AI and data analytics present powerful tools for law enforcement and investigators to combat fraud by analyzing vast datasets, uncovering hidden patterns, and enhancing decision-making and predictive abilities
Financial fraud — a widespread and costly issue — manifests itself in various forms, including identity theft, credit card and insurance fraud, cybercrime, money laundering, and tax evasion. Now, however, there may be a more novel tool that can help law enforcement and investigators fight fraud: Artificial intelligence (AI).
Indeed, conducting Ai-driven data analytics can help law enforcement and investigators identify patterns, trends, anomalies, and correlations in large and complex data sets that may indicate fraudulent activity. Data analytics also can help law enforcement organizations automate and streamline work tasks that are tedious, time-consuming, or prone to human error, such as data cleaning, validation, and visualization.
Data analytics joins the fraud fight
Generative AI (GenAI) and the use of large language models (LLM) — with which AI systems can generate natural language text based on a given input, such as a word, a phrase, or a prompt — can further power up law enforcement efforts. For example, LLMs can be used for various natural language processing tasks, such as text summarization, translation, question answering, sentiment analysis, and text generation. In this way, GenAI and LLMs can help law enforcement and investigators extract and synthesize information from various sources, such as police reports, documents, emails, social media, and online forums. LLMs can also help generate natural and engaging content that can be used for education, awareness, and prevention campaigns with the public.
Marc Evans, the founder of Fraud Hero, a firm specializing in fraud consulting and training services, is one of the proponents of employing AI and data analytics for fraud prevention and detection. With more than 12 years of experience in law enforcement and fraud investigation, Evans asserts that AI can serve as a formidable tool in combating fraud and other crimes.
“There are AI applications commonly discussed by the public, such as facial recognition, object recognition, and license plate readers. However, a significant use of AI, particularly beneficial for law enforcement, is analyzing vast datasets because crimes often recur, sometimes in varying locations or across different types of crime,” Evans says, adding that this recurrence is frequently observed in fraud cases due to convenience, ease, or a variety of other factors.
Evans also mentioned some of the tools and platforms that can be used by investigators, such as OpenAI’s ChatGPT, Microsoft’s Co-pilot, Google’s Gemini, Perplexity, and other paid AI options that are based on language models. “These are the language learning models everyone thinks of when they think of AI, but they forget about how pattern recognition and AI can be used for data analytical functions,” he explains.
Leveraging the AI toolbox
Certain types of fraud cases, such as those involving money laundering for example, depend a great deal on AI-driven analysis, Evans says, noting that, given the complexity of bank statements and other detailed documents, AI can categorize financial accounts into various useful segments. Further, Ai-driven analysis facilitates cross-referencing transactions, enabling the tracking of monetary flows into and out of accounts, which is particularly valuable when monitoring cryptocurrencies. The potential applications of this innovative technology are extensive, especially when considering the various forms of fraud that AI could help address.
“One significant use of AI that’s particularly beneficial for law enforcement, is analyzing vast datasets because crimes often recur, sometimes in varying locations or across different types of crimes.”
Consider a scenario in which someone falls victim to an investment scam and transfers their funds using cryptocurrency. Previously, investigators like Evans had to painstakingly comb through extensive spreadsheets and documents when addressing large-scale financial fraud. Now, with the aid of advanced analytics, robust regulation, and collaborative efforts, unraveling the true account holders becomes more feasible. Additionally, identifying the beneficiaries increases the chances of restoring the funds to their rightful owner. Although this may seem aspirational, it represents a tangible advance in fraud resolution practices.
Evans emphasized the importance of data analytics for fraud investigation, and he said that arresting fraudsters is not enough to solve the problem. “You can’t arrest your way out of fraud, you have to investigate your way out of it,” he explains. “You need preemptive data analytics, so you are prepared to see where the money is going and who the bad actors are.” By analyzing large quantities of data — from historic crimes to current transactions — can help to predict when the next problem will occur and make it easier to solve current issues and prevent future losses, he says.
AI-driven data analytics can offer law enforcement and investigators significant benefits as they deal with fraud and other crimes. AI can help them collect, process, analyze, and interpret large and complex data sets, and generate insights, predictions, and recommendations that can improve their decision-making. Further, AI can help them create realistic and convincing synthetic data that can be used for training, testing, and validating AI models, and for creating scenarios and simulations that can help them anticipate and prepare for potential fraud risks and threats. AI can also allow them to extract and synthesize information from various sources and generate natural and engaging content that can be used for education, awareness, and prevention campaigns.
In fact, by using AI and data analytics, law enforcement and investigators can enhance their capabilities and performance, and ultimately, reduce the heavy financial impact and cost of fraud on individuals, businesses, and governments.
You can find more about law enforcement tactics to fight fraud here.
