Leveraging AI for fraud prevention in banks

The Problem

More and more people are completing financial transactions online today. Things that people used to buy at retail stores are being purchased online. Detecting fraud from the huge volume of data and transactions happening globally is overwhelming for enterprises. According to McAfee’s latest report, cyber-crime presently damages the global economy $600 billion, or 0.8 percent of global GDP. Fraud is becoming a more and more serious threat to banks and their consumers, costing billions of dollars each year.

Solution

Machine Learning models in Kranium can be used to provide a solution for the problem. The models can be used to analyze huge numbers of transactions in order to uncover fraud trends, which can subsequently be used to detect fraud in real-time.

When fraud is suspected, Kranium’s AI models may be used to reject transactions altogether or flag them for further investigation, allowing investigators to focus their efforts on the most promising instances. In addition, Kranium’s AI models can offer cause codes for the transaction being flagged. These reason codes direct the investigator as to where they should seek to find the faults and aid to speed up the investigation.

AI may also learn from investigators when they evaluate and clear questionable transactions, reinforcing the AI model’s knowledge and avoiding trends that don’t lead to fraud.