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The financial services sector and the sectors involved in financial transactions suffer from losses and damages associated with fraud. For financial scammers, 2016 was a banner year. The number of clients who witnessed fraud hit a record 15.4 million individuals in the US alone, which is 16 percent higher than 2015. In 2016, fraudsters robbed about $6 billion from banks.
Machine learning allows data scientists to assess effectively which transactions are most likely to be fraudulent, thus reducing false positives dramatically. In fraud prevention and detection, the techniques are extremely effective, as they allow for the automated discovery of trends through large volumes of streaming transactions.
Companies use two approached – Rule based and ML-based fraud detection. In rule-based approach. Rule-based fraud prevention systems mean that specific written rules are generated to “tell” the algorithm which types of operations seem natural and should be allowed, and which should not be because they seem suspicious. Writing rules takes a lot of time, however.
ML techniques demonstrate better performance along with the production of the dataset to which they are equipped, meaning the more fraudulent operations they are educated on, the more they identify fraud. For rule-based systems, this concept does not apply as long as they never develop themselves
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