Is AI giving much-needed assistance to U.S. law enforcement agencies to beat bad actors?
In 2024, the Treasury Department used AI to prevent and recover over $4 billion in fraudulent payments. “The use of AI has been instrumental in the fight against fraud,” said Deputy Secretary Wally Adeyemo, adding, “Helping ensure that agencies pay the right person, in the right amount, at the right time is central to our efforts.”.
Yet, as fraud tactics grow more advanced and complex, law enforcement agencies struggle with vast data and sophisticated schemes.
AI is emerging as a critical tool, enabling agencies to adapt to these new challenges. In fact, Deputy Attorney General Lisa Monaco has called AI “an indispensable tool to help identify, disrupt, and deter criminals,” underscoring its transformative role in modern investigations.
Considering this, let’s explore the seven key reasons why agencies are adopting AI to combat financial crime.
Discover 10 AI trends revolutionizing financial fraud investigations.
7 Reasons AI is Transformational for Law Enforcement Agencies to Fighting Fraud
As Benjamin Chou, a veteran in the field of fraud investigation, puts it: “If scammers are using AI to hide the money trail, there’s no way investigators can catch them without it.”
AI isn’t just a tool; it’s become essential for law enforcement to keep up with today’s fraud tactics. Here’s why agencies are leaning on AI more than ever:
- Handling the Overwhelming Volume of Data
- Decoding Complex Fraud Schemes
- Reducing Time-Consuming Manual Analysis
- Getting Rid of Mundane Data Normalization
- Maximizing Efficiency Within Resource-Strapped Agencies
- Bridging Gaps in Financial Crime Expertise
- Adapting to Support Each Investigator’s Unique Approach
- Enhancing Data Confidentiality and Compliance
Let’s dive into each one by one.
1. Handling the Overwhelming Volume of Data
Uncovering fraud buried within complex webs of transactions has always challenged law enforcement due to the sheer data volume involved. Now, with fraudsters using AI to create intricate schemes at unprecedented speeds, investigators face an overwhelming influx of data—fake identities, shell companies, and rapid, layered transactions intended to obscure illicit activities.
One notable case involved a fraud ring who used synthetic identities across hundreds of accounts to file over 370 false tax returns, claiming more than $110 million in refunds from the IRS. This intricate scheme created vast data trails that were difficult to manage with traditional investigative methods
AI, however, enables agencies to tackle this data overload efficiently. “AI lets us process massive data sets in seconds, cutting through noise,” explains Jim Lee, Chief of IRS Criminal Investigation.
By rapidly highlighting suspicious patterns, AI allows investigators to focus on high-risk leads with speed and precision, turning massive data sets into actionable insights.
Don’t miss 4 Ways AI Can Help Tackle Data Overload in Fraud Investigations.
2. Decoding Complex Fraud Schemes
Today’s fraud schemes are more sophisticated than ever, with criminals layering transactions, using shell companies, and creating synthetic identities to obscure their activities. These schemes require investigators to connect many hidden links—a process that can take months when done manually.
Consider the 1MDB scandal, where over $4.5 billion was funneled through a maze of shell companies across several countries. Each layer added complexity, making it extremely difficult to trace funds back to their origins.
This is where AI’s capabilities come into play. As Deputy Attorney General Lisa Monaco explains, “AI allows us to uncover relationships that would otherwise go undetected.” With pattern recognition, AI can quickly flag suspicious patterns, like frequent small deposits or unusual transaction spikes, that indicate potential fraud. By detecting these hidden links, AI offers investigators insights that manual methods might overlook, making it easier and faster to trace intricate money trails across borders.
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3. Reducing Time-Consuming Manual Reconciliation
Manually reconciling financial data—verifying and cross-checking transaction details across various sources like bank statements, checks, and receipts—is a tedious and error-prone process. This traditional approach can bog down investigations and introduce the risk of human error.
AI streamlines this process not only by automating data extraction and reconciliation. And some tools, like ScanWriter AI, also provide verifiable source links for each extracted data point. This traceability means investigators can validate AI-generated findings in real-time, reducing the risk of errors and reinforcing trust in AI-assisted outcomes.
As Benjamin Chou puts it, “This automation not only saves significant time but also reduces human errors, allowing investigators to focus on high-priority analysis.” With AI taking over repetitive tasks and ensuring source-verifiable accuracy, investigators can work more efficiently and confidently, accelerating complex financial investigations.
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4. Getting Rid of Mundane Data Normalization
Until AI came along, investigators had to handle data normalization by setting up rules and doing manual clean-ups. They’d spend hours aligning names, dates, and formats across different sources. Even minor variations—like a person listed as “John Doe” in one bank and “J. Doe” in another—could slip through the cracks, making it easy to miss key connections.
AI changed that game entirely. Now, AI-driven tools automatically standardize these differences, catching variations like “John Doe” versus “J. Doe” or different date formats without any manual effort. The AI links related entities and transactions across sources instantly, giving investigators a clean, unified view.
