Real-time Decision Engine
Challenge
A fintech company needed a high-performance system to make lending decisions in milliseconds while maintaining accuracy.
Solution
We built a real-time decision engine that processes a substantial volume of daily lending requests with sub-300ms latency. The system integrates multiple ML models for credit risk assessment and fraud detection.
Results
The system significantly reduced credit losses while maintaining high throughput and reliability. It processes tens of thousands of decisions daily with excellent uptime and full regulatory compliance.
Project Overview
This real-time decision engine helps a fintech lender make instant credit decisions with high accuracy, low latency, and full compliance with regulatory requirements.
Technical Solution
Architecture
We designed a high-performance architecture featuring:
- Horizontally scalable API layer for handling incoming requests
- In-memory data grid for ultra-fast access to critical data
- Model serving infrastructure optimized for low-latency inference
- Decision rule engine for applying regulatory and business policies
- Comprehensive monitoring and logging for compliance and debugging
Model Development
The solution incorporates multiple specialized models:
- Credit risk assessment using gradient boosted decision trees
- Fraud detection using neural networks
- Income verification using document classification models
- Affordability calculation using custom statistical models
Implementation Challenges
The project required solving several complex problems:
- Achieving sub-300ms response times with complex model inference
- Building redundancy to ensure 99.99% uptime
- Implementing thorough model monitoring for regulatory compliance
- Creating explainable decisions for regulatory requirements
- Supporting high throughput during peak traffic periods
Business Impact
The system delivered substantial benefits:
- Significant reduction in credit losses through improved risk assessment
- Ability to serve a large volume of customers daily with instant decisions
- Full regulatory compliance with decision explainability
- Ability to deploy new models and rules without downtime
- Detailed analytics on approval rates and risk factors
Technology Stack
- Kubernetes for containerized deployment
- Redis and Aerospike for in-memory data
- NVIDIA Triton for optimized model serving
- Python and Java for core services
- Prometheus and Grafana for monitoring
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