Automated Credit Risk Assessment Digital Worker
Deploys an 8-agent AI system that orchestrates the complete credit assessment pipeline in seconds. Uses logistic regression for probability of default calculation, Isolation Forest for fraud detection, and a priority-based rules engine for policy compliance.
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Credit application submission form with 4-step wizard capturing personal info, address, employment, and loan details with instant AI processing
Real-time AI pipeline processing view showing multi-agent orchestration with distributed tracing, agent status, and processing metrics
AI assessment results displaying counter offer decision with applicant details, credit profile score, employment verification, and loan terms
Analytics dashboard showing platform performance metrics, risk distribution by tier, AI agent accuracy, and business ROI with 96.2% automation rate
AI Agents
Specialized autonomous agents working in coordination
Orchestrator Agent
Complex credit workflows require coordination across multiple specialized systems with proper sequencing, error handling, and load balancing.
Core Logic
Manages the complete processing pipeline by routing applications to appropriate agents, coordinating inter-agent communication, handling retries and failures gracefully, and maintaining distributed tracing with unique trace IDs for full audit trail visibility. Powered by GPT-4 Turbo for intelligent workflow decisions.
Data Ingestion Agent
Credit bureau data arrives in varying formats across three bureaus with different field mappings, requiring normalization before analysis.
Core Logic
Fetches tri-merge credit reports via bureau APIs, normalizes data structures across Experian, Equifax, and TransUnion formats, implements LRU caching with 94.3% hit rate for repeated lookups, and validates data completeness before downstream processing. Average latency: 347ms.
Feature Engineering Agent
Raw credit data contains hundreds of fields that need transformation into meaningful risk predictors for ML models.
Core Logic
Extracts and transforms 47 features from credit profiles and employment data including payment history scores, utilization ratios, credit age metrics, and debt-to-income calculations. Performs normalization, one-hot encoding, and syncs with central feature store for model consistency.
Risk Scoring Agent
Lenders need accurate probability of default predictions with explainable factors to justify decisions and meet regulatory requirements.
Core Logic
Runs a calibrated logistic regression model (CreditRisk-LR v2.3.1) using weighted FICO methodology. Calculates PD with 95% confidence intervals, assigns risk tiers (Prime, Near-Prime, Subprime, Deep-Subprime), generates SHAP-based feature importance for explainability. Accuracy: 98.7%.
Fraud Detection Agent
Synthetic identity fraud and application manipulation cause significant losses, requiring real-time detection without impacting legitimate applicants.
Core Logic
Deploys Isolation Forest model (FraudDetect-IF v3.1.2) analyzing 8 feature dimensions including device fingerprinting, IP velocity, identity verification scores, and synthetic ID risk indicators. Performs velocity checks across 24-hour windows for IP, device, and SSN patterns. Detection accuracy: 94.3%.
Compliance Agent
Credit decisions must comply with FCRA, ECOA, TILA, CFPB regulations and GLBA privacy requirements, with proper disclosures and audit trails.
Core Logic
Validates decisions against regulatory requirements including fair lending analysis, OFAC sanctions screening, and proper authorization verification. Determines required disclosures (Privacy Notice, Rate Disclosure, Credit Score Disclosure) and ensures audit-ready documentation. Accuracy: 99.9%.
Decisioning Agent
Final credit decisions require evaluation of multiple factors against configurable business policies while optimizing approval rates and risk.
Core Logic
Executes PolicyEngine v4.2.0 rules engine evaluating 12 priority-ordered policy rules across credit, financial, fraud, compliance, and employment categories. Calculates rate stratification with basis point adjustments, structures loan terms to meet DTI thresholds, and generates counter-offers when appropriate.
Document Generation Agent
Approved applications require compliant approval letters and loan agreements, while declined applications need proper adverse action notices with specific reason codes.
Core Logic
Generates decision-appropriate documents using Claude-powered DocGen-LLM v2.0.0. Creates approval letters with Truth in Lending disclosures and loan agreements for approvals, or adverse action notices with credit score disclosures for denials. Prepares documents for e-signature integration.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
4 technologies
Architecture Diagram
System flow visualization