Financial fraud has wide-reaching implications. Fraudulent transactions are rare, they represent a small fraction of activity within an organization, but this small percentage of activity can turn into large losses for an organization. Criminals have been changing their tactics, making it hard for traditional systems to find fraud incidences.
Through AI and Machine Learning algorithms it is possible to analyze historical transaction data to build a model that can detect fraudulent patterns. Using AI algorithms to drive fraud detection, it is possible to significantly reduce the cost of investigating false positives, increasing the overall efficiency within a bank and allowing for substantial savings. AI can automate many manual processes in the investigation process, freeing up time and humans for analyzing real cases.