Financial Risk Analytics and Fraud Analytics involves gathering and storing relevant data and mining it for patterns, discrepancies, and anomalies. These Analytics uses Machine Learning to examine and ensure all the pertinent data regarding a transaction and assigns a risk score to the transaction. Based on the risk score, the model makes a recommendation to allow the transaction, block the transaction, or ask for step-up authentication before allowing the transaction. These operations are in real-time with or without human intervention, providing the financial institution with enhanced fraud prevention without causing undue friction in the customer session.
The Fraud Analytics findings are then translated into insights that can allow a company to manage potential threats before they occur as well as develop a proactive fraud and bribery detection environment.