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. The system optimizes for both operational efficiency and effective risk management.
Technical Solution
Architecture
We designed a high-performance architecture built on a cutting-edge BPMN workflow engine, featuring:
- Visual workflow representation enabling clear conversations with business stakeholders and regulators
- Real-time monitoring and observability for comprehensive system visibility
- Model serving infrastructure integrated within the workflow
- Decision rule engine visualized as flow nodes for applying regulatory and business policies
- Comprehensive audit trails for compliance and debugging
- Explainability module for transparent decision rationale
- Bias detection and mitigation for fair lending practices
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
- Champion/challenger testing framework for continuous improvement
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
- Balancing approval rates with risk management
- Ensuring fairness across demographic groups
Business Impact
The system delivered substantial benefits across multiple dimensions:
Speed & Efficiency Metrics
- Daily Decisions: Over 100,000 loan applications processed daily
- Response Time: Less than 300ms average decision latency
- System Uptime: 99.99% availability (less than 5 minutes downtime monthly)
- Throughput: Peak capacity of 15,000 decisions per minute
- Operational Efficiency Ratio: Reduced from 0.62 to 0.48
Accuracy & Effectiveness Metrics
- Loss Reduction: 25% decrease in credit losses
- Kolmogorov-Smirnov Statistic: 0.72, demonstrating strong separation of good/bad risks
- Expected Calibration Error: 0.037, indicating well-calibrated probability estimates
- Mean Absolute Error: 2.1% for repayment predictions
- Coefficient of Determination (R²): 0.83 for risk models
- Normalized RMSE: 0.16 for financial impact predictions
- Concordance Index: 0.88 for risk ranking accuracy
- Manual Override Rate: Only 3.2% of decisions require human review
Compliance & Fairness Metrics
- Compliance Rate: 100% adherence to regulatory requirements
- Decision Explainability: 98% of decisions with clear factor attribution
- Adverse Action Compliance: 100% of rejections with valid reasons
- Demographic Parity: Less than 2% approval rate variance across protected groups
- Model Documentation: Full compliance with SR 11-7 regulatory requirements
Business Outcome Metrics
- Net Interest Margin: Increased from 7.6% to 8.3%
- Customer Satisfaction: 91% positive rating for decision speed
- Cost of Funds: Reduced by 0.4 percentage points
- Customer Acquisition: 17% increase in approved applications
- Lifetime Value: 22% improvement in average customer LTV
Evaluation Methods
The system’s performance is continuously assessed through:
- Shadow Testing: Parallel running with existing decision process
- Champion/Challenger Testing: Ongoing testing of model variants
- Continuous Monitoring: Real-time KPI tracking in production
- A/B Testing: Controlled experiments for new features
- Customer Feedback Analysis: Regular surveys on decision experience
- Regulatory Audits: Quarterly independent compliance reviews
Technology Stack
- BPMN workflow engine for process orchestration and visualization
- Java for core services and workflow implementation
- Python for machine learning and analytics components
- Monitoring and observability framework integrated with workflow
- Custom explainability framework
- SHAP values for feature importance
- Regulatory reporting automation