Generative AI in Fraud Detection: How to Stay Ahead of Fraudsters

Financial crime is evolving at an unprecedented pace as fraudsters become early adopters of generative AI. Compliance teams, banks, and law enforcement must adapt rapidly to keep up with the increasing sophistication of AI-driven fraud tactics. 

‍

In this blog, our expert panel—Dana Lawrence, Hailey Windham, and Matthew Hogan, shares insights on the impact of AI in financial services and how institutions can apply generative AI in fraud detection and prevention.

‍

What’s Holding Companies Back from Investing in AI in Risk and Compliance?

‍

Many companies are hesitant to invest in AI for risk and compliance due to a variety of challenges:

‍

Regulatory Concerns

‍

Insights from Dana Lawrence, Sr. Director of Fintech Compliance, Pacific West Bank

‍

Regulators have made it clear that existing laws still apply to AI in terms of risk and compliance. For instance, the Federal Deposit Insurance Corporation (FDIC) has shown openness to new technologies but expects risks to be managed appropriately. But, the challenge is that compliance teams must balance innovation with regulatory expectations. Institutions must ensure their AI in risk and compliance programs are transparent and explainable to pass regulatory scrutiny.

‍

Job Preservation

‍

Insights from Hailey Windham, Podcast Host & CU Advocate, Bank of Fraudology

‍

Job preservation concerns around AI are misplaced. AI is a tool to enhance, not replace, fraud fighters. And if your current technologies don’t offer AI, that’s when you need to make that exit plan, and begin searching for another provider. If we’re still fighting AI-driven fraud with manual processes, it’s not going to work.

‍

Evidence Admissibility and Accuracy

‍

Insights from Matthew Hogan, Detective, Connecticut State Police

‍

In law enforcement, AI adoption raises concerns about evidence admissibility and accuracy. Fraudsters operate without restrictions, while investigators must prove data integrity. Nevertheless, generative AI in fraud detection can be a game-changer for detecting large-scale fraud schemes, making adoption necessary despite its challenges.

‍

The Growing Threat of AI-Driven Fraud and Its Challenges

‍

The rise of AI-driven fraud presents an increasingly complex threat, raising several challenges for businesses to address:

‍

The Slow Adoption of AI in Risk and Compliance

‍

Insights from Matthew Hogan, Detective, Connecticut State Police

‍

The adoption of AI in risk and compliance will be slow, as many companies, agencies, and law enforcement remain hesitant to fully embrace generative AI in fraud detection. This is often due to a lack of understanding of how to use it effectively. However, the rise of AI-driven fraud is inevitable, with everything from pig butchering scams to phishing and smishing being enhanced on a much larger scale. 

‍

Many still rely on KYC (Know Your Customer) processes to prevent fraud, but this approach is increasingly insufficient. While KYC remains essential, it doesn't address the complexities of modern fraud detection, as I've seen firsthand in cases involving subpoenas. It's clear that over-relying on KYC as the sole deterrent is no longer enough.

‍

Challenges in Real-Time Detection and Response

‍

Insights from Hailey Windham, Podcast Host & CU Advocate, Bank of Fraudology

‍

As technology defenses improve, fraudsters quickly adapt, using generative AI tools that can bypass security checks and authentication measures in real-time. One of the most concerning developments is their ability to generate highly realistic synthetic identities with fake yet convincing data. 

‍

This complicates efforts, especially for teams operating in manual environments, as they struggle to keep up with these sophisticated threats' growing scalability and adaptability. However, the challenge is that these updates can’t always be implemented swiftly.

‍

Rising Sophistication in AI-Driven Fraud

‍

Insights from Dana Lawrence, Sr. Director of Fintech Compliance, Pacific West Bank

‍
Scammers are becoming more sophisticated, particularly in bypassing KYC controls like liveness checks in digital onboarding—once seen as impenetrable but now easily circumvented. This shift, coupled with the volume of attacks and massive data breaches exposing confidential information, reveals that traditional defenses may no longer suffice. 

‍

As AI-driven fraud and Fincrime Grow, What is Your Biggest Concern Today?

‍

As AI-driven fraud and financial crime continue to grow, many organizations are grappling with a range of concerns:

‍

Lack of Resources for Generative AI in Fraud Detection 

‍

Insights from Dana Lawrence, Sr. Director of Fintech Compliance, Pacific West Bank

‍
One of my biggest concerns about emerging generative AI is that, as I represent a smaller community bank, we may not have the resources to effectively manage the new risks it introduces. However, we still need to address these challenges. The question becomes: How do we adapt and pivot, especially given our smaller budget compared to larger regional or national banks? 

