Artificial intelligence (AI) is something you might be familiar with or have even used in the workplace. What about Gen AI (or generative AI)? Some well-known examples of Gen AI systems include ChatGPT and DALL-E, which use raw data to generate new material (including text, graphics, code, and video).
Gen AI may also help reduce, uncover and fight fraud. But utilizing these technologies could lead to ethical and legal complications.
Digital Decisions
Fraud can occur when a perpetrator finds one or more weaknesses in a company’s defenses and exploits them, creating patterns of activity. To keep any losses to a minimum, you must find patterns fast. Traditional fraud detection engines use rules as their backbone, using them to identify unusual activity. A fraud investigator is notified when the system identifies suspicious activity, such as a questionable transaction, and evaluates whether or not the activity constitutes fraud.
In order to detect fraud, AI-powered systems look for behaviors that match previously identified fraud schemes. They then can adapt and create new rules in response to new trends and patterns. Therefore, AI can detect suspicious transactions in real-time as criminals update their tactics, in addition to detecting tried and true fraud techniques.
Gen AI can automatically approve or reject potentially fraudulent transactions, reducing the need for investigators to manually analyze them. An organization’s policies and procedures, along with the technology’s capacity to evaluate and learn from real-time data, can be used by the system to create “digital decisions”.
Hidden Connections
Fighting fraud calls for the precise and rapid analysis of massive datasets. Without advanced technology, fraud detection can quickly become a laborious and error-prone process. A common problem for fraud investigators is the number of “false positives” (transactions that seem fraudulent but are actually genuine). Investigator fatigue also plays a role. However, Gen AI guarantees consistent treatment of suspicious transactions while minimizing false positives.
Moreover, it has the ability to reveal outliers that could otherwise stay hidden. For example, consider a string of fraudulent transactions that were carried out over an extended period using accounts associated with the same street address. If they happen over a long period of time—months or even years—it would be difficult for a person to notice them. Gen AI is able to identify patterns and flag repeat transactions that follow a similar pattern.
A fraud detection system has to be able to keep up with the constantly evolving nature of fraud in order to be effective. Gen AI helps by 1) anticipating how a fraud scheme might evolve and then building rules to detect its occurrence and 2) using established schemes to produce synthetic data to train and optimize a fraud detection solution. Tasking Gen AI with developing synthetic data ensures your company remains at the cutting edge of fraud prevention.
Problems with Ethics
Gen AI legislation is in a constant state of flux, so businesses may have to wait a while for definitive rules. Unintentional consequences, such as implicit prejudice and privacy breaches, are possible with any technology. Find out how Gen AI stores and retrieves information before you use it. Inquire about the measures your solution provider takes to safeguard your data from prying eyes, as well as its compliance with existing laws and regulations and its strategy for meeting future regulatory requirements.
If you have questions about how fraud investigators use Gen AI, contact us.
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