The impact? Investigators now see the big picture right away. They can quickly spot patterns and connections across institutions that would’ve taken hours, even days, to catch before. This technology makes it faster and more precise to detect complex fraud, freeing investigators to focus on the hunt for evidence instead of chasing down mismatched data. It’s a major win in the fight against fraud.
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5. Maximizing Efficiency Within Resource-Strapped Agencies
U.S. law enforcement agencies, particularly financial crime units within organizations like FinCEN, FBI, and DOJ, face significant resource challenges. Budget cuts and an increasing volume of complex cases mean that these agencies often lack the manpower to investigate every fraud case thoroughly.
AI provides a practical solution by enabling agencies to handle more cases with the same—or even fewer—personnel. For instance, tools like ScanWriter AI, widely used by government investigators, automate labor-intensive tasks such as data analysis and flagging suspicious patterns across financial records. With pre-set prompts tailored to specific case types (like human trafficking or elder fraud), such AI tools support both new and experienced investigators by providing a structured process and highlighting critical insights.
6. Bridging Gaps in Financial Crime Expertise
As financial crime grows more complex, federal agencies face a tough reality: more cases and not enough skilled investigators to handle them. GAO’s last year’s analysis makes it clear—there’s a widening gap in specialized expertise, particularly in complex financial investigations, and the shortage is making it harder to keep up.
AI is stepping in to bridge these gaps. By taking on foundational tasks—handling massive data loads, flagging suspicious patterns, cross-referencing documents—AI enables agencies to manage their investigative workload without bringing in less-experienced hires. This keeps seasoned investigators focused on critical analysis and decision-making, letting them apply their expertise where it truly counts.
And when new hires do join, AI serves as an effective training partner, guiding them through essential tasks. This hands-on experience allows new personnel to contribute sooner and build key skills. Recent DHS pilot programs even demonstrated that AI can “enhance training capacity,” helping recruits become productive team members faster by complementing traditional training.
In short, AI isn’t just filling the skills gap—it’s boosting productivity across the board, equipping agencies to tackle complex investigations effectively, whether they’re understaffed or onboarding new talent.
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7. Adapting to Support Each Investigator’s Unique Approach
Each investigator brings a unique mix of experience and instinct to financial crime cases. Some focus on transaction patterns, while others prioritize account linkages or subtle inconsistencies that could indicate deeper issues. Traditional tools lack the flexibility to adapt to these individual preferences, providing static outputs rather than tailored insights.
AI changes this by evolving over time to align with each investigator’s approach. Initially, it may help with data organization or flagging anomalies, but as it works with an investigator, AI learns from their specific focus areas, gradually refining its insights. As fraud expert Benjamin Chou explains, “AI learns continuously with the user. It may start with data organization; over time, it begins aligning its insights with the investigator’s approach.”
This adaptability transforms AI into a dynamic partner, enhancing each investigator’s strengths. It suggests connections, detects nuances aligned with their focus, and amplifies their expertise without altering their methods.
Given this unique adaptability, it’s clear that AI doesn’t replace the skill of a seasoned investigator—it complements it, acting as an ally finely tuned to their instincts.
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8. Enhancing Data Confidentiality and Compliance
As law enforcement agencies handle sensitive data in fraud investigations, maintaining confidentiality and compliance with regulations is essential. Although AI’s main role is to analyze and process large datasets, many AI solutions come equipped with security features that protect sensitive information.
For example, AI tools often include encrypted data pathways, allowing secure data sharing within and between departments. Features like audit trails also ensure transparency, as every access point and data change is logged, which helps agencies maintain data integrity and accountability. These security components enable agencies to manage sensitive data confidently, adhering to the highest standards in fraud investigations.
While these are just a few of the many reasons to adopt AI in fraud investigations, the technology—known for risks like biases, inaccuracies, and even hallucinations—poses real challenges that have no place in high-stakes investigations. Investigators need tools like ScanWriter AI, they can rely on for both accuracy and transparency, ensuring data integrity and clear insights.
Check Out 3 Modern AI Tools Transforming Financial Fraud Investigations Today!
Trusted by public investigators across the U.S., ScanWriter AI is uniquely designed to meet today’s investigative demands. Read on to learn more.
Why is ScanWriter AI a Breakthrough in Fraud Investigation?
As law enforcement agencies face mounting challenges in combating fraud, the need for advanced solutions has never been greater. That’s where ScanWriter AI comes in. Tailored specifically for public sector investigators, this tool empowers teams to work smarter, not harder, allowing them to quickly sift through large volumes of data with ease.
ScanWriter AI tackles the common hurdles investigators face, like the need for rapid analysis and the demand for reliable findings. Its patented traceability feature lets users track data back to its original source, boosting accountability and transparency every step of the way.
With on-premise deployment, your sensitive information stays secure within your agency’s walls, ensuring data integrity. This powerful, user-friendly solution speeds up investigations, sharpens accuracy, and enhances overall effectiveness, positioning your agency to take the lead in the fight against financial crime.
Ready to see how ScanWriter AI can transform your investigations? Contact us today for a demo and experience the future of fraud detection firsthand!