Real-time Bank Fraud Detection & Prevention Software

About

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.

Capability

Data Analytics

Industry

FinTech

Country

Canada

Key Features

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.

Fraud prevention framework

The integrated banking fraud detection solution enables real-time monitoring, modification, and addition of fraud detection rules with proactive recommendations.

Deep insights on fraud patterns

An essential checkpoint component analyzes in-process transactions and enables real-time blocking of unauthorized card/online transactions based on fraud detection algorithms.

Real-time transaction screening

The developed adapter combined with ML capabilities constantly monitors incoming transaction data.

Automated alerts

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.

Challenges

Limited visibility into payment processing across applications
The limited window for fraud discovery on transactions occurring at a specific terminal
Inability to handle a high volume of unexpected fallback transactions
Minimal transparency and traceability for unexpected transaction scenarios

Solution

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:

Connector application

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.

Automated analytics 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.

Predictive rule model

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.

Load balancer

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.