Increase of online transactions, interactions and data exchange created an opportunity for fraud.
Innovations in AI have allowed AI software to detect potential fraudulent threats before an attempt to defraud is made. Using algorithms and deep-learning models, AI can identify behaviors linked to ex-ante fraudulent actions. AI uses risk potential analysis and historical data analysis to understand when fraud is likely, and who is likely to attempt it.
Using data analysis technologies, AI reviews past and present data patterns to develop a risk analysis for a potential client in minutes. It can then generate reports explaining the risk potential score, and detail client risk factors. The model can be trained and designed based on company-specific needs and allowable levels of risk. It can do all of this at the click of a mouse, and automation can be applied to keep a constantly updated risk analysis at the user’s fingertips.
AI for fraud detection can drastically reduce the resources needed to properly and objectively analyze a client’s risk potential, using the most updated information from the client’s AI-generated profile. This constant transcription and analysis of data results in less than 3 percent predictability error and can amount to more than 60 percent loss reduction overall.