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Artificial intelligence goes beyond financial crime, and the financial industry is behind the defenses. Criminals are now using AI to create compelling deepfakes, tuned phishing attacks, and synthetic identities at scale. These tactics move faster than traditional compliance systems can track, exposing fatal flaws with current approaches.
Despite the scale of this threat, many organizations are rushing to deploy their own AI systems without ensuring that these tools are explained, transparent, and even fully understood. Unless explanability becomes a baseline requirement for AI systems used in financial compliance, there is a risk of replacing one form of opacity with another form, which will not build trust with the public or regulatory authority.
The arms race has already begun
AI is used to make old crimes faster and cheaper, and to promote the perpetration of new types of crimes. Consider the recent surge in synthetic identity scams. Cybercriminals use AI to combine realistic, forged data into realistic, forged identities. These profiles can be hardly distinguishable from real users, opening accounts, getting credits, and bypassing the verification system.
Deepfake Technology has added another weapon to Arsenal. Persuasive impersonations of CEOs, regulators, or families can now be generated with minimal effort. These video and audio clips are used to initiate rogue transactions, mislead employees, and trigger internal data leaks.
Even fishing has evolved. AI-driven natural language tools can create ultra-personal, grammatically modified messages tailored to each target based on public data, online behavior, and social context. These are not past misspelled spam messages. They are bespoke attacks designed to gain trust and bring out value. In the crypto space, phishing is booming, and AI is accelerating the trend.
Compliance tools are packed with pre-AI era
The challenge is not just the speed and scale of these threats. It is a discrepancy between attacker innovation and defender inertia. Traditional rules-based compliance systems are reactive and vulnerable. They rely on predefined triggers and static pattern recognition.
Machine learning and predictive analytics provide a more adaptive solution, but many of these tools are opaque. They generate the output without clarifying how they reached their conclusion. That “black box” problem is more than technical limitations. It’s a headache of compliance.
If there is no explanation, there is no accountability. If a financial institution cannot explain how the AI system flags a transaction (or fails to flag one), it cannot defend a decision to the regulator, client, or court. What’s even worse, you may not be able to detect that the system itself is making biased or inconsistent decisions.
Explanationability is a security requirement
Some argue that being able to explain AI systems slows down innovation. It’s shortsighted. Explanability is not a luxury. It is a requirement of trust and legality. Without it, the compliance team would be flying blind. They may detect anomalies, but they don’t know why. They may approve the models, but they cannot audit them.
The financial sector should stop treating explanability as a technical bonus. It must be a deployment condition for tools involved in KYC/AML, fraud detection, and transaction monitoring, among other things. This is more than just a best practice. This is an essential infrastructure.
This becomes even more urgent in a fast-moving space like a cryptography where trust is already vulnerable and highly scrutinized. The use of AI in security and compliance must not only be effective, but clearly fair, auditable and understandable.
Adjusted responses cannot be negotiated
Financial crime is no longer a matter of isolated incidents. In 2024 alone, illegal trading volume reached $51 billion. No company, regulator or technology provider can address this threat alone.
The adjusted response should include:
Demands explainability in AI systems used in high-risk compliance functions. It enables shared threat intelligence to represent new attack patterns across businesses. Training of compliance experts to interrogate and evaluate AI output. Requires fraud detection and external auditing of ML systems used by KYC.
Speed is always important. However, speed without transparency is a responsibility, not a function.
AI is not neutral and is not misused
The conversation needs to be shifted. It’s not enough to ask if AI “works” in compliance. You have to ask if you can trust them. Can I interrogate? audit? got it?
If these questions are not answered, the entire financial system is at risk. Not just criminals, but from the tools we rely on to stop them.
If you don’t build transparency in your defense, you’re not defending your system. We automate that obstacle.
