Our client is a credit union bank and they were on the lookout to integrate fraud detection capability into their core banking solution with data analytics capabilities to monitor, identify, and prevent fraudulent transaction activities.
The developed bank fraud detection & prevention software combines a rules-based decision engine & tracks past transactions for usage patterns to detect anomalies & prevent fraudulent attempts.
The integrated banking fraud detection solution enables real-time monitoring, modification, and addition of fraud detection rules with proactive recommendations.
An essential checkpoint component analyzes in-process transactions and enables real-time blocking of unauthorized card/online transactions based on fraud detection algorithms.
The developed adapter combined with ML capabilities constantly monitors incoming transaction data.
Accelerates investigation with suggestions on suitable action (review and block or discard) on detection of suspicious issues such as repeat transactions, high withdrawal velocity, or a rise in failed transaction rates.
To offer proactive banking fraud analytics, listed below were our objectives;
1) Connecting core banking endpoints as switch gateways
2) Developing analytics engine
3) Seamless integration with the core banking system
Key components of the developed solution include:
For each transaction submitted by the banking solution to the connector application, the connector application establishes a synchronous socket connection with a socket timeout set as per SLA. It parses incoming transaction, validates them, and then sends it for further processing to the stream processing engine.
Our team adopted a hybrid approach that combined rule-based engines, anomaly analysis, mathematical models, and unsupervised machine learning to build a recommendation engine. It would scan the financial data in real-time, highlight suspicious behavior & notify the bank to act in time.
To generate quantitative insights into potential fraud activities, our team created risk and value-based scoring models applying statistical analysis. We also created and applied heuristic rules to identify unusual trends, flag risky data elements, and automatically route suspicious transactions to the case manager.
We ensured zero downtime with an average transaction processing response time of 125 milliseconds while handling 90 million monthly transactions with automatic failover & intelligent clustering.