Scenario 2: Multi-Agent Credit Decisioning Platform

Core Logic

The Data Ingestion Agent orchestrates connections to multiple credit bureaus (Experian, Equifax, TransUnion) via APIs to retrieve comprehensive credit reports including payment history, account details, and credit scores. It integrates with KYC providers for identity verification, validating applicant name, employer, and address against authoritative sources. The agent performs data normalization to standardize formats across sources, applies PII tokenization for SSN and sensitive data protection, validates data completeness checking for missing fields, and produces unified applicant profiles ready for feature engineering. Reasoning traces show step-by-step progress: initiating data collection, pulling credit reports, performing KYC verification, and completing data normalization.

Core Logic

The Feature Engineering Agent connects to Feast Feature Store to load standardized feature definitions from the credit_risk_v3 feature set containing 147 features. It computes credit features including credit utilization percentage, debt-to-income ratio, and payment velocity metrics. Behavioral features analyze application source patterns, session duration, and form completion rates. The agent applies feature versioning (v3.2.1) to ensure model compatibility, handles missing value imputation with documented strategies, assembles final feature vectors for model inference, and validates feature values against expected ranges. Reasoning traces demonstrate: loading feature definitions, computing credit features with specific calculations, computing behavioral features from application metadata, and assembling validated feature vectors.

Core Logic

The Risk Scoring Agent loads production ML models (CreditRisk-XGBoost-Ensemble v3.2.1) from MLflow model registry, fetching from mlflow://models/credit-risk/production. It runs ensemble inference combining XGBoost and LightGBM predictions with weighted averaging. The agent calculates probability of default with confidence intervals, assigns risk tier classification (Super Prime for 740+, Prime for 670+, Near Prime for 620+, Subprime for 580+, Deep Subprime below 580), generates SHAP explanations computing feature importance percentages (Credit Score 35%, DTI Ratio 22%, Employment Tenure 15%, etc.), and produces model explainability data for regulatory compliance. Risk assessment output includes risk tier, probability of default, expected loss calculation, risk score (0-100), FICO-style breakdown of contributing factors, and model version with confidence intervals.

Core Logic

The Fraud Detection Agent runs in parallel with Risk Scoring to minimize latency. It loads Isolation Forest models for outlier detection and Graph Neural Networks for relationship pattern analysis. The agent performs velocity checks tracking applications per day, same-device applications, and IP risk scores (detecting VPN/proxy usage). Identity verification scoring validates consistency of applicant information across sources. Device risk scoring analyzes device fingerprints for suspicious patterns. Behavioral scoring evaluates application completion patterns against normal distributions. The agent detects and flags suspicious patterns including synthetic identity indicators (credit file inconsistent with claimed history), VPN/proxy usage, thin credit files with high income claims, and application velocity anomalies. Output includes fraud risk level (CRITICAL to MINIMAL), fraud score (0-100), anomaly score, detailed pattern descriptions with confidence levels, and detection method attribution.

Core Logic

The Underwriting Agent loads the credit policy rules engine with 156 active rules from policy version 2024-Q4. It evaluates credit policies against defined thresholds: Credit Score vs 620 minimum, DTI ratio vs 43% maximum, Annual Income vs $24,000 minimum. The agent applies risk-based pricing calculation starting with 6.99% base rate and adding risk premium (0% for Super Prime, 2% for Prime, 4.5% for Near Prime, 7.5% for Subprime, 12% for Deep Subprime). It generates decision outcome (approved, declined, manual_review) with confidence percentage, calculates approved terms including approved amount, interest rate, term months, monthly payment, total interest, and total repayment. The agent produces decision explanations with primary decision factors and regulatory decline codes when applicable. Reasoning traces show: loading policy rules, evaluating credit policies with specific pass/fail results, calculating risk-based pricing breakdown, and making final decision with confidence.

Core Logic

The Compliance Agent initiates comprehensive compliance checks loading regulatory requirements for TILA, ECOA, and FCRA. It performs fair lending analysis checking for disparate impact by analyzing decisions across protected classes, validating statistical parity, and documenting disparate treatment prevention measures. The agent validates APR calculations per TILA requirements, ensures proper credit report authorization per FCRA, and generates required disclosures: for approvals - TILA Disclosure, Privacy Notice, Fee Schedule; for declines - Adverse Action Notice, Consumer Rights documentation. Adverse action notices include notice type, primary and secondary decline reasons with regulatory codes (e.g., FCRA-615), credit bureau identification, credit score with score range context, complete consumer rights list, and appeal process instructions. Reasoning traces demonstrate: initiating compliance checks, performing fair lending analysis with pass results, validating regulatory requirements, and generating appropriate disclosures.

Core Logic

The Document Generation Agent loads Claude 3.5 Sonnet (Legal Fine-tuned) for document creation with 12 legal templates specific to US Federal jurisdiction. For approvals, it generates Loan Agreement, Promissory Note, and TILA Disclosure documents with all required terms, conditions, and disclosures. For declines, it generates Decision Letter, Adverse Action Notice, and Appeal Instructions with proper regulatory language. The agent performs AI-assisted legal review validating language compliance, required disclosure completeness, and signature block configuration. Final document assembly produces PDF documents ready for digital signature with delivery via Email and Portal. Reasoning traces show: loading document templates, generating appropriate documents based on decision outcome, performing legal review validation, and finalizing document package for delivery.