Use case · Financial Services

Real-time fraud detection

Streaming risk scoring that flags suspicious payments and account activity in milliseconds, with explainable signals that fraud analysts and model risk teams can review.

The challenge

What organisations face

Rules-based fraud engines struggle with instant payments, synthetic identities and fast-moving attack patterns. Static thresholds generate high false-positive volumes, frustrating genuine customers and overloading investigation teams, while new fraud typologies slip through until rules are manually rewritten. Risk committees also need to understand why a transaction was blocked before trusting machine learning.

The solution

What VulcanTech engineers

VulcanTech would engineer an event-streaming pipeline that enriches each transaction with device, behavioural and network features, scores it against gradient-boosted and graph-based models, and returns a decision within the payment window. Reason codes accompany every score. A feature store, champion-challenger deployment, drift monitoring and an analyst case console complete the platform, with full decision logging for audit.

Expected outcomes

What changes for the business

  • Fewer genuine customers declined at the point of payment
  • Analyst effort focused on the highest-risk alerts
  • Explainable decisions ready for model risk review

Capabilities

Service lines involved

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