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Nine Fraud Detection Use Cases Delivered with Near Real Time Analytics
BCT combined transaction streaming, configurable business rules and visual analytics to help the organisation identify and investigate potentially fraudulent fleet-payment activity.
Client
Challenge
Loyalty-card transaction data was distributed across multiple source systems, limiting the organisation’s ability to build a unified view of activity. Existing processes made it difficult to identify suspicious behaviour quickly and investigate patterns across locations, customers, merchants and transaction values.
The organisation needed to monitor transactions in near real time, identify unusual volumes and locations, detect potentially unauthorised card or reward-point usage, and apply risk rules without creating unnecessary alerts. It also needed detailed investigation views, recurring fraud-monitoring reports and a way to refine detection logic as new patterns emerged.
The organisation needed to monitor transactions in near real time, identify unusual volumes and locations, detect potentially unauthorised card or reward-point usage, and apply risk rules without creating unnecessary alerts. It also needed detailed investigation views, recurring fraud-monitoring reports and a way to refine detection logic as new patterns emerged.
Solution
Over a four-month engagement, BCT delivered an integrated fraud-monitoring and analytics solution using TIBCO Streaming, TIBCO Spotfire and SingleStore. Transaction data from multiple sources was continuously captured and monitored through a streaming layer, creating a more complete view of activity.
BCT implemented a dynamic fraud-detection framework with configurable business rules. The framework covered abnormal transaction volumes, excessive card usage, geographically improbable activity, unusual behaviour on inactive accounts and changes in payment behaviour, transaction value, purchase volume, location or product preference.
Spotfire dashboards gave business users drill-down views for investigating flagged transactions and broader behavioural patterns. Automated hourly and daily reports supported recurring monitoring and review. Training and knowledge transfer enabled the in-house team to use the platform and update detection rules as fraud behaviour changed.
BCT implemented a dynamic fraud-detection framework with configurable business rules. The framework covered abnormal transaction volumes, excessive card usage, geographically improbable activity, unusual behaviour on inactive accounts and changes in payment behaviour, transaction value, purchase volume, location or product preference.
Spotfire dashboards gave business users drill-down views for investigating flagged transactions and broader behavioural patterns. Automated hourly and daily reports supported recurring monitoring and review. Training and knowledge transfer enabled the in-house team to use the platform and update detection rules as fraud behaviour changed.
Impact
The organisation completed nine fraud-detection use cases covering transaction volume, timing, location, customer activity, loyalty-card usage, reward-point redemption and merchant behaviour. The use cases gave teams a structured basis for identifying potentially suspicious activity across the fleet-payment programme.
Near-real-time streaming reduced the dependence on fragmented, retrospective analysis. Business users could move from a flagged transaction to detailed analytical evidence through Spotfire dashboards, supporting more informed investigation.
Configurable rules and knowledge transfer gave the in-house team the ability to refine monitoring as transaction behaviour evolved. Automated reports created a regular operating rhythm for review, while the integrated solution established a foundation for future fraud analytics across high-volume loyalty and payment environments.
Near-real-time streaming reduced the dependence on fragmented, retrospective analysis. Business users could move from a flagged transaction to detailed analytical evidence through Spotfire dashboards, supporting more informed investigation.
Configurable rules and knowledge transfer gave the in-house team the ability to refine monitoring as transaction behaviour evolved. Automated reports created a regular operating rhythm for review, while the integrated solution established a foundation for future fraud analytics across high-volume loyalty and payment environments.
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