How AI is Helping Fight Back Against Financial Crime

While it’s true that bad actors currently leverage AI with few restraints, the “good actors” in financial services possess their own unique strengths, particularly when it comes to harnessing AI for defense against fraud and financial crime.

Understanding How Fraudsters Use AI

Bad actors face a very low barrier to entry to using AI. They’re free to use technology for any new scheme they can imagine and have no hesitation exploiting other people’s data. Good actors, meanwhile, must navigate a complicated web of regulations and ethical considerations. While essential, juggling data privacy, security, regulatory compliance, and the need for fair, unbiased AI can inadvertently create hurdles for the responsible development of AI models by financial institutions.

How AI Can Empower Financial Institutions

But the good actors should not get discouraged. It’s possible to turn the tide against criminals—by taking a page out of their playbook. Specifically, when it comes to collaboration and intelligence-sharing.

Criminals readily share intelligence. This begs the question: shouldn’t financial services’ good actors adopt a similar collaborative approach to combat this shared threat?

Imagine yourself being part of the financial services version of “The Avengers” team-up. Each superhero brings their own special skillset to the fight against the shared threat. In financial services such an industry-wide effort unites financial institutions, tech providers, regulators, cybersecurity teams, fraud analysts, and third-party enrichers against fraudsters. Members of this all-star superhero lineup can use AI to combine different insights and analyze the latest fraud and financial crime threats.

Overcoming Barriers to AI-Driven Fraud Prevention

Historically, concerns around data-sharing have hindered collaboration. While regulations like Article 314B of the US Patriot Act encourage data exchange under suspicion of money laundering, incentives for broad participation have been limited.

However, the key to effective collaboration lies in sharing anonymized risk signals, fraud patterns, and metadata – insights, not raw customer data – which can significantly bolster the security of the entire financial services ecosystem. Techniques like federated learning further enable this by allowing fraud models to learn across multiple data sources without compromising privacy or security.

Collaboration: The Key to AI-Driven Fraud Prevention

Bringing together various participants across the financial ecosystem is a critical step in collaboration against fraud. Gathering multiple insights from many different financial institutions and technology providers, allows organizations to learn key financial crime patterns while protecting customer data. Applying federated learning techniques across multiple data sources, your fraud models can continuously innovate and improve without compromising on data privacy or security.

Building on this foundation of collaborative intelligence, it’s crucial for the good actors to also adopt the mindset of their adversaries. This is where adversarial AI and machine learning become invaluable. Even if you don’t see a fraud attack yet, you can use adversarial testing to simulate a potential attack or fraud pattern. Your AI system can imitate fraud patterns learned from collaborative intelligence and take aim at your system’s defenses. Using this data, you see where your system is most vulnerable.

Drawing inspiration from cybersecurity practices, where ‘red teams’ simulate attacks to test the defenses of “blue teams,” financial institutions can leverage AI for adversarial testing. By mimicking fraud patterns learned from collaborative intelligence, AI systems can probe their own vulnerabilities. This approach, inspired by cybersecurity best practices, not only strengthens individual defenses but also encourages valuable knowledge sharing and collaboration between cybersecurity and fraud teams within financial organizations.

There’s power in collaborative teamwork for and avenues to do so for good actors using responsible AI. The bad actors are already working together to commit fraud. Good actors can ensure the financial services ecosystem remains trustworthy by making collaborative efforts a central part of their AI-based fraud prevention strategy.

• Blog: AI-Driven Fraud: How Criminals Are Using AI to Scam You
• Report: 2025 AI Trends in Fraud and Financial Crime Prevention
• Solution Sheet: Feedzai as a Service
• Solution: RiskOps. Unify All Risk Signals to Stop FinCrime.