But rule-based engines have a significant limitation: rules don't adapt quickly to new or evolving types of attacks. Traditionally, rule-based engines are effective at detecting established patterns of fraud-like transactions generated from risky IP addresses or multiple transactions generated within a brief period on a new account. A second challenge is that many fraud monitoring solutions rely on rule-based engines. Fewer than 1 percent of all transactions are fraudulent, so it can take time for a fraud or case management team to sift through potentially fraudulent transactions to identify the fraudulent ones. Mobile fraud is hard to detect and expensive for consumers and banks. With this information, the criminal can impersonate a bank customer, register for mobile access, and immediately generate fund transfers and withdrawals. The criminal then obtains sign-in credentials by using social engineering, phishing, vishing (using a phone to phish), or an infected downloaded app. The phone number is cloned and the criminal receives the SMS notifications and calls sent to the victim's mobile device.

Most mobile fraud occurs when a SIM swap attack is used to compromise a mobile number. Challenges: Rare instances of fraud and rigid rules

The architecture presented here is based on a real-world solution. This article presents a solution that uses Azure technology to predict a fraudulent mobile bank transaction within two seconds. The financial industry is experiencing a 100 percent year-over-year increase in losses caused by access from mobile platforms. As that number increases, mobile bank fraud also increases. More than 800 million people use mobile apps. That's why fraud detection must happen in near real-time. In a typical case of online fraud, the thief makes multiple transactions, leading to a loss of thousands of dollars.

Finderpath app fraud