When AI Fights Back: What the Experian–Resistant AI Partnership Signals About the Future of Financial Crime Prevention

The global fraud landscape is undergoing a transformation. Once characterized by opportunistic, low-tech scams, financial crime is now being perpetrated by increasingly sophisticated, tech-savvy syndicates—many of which operate across borders and at machine speed.

In this environment, traditional defences are showing their age. Static rules, manual case reviews, siloed data, and overnight batch monitoring simply can’t keep up with the velocity of modern scams. Now, a landmark move by Experian—a global leader in data and analytics—has put a spotlight on where financial crime prevention must go next.

On July 23, 2025, Experian announced a strategic investment in Resistant AI, a company specializing in artificial intelligence models designed to detect fraud, financial crime, and data manipulation in real time. This partnership represents more than capital injection. It underscores a critical inflection point for the industry: a recognition that AI must be used to counter AI.

Let’s explore why this is happening now—and what it means for financial institutions, regulators, and customers across Asia-Pacific and beyond.

The Escalation of Threats: AI-Powered Crime Is Already Here

In recent years, fraudsters have embraced artificial intelligence to supercharge their schemes. Deepfake voice calls are being used in business email compromise. Synthetic identities, often built with stolen or fabricated data, are being used to open mule accounts.

Criminal networks use automation to submit hundreds of loan or grant applications simultaneously, bypassing outdated document verification systems.

These developments aren't hypothetical. They’re happening right now—at scale.

In parallel, real-time payment systems have become the norm in many economies. While these systems improve customer experience, they also remove the window of time that institutions once relied on to detect and block suspicious activity.

The challenge is clear: financial institutions are now operating in an environment where high-speed transactions, AI-powered fraud, and strict privacy laws make traditional controls inadequate. And yet, the stakes—financial, reputational, and regulatory—have never been higher.

Authorised Push Payment (APP) Fraud: A Symptom of the Problem

One of the clearest examples of this new threat environment is Authorised Push Payment (APP) fraud.

In an APP scam, victims are tricked into transferring money to fraudsters under false pretences. These could include fake invoices, impersonated government officials, bogus investment opportunities, or even romance scams. Because the victim technically authorizes the transaction, banks are often limited in their ability to intervene—especially once funds leave the sender’s account and are moved quickly through laundering networks.

In some countries, APP fraud already accounts for nearly half of all reported fraud cases. And across Asia-Pacific, the numbers are rising:

• Singapore’s PayNow and FAST systems have enabled near-instant transfers, but also provided scammers with rapid exit routes. In 2024, the Monetary Authority of Singapore and major banks launched measures to counter rising APP fraud—but the threat continues to evolve.

  
  

• Australia reported over AU$3.1 billion in scam-related losses in 2023, much of it tied to APP-style transfers linked to investment and romance scams.

  
  

• Hong Kong, Malaysia, and Thailand have each reported a surge in cross-border push payment scams targeting consumers and SMEs, with cases often involving impersonation, phishing, and mule networks.

APP fraud is not just a consumer problem. It reflects a broader issue: real-time systems with limited counterparty transparency leave financial institutions with little time—or information—to assess fraud risk before funds are moved.

The Innovation: AI-Powered Detection Without Infrastructure Overhaul

One of the most interesting aspects of the Experian–Resistant AI partnership is its focus on real-time, infrastructure-light deployment.

Financial institutions today face a dilemma. They know their legacy rules-based systems aren’t sufficient, but many lack the appetite—or budget—for major system replacements.

This new solution offers an alternative: plug-in machine learning models that can work with existing platforms to detect anomalies and suspicious behaviors as they happen.

This is made possible by combining:

• Data-scale: Experian brings rich datasets and historical fraud signals across consumer, credit, and transactional domains.

• AI-scale: Resistant AI contributes real-time, adaptive models capable of spotting unfamiliar fraud patterns based on subtle deviations, behavioral signals, and document integrity.

Together, they aim to create a platform that not only detects known fraud typologies, but can also recognize emerging and evolving tactics in milliseconds. Crucially, it does this without disrupting the customer experience or requiring banks to overhaul their core systems.

For institutions across Asia-Pacific—many of which are balancing digital transformation with legacy infrastructure—this type of modular AI deployment is especially appealing.

The FRAML Shift: Converging Fraud and AML in Real Time

One quote from Resistant AI’s CEO, Martin Rehak, stands out:

“APP fraud can morph into money laundering in under five seconds.”

This observation reflects a growing trend: the convergence of fraud detection and anti-money laundering (AML) obligations. Increasingly, the same technologies used to detect fraud must also support broader compliance, including transaction monitoring, sanctions screening, and suspicious activity reporting.

Why? Because the pathways from fraud to laundering are now tightly connected. Stolen funds from an APP scam are often funneled into mule accounts, cycled through crypto exchanges, or layered across jurisdictions—all before human analysts can respond. This requires an integrated, cross-functional approach to financial crime risk management.

Enter FRAML: an emerging term that refers to the fusion of Fraud and AML programs, data, and tooling. In this model:

• Transaction monitoring and fraud detection teams share infrastructure and insights.

• AI models are trained across both fraud and compliance datasets.

• Real-time interventions are triggered based on a broader, more contextual view of risk.

Leading regulators, including AUSTRAC, MAS, and Bank Negara Malaysia, have begun encouraging such integration—while also requiring that any AI use in compliance functions be explainable, auditable, and transparent.

Implications for Financial Institutions in Asia-Pacific

The Experian–Resistant AI deal, though global in nature, carries significant implications for banks, fintechs, and regulators across Asia-Pacific.

1. AI adoption is no longer optionalWith the speed and sophistication of modern fraud, institutions must move beyond static rules and embrace adaptive technologies. Real-time anomaly detection, behavioral modelling, and contextual decisioning are becoming baseline capabilities.

   
   

2. Counterparty transparency is a priorityCross-bank, real-time payments make it difficult to assess who is receiving funds—and why. Solutions that can provide risk scores or fraud signals without violating privacy laws are essential for effective intervention.

   
   

3. Modular deployment will winEspecially in emerging markets, institutions need tools that integrate with existing systems and provide immediate ROI—without requiring months of customization or transformation.

   
   

4. Regulatory expectations are risingRegulators in the region are already issuing guidance around digital onboarding, mule accounts, and real-time fraud monitoring. Institutions that can demonstrate proactive, AI-driven controls will be better positioned during audits and inquiries.

   
   

5. The customer experience must remain seamlessReal-time fraud detection cannot come at the cost of user friction. The new generation of tools must distinguish legitimate activity from fraudulent behavior with high accuracy—without blocking good customers.

Conclusion: The Path Forward

Experian’s strategic investment in Resistant AI is more than a business move—it’s a recognition that the future of financial crime prevention is intelligent, real-time, and collaborative.

For financial institutions navigating an increasingly complex threat environment, this is a clear signal:Fraud and financial crime won’t wait. And neither should prevention.

AI is no longer just a competitive advantage—it’s becoming a regulatory expectation and an operational necessity. Institutions that act early to integrate adaptive technologies, converge fraud and AML programs, and deploy intelligent detection capabilities will not only protect their bottom line—but also their reputation, customer trust, and regulatory standing.

The battle against AI-enabled financial crime is here. It’s time for defenders to match that intelligence—with intelligence of their own.