‍

Siloed Data & Poor Internal Communication 

‍

Insights from Hailey Windham, Podcast Host & CU Advocate, Bank of Fraudology

‍

One of our main challenges is siloed data, impacting organizations of all sizes. Without better visibility into payment flows and controls, staying proactive becomes difficult. But the issue isn’t just about external data silos—it’s also driven by weak internal communication. Collaboration between payments, fraud, operations, and frontline teams is essential to tackling threats, especially as new risks continue to emerge. With transaction monitoring systems falling short, it's critical to invest in strategies that strengthen integration and infrastructure across the organization.

‍

Tips on Fostering Awareness Around Generative AI in Fraud Detection

‍

Insights from Hailey Windham, Podcast Host & CU Advocate, Bank of Fraudology

‍

Financial institutions need to move beyond passive fraud prevention measures and actively build a culture of fraud awareness at every level. Here’s how organizations can strengthen their generative AI in fraud detection and prevention culture:

‍

• Frontline Employee Training: Employees must be equipped with real-time knowledge about fraud tactics. This training should be updated regularly as AI-driven fraud tactics evolve.
• Customer Education: Customers are often the first line of defense. Financial institutions must take the lead in educating them about common fraud schemes, red flags, and steps to protect their assets.
• Two-Way Communication: Fraud prevention is a collaborative effort. Institutions should create open communication channels where customers and employees can report suspicious activity in real-time.
• Internal Incentives for Fraud Prevention: Rewarding employees who successfully identify fraud early on can be a game-changer. Fun fact: Hailey’s team used the concept of “Scooby Snacks” to incentivize fraud detection efforts—simple, effective, and highly motivating.

‍

How to Use and Implement Generative AI in Fraud Detection

‍

Insights from Dana Lawrence, Sr. Director of Fintech Compliance, Pacific West Bank

‍

Start with AI Governance:

• Review the Federal Reserve’s AI Program to see an example of a regulator’s AI Governance Program. 
• Having an AI governance policy is important, even if your institution isn’t currently using AI. It provides a framework to overcome indecision. Using an AI governance framework helps identify the risks and set up incremental steps for safe AI integration
• Consider the NIST AI framework to learn how to set up controls for AI.
• Refresh your knowledge on model risk management—understanding how AI outputs are calculated to ensure they are explainable.

‍

Learn AI Use Cases in Risk and Compliance:

‍

• Generative AI in fraud detection: Refines monitoring programs by identifying patterns in your data and suggesting new rules.
• AI agents for Level 1 reviews: AI can handle the research phase by applying set rules to data, saving time for human decision-makers to focus on final assessments.
• Anomaly detection: AI detects deviations from normal patterns using machine learning, getting more accurate as more data is processed.
• AI for research: Use AI to scrape relevant news and information, keeping your team updated.
• Auto-generating alert narratives: Tools like Unit21’s AI Agent can draft alert narratives automatically, which can then be customized and reviewed, saving time on documentation.

‍

Prioritize Fraud Cases with AI-Driven Insights for Effective Detection

‍

Insights from Hailey Windham, Podcast Host & CU Advocate, Bank of Fraudology

‍

For effective fraud detection, it’s important to prioritize cases based on accurate analysis. While case scores may help, they might not capture all necessary details. An AI-powered case management system can provide a more comprehensive view. 

‍

For example, AI can flag a situation where an online account takeover is coupled with fund transfers, which requires urgent attention. This allows you to act before processing the ACH origination file, minimizing risk. 

‍

Generative AI in fraud detection also helps prioritize cases by analyzing case data and acting as a virtual assistant, triaging fraud alerts and highlighting high-risk cases that require immediate action.

‍

Assess Workflows and Implement Risk-Managed Solutions

‍

Insights from Matthew Hogan, Detective, Connecticut State Police

‍

Every organization must assess its current workflow and identify where it can effectively integrate generative AI in fraud detection and prevention. AI can enhance operations in countless ways, but each institution will have unique needs and approaches. Therefore, it's crucial to adopt AI in a justified manner, implement a solid testing process, and address risk management concerns. If you're considering AI, using an established framework or borrowing one is a great way to get started.

‍

Implement Generative AI in Your Fraud Detection with Unit21!

‍

Generative AI in financial services isn’t a futuristic concept; it’s here, shaping fraud and compliance daily. And this year will determine which institutions lead in AI adoption and which fall behind. 

‍

Register here to watch our webinar on “The Good, Bad, and Ugly of GenAI in Fraud & AML” and gain valuable insights and strategies in generative AI in fraud detection!