Published in FinTech Global
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Artificial intelligence is transforming the fight against financial crime. However, it’s also giving criminals powerful new tools. As banks, regulators, and technology providers race to harness AI for fraud detection, AML, and risk management, cybercriminals are using the same technology to launch more convincing scams, automate attacks, and evade detection.
The result is an escalating AI arms race, where success depends not just on adopting the technology, but on staying one step ahead of those using it for the opposite purpose.
How GenAI is changing financial crime
How is Generative AI changing the sophistication and scale of financial crime? In the view of Bhavin and Pooja Shah, founder and CEO, and director of product and client solutions at Sherlocq, respectively, the tools that promised to transform compliance are now being weaponized by the very criminals they were designed to stop. Both sides of the fight are running the same technology, and across every major financial center, the gap between attack and defense is widening.
They said, “We are no longer debating whether generative AI will reshape financial crime. It already has. The question confronting regulators, compliance leaders, and technology providers: from London and Frankfurt to Singapore, Dubai, and New York, is whether defenses can evolve fast enough to keep pace, or whether we have entered a genuine arms race with no clear winner.”
For many decades, financial crime followed easy-to-follow patterns: fraud required effort, money laundering required human networks and sanctions evasion required specialist knowledge. “Each constraint acted as a natural brake on scale. Generative AI has removed the brakes,” said the Sherlocq pair.
Generative AI, they added, allows criminal networks to personalize attacks at scale, test them against live defenses, iterate in real-time, and deploy across jurisdictions simultaneously.
They added, “Large language models are crafting hyper-personalized phishing campaigns, generating synthetic identities that pass traditional KYC checks, and probing AML detection systems to identify thresholds and exploit blind spots. US fraud losses climbed to $12.5 billion in 2025, with AI-assisted attacks contributing significantly to the increase. The pattern is not uniquely American: authorized push payment fraud in the UK, invoice fraud across the EU’s single market, and trade-based money laundering in APAC corridors are all exhibiting similar AI-assisted acceleration.
GenAI also enables attackers to deploy AI agents to automate and scale social engineering attacks at volume, conduct deepfake video calls to authorize fraudulent transactions, generate forged documents indistinguishable from real ones, and simulate legitimate transaction patterns to evade detection. “These capabilities mean that financial crime is no longer just more frequent, it is more adaptive, targeted, and difficult to identify,” said Bhavin and Pooja.