AI Credit Assessment System
Deploys a multi-agent AI orchestration system where five specialized agents analyze transaction patterns, business performance, alternative data sources, and risk factors in parallel. Agents synthesize findings into a composite credit score with 30-second assessment turnaround, enabling instant loan decisions.
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
AI Agents
Specialized autonomous agents working in coordination
Transaction Analysis Agent
Lenders lack visibility into actual cash flow patterns and revenue consistency of informal businesses without traditional banking statements.
Core Logic
Analyzes 6 months of transaction history to detect revenue patterns, calculate average monthly income, identify seasonal trends, and evaluate transaction velocity. Processes 487+ transactions to generate income stability metrics and payment behavior scores. Contributes 35% weight to composite score with typical scores of 85-90/100.
Business Intelligence Agent
Individual business assessment lacks context of market positioning and peer performance benchmarks for accurate risk calibration.
Core Logic
Evaluates business model viability by benchmarking against 1,200+ similar businesses in the same sector. Analyzes market positioning, growth trajectory, competitive advantages, and industry-specific risk factors. Calculates peer percentile ranking and sector performance deviation. Contributes 25% weight with typical scores of 80-85/100.
Alternative Data Agent
Credit-invisible borrowers with no formal credit bureau records cannot demonstrate creditworthiness through traditional documentation.
Core Logic
Mines non-traditional data sources including utility payment history (electricity, water, telecom), digital footprint analysis (social media presence, online reviews), and supplier relationship assessment. Tracks payment punctuality rates and business reputation signals. Contributes 20% weight with typical scores of 88-92/100 for consistent payers.
Risk Assessment Agent
Manual risk evaluation misses subtle fraud indicators and fails to quantify default probability with statistical precision.
Core Logic
Executes multi-model risk profiling combining fraud detection algorithms, default probability calculations, and anomaly detection. Computes risk metrics including 8.2% baseline default probability, fraud indicator flags, and exposure-at-default estimates. Runs velocity checks and pattern deviation analysis. Contributes 20% weight with typical scores of 90-94/100.
Decision Synthesis Agent
Disparate agent outputs require intelligent aggregation to produce coherent, explainable lending decisions with confidence bounds.
Core Logic
Waits for all specialist agents to complete (activates at T+20s), then synthesizes insights using weighted scoring algorithms. Calculates composite credit score, generates confidence percentages, determines final decision (APPROVED/UNDER_REVIEW/DECLINED), computes loan terms (amount, rate, monthly payment), and produces human-readable decision reasoning. Typical scores of 86-90/100.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
6 technologies
Architecture Diagram
System flow visualization