Financial institutions are charged with the task of working against fraud, money laundering and other undesirable activities. Fraud is often characterized in data by subtle deviations in actions.
ML21 is particularly suitable for detecting suspicious transactions. This is largely because our machine learning algorithms have the ability to extract patterns from seemingly unrelated data and help companies better detect fraud. Machine learning becomes even more pervasive with larger data sets, allowing companies to make informed decisions in real time and get clear advice on the risk profile of a given transaction.
USPs of our ML for fraud prevention:
1. Realtime analysis;
2. Clearbox. Our ML explains to you why a transaction is “suspicious.”
3. Rapid implementation;
4. Feedback loop for fast learning curve;