— We need to think about resilience, not just resistance. It's shared intelligence, explainable decisions, and ecosystems that learn collectively. Because fraud doesn't respect organizational boundaries—and neither should our response.”

- João Courinha, Senior Global Product Manager - Fraud Management Solutions

1. Synthetic Identities

The next wave of identity fraud will come from data that never existed, not only from stolen data. How? By using GenAI and blending real and fake data to create people who don’t exist, yet look completely legitimate. With jobs, credit histories and even social media activity, they act like real customers until one day, when they disappear, taking loans, overdrafts or stolen funds with them.

Experts warn that these “Frankenstein identities” are already bypassing traditional KYC checks and could soon become one of the most expensive and pervasive forms of digital fraud. What makes them so hard to catch? They look ordinary. There’s no victim to complain, no flag to raise, just a “digital ghost” hiding in the system.

2. Deepfakes and Real-Time Scams

Fraud now moves at the same speed as money: instant. As real-time payments become standard, so do real-time scams. With deepfake technology like voice clones, AI videos, digital impersonations, fraudsters can now mimic anyone, even a bank adviser. For example a customer receives a video call from their “bank.” Same voice, same face, same office background. Only it’s not them. Within minutes, they authorize a “security transfer”.

These scams work not because people are careless, but because they really trust their banks. Deepfakes bypass logic and exploit emotion faster than security systems can react.

3. First-Party and Money Mule Fraud

Not all fraud comes from outsiders. Some involves real customers or their accounts (or even both).
First-party fraud happens when someone lies about a transaction or credit to avoid paying. Money-mule fraud happens when people let their accounts move stolen money, sometimes for quick cash, sometimes without even realizing. Across the world, this behaviour is being industrialised through social media and job posts that look legitimate but in reality they are not.
Both types are difficult to detect because they look like normal activity, which means we all should rethink what “suspicious” activity and “suspicious” transactions really look like.

4. Account Takeover

Fraudsters used to guess passwords, but now they actually imitate people. Scammers use bots and stolen data, log in, copy human behaviour, and take control of real accounts in seconds. These takeovers really damage both funds and trust in digital banking itself.

By 2030, fraud prevention will depend on continuous verification, using behavioural biometrics to spot how someone types, swipes, or moves through an app.

5. Emotional Scams

Scammers are getting better at sounding… real. There are more and more cases worldwide. They build fake relationships, send convincing images, and guide victims into “investment” or “romance” traps. With AI creating  photos, voices, and messages, these scams feel very personal.

What’s new is how personal these attacks have become. They exploit vulnerability, empathy, and urgency, the very traits that define humanity. It’s no longer just about stealing money, but manipulating emotion.

The Industry’s Next Evolution: Explainable AI and Collaborative Defence

AI has transformed fraud detection but it’s also transforming fraud itself. As algorithms get more powerful, transparency becomes essential. Regulators and banks are beginning to demand explainable AI (XAI) models that not only predict risk but also explain why.

To keep up, the industry is investing in behavioural analytics, real-time intelligence sharing, and living rule sets, dynamic systems that evolve as fast as fraud tactics change.

Collaborative ecosystems, where banks, fintechs, telecoms, social media platforms and regulators share insights securely, will define the next decade.

Beyond Defence: Building Resilience in a Human Era

Fraud between 2026 and 2030 is getting faster and smarter, but it’s not unbeatable. The future of prevention will rely on three pillars: collaboration, intelligence, and trust.

Fraud will continue to evolve, but so will defences. Trust doesn’t stop at one organisation’s border, it’s built, shared, and protected collectively. When one organisation learns, the whole ecosystem benefits. The banks leading this shift are finding new balance: verifying continuously, understanding behaviour in context, using explainable AI, and keeping people at the centre.