{"id":2359,"date":"2018-08-30T00:00:00","date_gmt":"2018-08-30T00:00:00","guid":{"rendered":"https:\/\/azure.microsoft.com\/blog\/two-seconds-to-take-a-bite-out-of-mobile-bank-fraud-with-artificial-intelligence"},"modified":"2025-04-29T15:30:59","modified_gmt":"2025-04-29T22:30:59","slug":"two-seconds-to-take-a-bite-out-of-mobile-bank-fraud-with-artificial-intelligence","status":"publish","type":"post","link":"https:\/\/azure.microsoft.com\/en-us\/blog\/two-seconds-to-take-a-bite-out-of-mobile-bank-fraud-with-artificial-intelligence\/","title":{"rendered":"Two seconds to take a bite out of mobile bank fraud with Artificial Intelligence"},"content":{"rendered":"

The future of mobile banking is clear. People love their mobile devices and banks are making big investments to enhance their apps with digital features and capabilities. As mobile banking grows, so does the one aspect about it that can be wrenching for customers and banks, mobile device fraud.<\/p>\n

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Problem: To implement near real-time fraud detection<\/h2>\n

Most mobile fraud occurs through a compromise called a SIM swap attack in which a mobile number is hacked. The phone number is cloned and the criminal receives all the text messages and calls sent to the victim\u2019s mobile device. Then login credentials are obtained through social engineering, phishing, vishing, or an infected downloaded app. With this information, the criminal can impersonate a bank customer, register for mobile access, and immediately start to request fund transfers and withdrawals.<\/p>\n

Artificial Intelligence (AI) models have the potential to dramatically improve fraud detection rates and detection times. One approach is described in the Mobile bank fraud solution guide.\u00a0 It\u2019s a behavioral-based AI approach and can be much more responsive to changing fraud patterns than rules-based or other approaches.<\/p>\n

The solution: A pipeline that detects fraud in less than two seconds<\/h2>\n

Latency and response times are critical in a fraud detection solution. The time it takes a bank to react to a fraudulent transaction translates directly to how much financial loss can be prevented. The sooner the detection takes place, the less the financial loss.<\/p>\n

To be effective, detection needs to occur in less than two seconds. This means less than two seconds to process an incoming mobile activity, build a behavioral profile, evaluate the transaction for fraud, and determine if an action needs to be taken. The approach described in this solution is based on:<\/p>\n