{"id":6582,"date":"2026-03-30T06:30:00","date_gmt":"2026-03-30T04:30:00","guid":{"rendered":"https:\/\/hub.hslu.ch\/economiccrime\/?p=6582"},"modified":"2026-03-30T11:58:27","modified_gmt":"2026-03-30T09:58:27","slug":"ai-fraud-how-artificial-intelligence-reshapes-prevention-detection-and-investigation","status":"publish","type":"post","link":"https:\/\/hub.hslu.ch\/economiccrime\/ai-fraud-how-artificial-intelligence-reshapes-prevention-detection-and-investigation\/","title":{"rendered":"AI Fraud: How Artificial Intelligence reshapes prevention, detection and investigation"},"content":{"rendered":"\n

von Patrick Wellens<\/a><\/p>\n\n\n\n

The Association of Certified Fraud Examiners<\/a> (ACFE) mentions in their annual Report to the Nation that organizations lose between 5% and 6% of their annual revenue to fraud. The typical fraudster is male, aged between 31 and 50, while individuals over 50 are responsible for the highest median losses. The ACFE also developed the \u201cfraud tree\u201c and the Cressey Fraud Triangle<\/a> (comprising opportunity, incentive\/pressure, and rationalization), a widely used framework among investigators and compliance officers to understand why individuals commit fraud.<\/p>\n\n\n\n

With the widespread use of Artificial Intelligence, the question is whether fraud losses as a percentage of revenue will increase, whether the fraudster profile is likely to change and whether the fraud triangle framework remains a valid model to explain fraudulent behavior. What can compliance officers and investigators do better in preventing, detecting and investigating AI-enabled fraud?<\/p>\n\n\n\n

AI and the fraud triangle<\/strong><\/h2>\n\n\n\n

AI dramatically expands the opportunity for fraud.<\/strong> AI enables fraudsters to effortlessly generate falsified travel and expense receipts (hotel invoices, restaurant bills, taxi receipts etc.), false or fake supplier invoices, manipulated images (to support insurance fraud), and fabricated documents (company records, contracts, emails etc.). In addition, AI allows them to generate deepfake voice\/video impersonating other people.<\/p>\n\n\n\n

The arguments to rationalize fraud are also shifting<\/strong> with AI. Traditionally, fraudsters justified their actions with arguments such as \u201cI worked really hard – I deserve this\u201d, \u201cupper management is doing it as well\u201d, or \u201cI was treated unfairly\u201d. With AI, the rationalization becomes delegated to technology (e.g., \u201cAI did it,\u201d \u201cI just executed what the system suggested,\u201d \u201cno one is directly harmed,\u201d \u201cit is just an algorithm\u201d etc.).<\/p>\n\n\n\n

AI does not change the pressure for fraud, however it lowers the effort and skill required to act fraudulently. <\/strong>Considering that, an increase in fraud incidents can be expected, unless companies strengthen their preventive and detective efforts.<\/p>\n\n\n\n

AI and the fraudster <\/strong>profile<\/h2>\n\n\n\n

Before Artificial Intelligence, fraudsters were employees with university degrees, long tenure within the organization and often had substantial expertise in accounting, IT or operational processes.<\/p>\n\n\n\n

The classic profile of a fraudster according to ACFE required access and skill, fraudsters were personally involved in the execution of fraud, fraud schemes developed gradually, and often behavioral red flags surfaced. Artificial Intelligence fundamentally disrupts this profile.<\/p>\n\n\n\n

With AI, a much broader population of employees can engage in fraud, even without specialized skills.  The natural language model of AI removes technical barriers to commit fraud. External actors can impersonate insiders and therefore no longer need to be long-tenured employees.<\/p>\n\n\n\n

Adjustments in fraud prevention, detection and investigation<\/strong><\/h2>\n\n\n\n

Fraud prevention<\/strong><\/h3>\n\n\n\n

Compliance officers in companies can implement the following measures to be more effective in the prevention of AI-related fraud:<\/p>\n\n\n\n