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Fraud detection is a challenging problem. The fact is that fraudulent transactions represent a very small fraction of activity within an organization. The challenge is that a small percentage of activity can quickly turn into big losses without the right tools and systems in place.
Criminals are crafty. As traditional fraud schemes fail to pay off, fraudsters have learned to change their tactics.
The good news is that with advances in machine learning, systems can learn, adapt and uncover emerging patterns for preventing fraud.
The customer, a large insurance company, needed to identify potential fraudulent users.
To keep the number of frauds as small as possible it is important to detect frauds right at the beginning. Data Reply faced the challenge to separate fraudulent users from honest ones, so that no righteous person would be suspected to be guilty of an offence.