Real-time fraud detection for a neobank
An AI layer that scores transactions in milliseconds to protect customers without adding friction.
Manual review couldn't keep up with transaction volume, causing fraud losses and false declines.
Research & discovery
- We interviewed stakeholders across the business and mapped every step of the current finance workflow to find where time and money were leaking.
- Competitive benchmarking and user testing revealed the moments of friction that mattered most to real users.
- We defined clear success metrics — including decision latency and fraud loss — before a single screen was designed.
Our approach
Strategy & architecture
We designed a scalable architecture capable of supporting Lumen Finance's growth without a future rewrite.
Design system
A reusable component library ensured consistency, speed and a premium feel across every surface.
Iterative delivery
Weekly demos kept stakeholders aligned and let us course-correct early and cheaply.
Launch & optimize
A zero-downtime rollout with monitoring, followed by data-driven iteration to compound results.
Development & solution
A streaming fraud engine with an explainable ML model, real-time rules and an analyst console.
Before & after
The measurable shift our work delivered.
The results
40ms
Decision latency
−44%
Fraud loss
−19%
False declines
Inside the build
Challenges we navigated
Tight timeline
Delivering enterprise-grade quality within 16 weeks demanded ruthless prioritization and async workflows.
Complex integrations
Connecting NestJS, Python, PostgreSQL required careful data modeling and resilient error handling.
Change management
We built for adoption — onboarding, training and intuitive UX so teams actually embraced the new system.
“The AI agents they deployed now handle most of our support. Transparent, fast and incredibly sharp engineers.”
Priya Nair
VP Product, EduSphere
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