Different fraud methods are emerging with the developing technology. Detection of these methods with traditional data analysis is almost impossible.
On the other hand, every detail that seems insignificant causes serious losses for the banking and financial sectors. Globally large companies are now positioning graph analytics as their biggest weapon to reveal relationships in the detection of fraudulent activities carried out by organized groups. However, this transformation has not been achieved in our country yet. If we look at the damages of fraudsters in the banking and financial sectors in more detail on a global scale, we will understand how the correct analysis method will contribute to financial institutions.
As of 2018, there has been a significant increase in organized fraud networks that use more sophisticated methods, including bots, to automate attacks. In fact, by 2022, financial institutions are planning to spend $ 9.3 billion a year on fraud detection and prevention. Even if only considering the online payment fraud, it is predicted that these activities will cause 130 billion dollars in damage to institutions by 2023. According to the latest report of LexisNexis, fraud attempts against financial institutions have increased significantly last year and the fraud success rate reached 85%. These statistics indicate that fraudulent activities cause serious financial damage to financial institutions and demonstrate that these companies should pay particular attention to fraud detection and prevention. Indeed, five of the ten largest global banks and two of the world’s largest payment card companies have turned to graph analytics to fight fraud. For this purpose, PayPal, eBay, and StubHub use graph techniques to perform fraud detection on their transactions. International Data Corporation (IDC) estimates that PayPal saved more than $ 700 million with this method.
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