When combined with other anti-fraud measures, machine learning improves detection by a factor of five. The more data computers analyze, the better they will become at spotting fraudulent behavior, making it harder for criminals to succeed, writes Jérôme Bovay.
Banks are increasingly turning to specialist FinTechs to deal with fraud for three solid reasons: they understand the problems better than generic IT companies, they fix problems faster - in a fraction of the time - than their larger competitors, and they are better at keeping on top of emerging banking fraud scams, writes Mine Fornerod.
The ability of banks to deal with fraud across Asia Pacific varies hugely depending on culture, the economy and local traditions, leaving the region patchily defended and allowing fraudsters to steal billions of dollars every year. If they are to fight back – and survive – they need the right technology, writes Shabirin Binhan.
Leveraging big data to meet your regulatory and fraud challenges is complex. Your big data initiative needs to be defined, managed and maintained. And just being able to capture the data isn’t enough. Basically, it’s about being able to make sense of the data you gather. You need to know your challenges and make use of the right technology built to target them.
Trying to fight fraud by screening transactions is a losing battle. It’s already happened. A better place to start is with an understanding of who is committing these crimes. This understanding gives organizations the opportunity to implement fraud mitigation strategies that follow fraud patterns to spot red flags and potentially catch thieves in the act.