Real-time fraud intelligence for an enterprise-scale digital finance team.
A risk-scoring and review workflow designed for high transaction volume, explainable decisions and secure integration with existing banking operations.
Risk operations moved from reactive review to real-time fraud intelligence.
The platform was designed around practical risk operations: fast event intake, explainable scoring, review queues and leadership visibility.
The business problem
we had to solve.
The client needed to reduce manual review pressure, detect new fraud patterns earlier and keep decision latency low enough for customer-facing financial workflows.
The solution WebSenor delivered
WebSenor built a modular scoring service with event ingestion, model-assisted risk bands, rules fallback, reviewer queues, audit trails and dashboards for operations and leadership.
- AI/ML architecture
- Backend API development
- Data pipeline engineering
- Security-aware QA
- Operational dashboards
From audit
to production rollout.
Discovery and risk audit
Reviewed workflows, event sources, reviewer queues and decision latency requirements.
Feature and rule design
Defined risk bands, data points, fallback rules and explainability requirements.
Scoring service build
Built API-first scoring and audit flow for integration with existing systems.
Shadow rollout
Tested decisions against production-like data before wider release.
Monitoring and iteration
Added dashboards, alerts and review feedback loops.
The system,
end to end.
Event ingestion
Transaction and account events normalized for scoring.
Risk scoring API
Model-assisted decision service with rules fallback.
Reviewer console
Queue, notes, decision history and escalation flow.
Analytics layer
Dashboards for risk, product and leadership teams.
What the client team
uses every day.
Risk dashboard
Live view of flags, segments and decision reasons.
Reviewer queue
Prioritized alerts with case notes and audit history.
Customer step-up flow
Optional verification path for suspicious activity.
Model governance panel
Release notes, eval history and drift indicators.
Built for measurable,
enterprise-ready impact.
Faster triage for suspicious activity
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Cleaner handoff between automated scoring and human review
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A scalable foundation for future ML models and compliance reporting
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Better visibility into risk drivers for product and operations teams
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Technology used
in production delivery.
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