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| title: GenAI Loan Advisor | |
| emoji: 🏦 | |
| colorFrom: blue | |
| colorTo: green | |
| sdk: docker | |
| app_port: 7860 | |
| pinned: false | |
| # 🏦 GenAI Loan Advisor | |
| A robust Multi-Agent System built with **CrewAI** and **Mistral** that automates the bank loan underwriting process. This system executes a strict sequential pipeline to retrieve customer data, consult internal policy documents (RAG), and determine risk levels with zero human intervention. | |
| ## 🏗️ Architecture: The "Plan & Execute" Model | |
| To prevent "infinite thinking loops" common in pure hierarchical systems, this project splits the workflow into two distinct phases: | |
| USER QUERY: "Can Andy get a loan?" | |
| │ | |
| ▼ | |
| ┌───────────────────────────────────────┐ | |
| │ PHASE 1: THE BRAIN (Strategy) │ | |
| │ Agent: Senior Context Analyst │ | |
| │ "I see a loan request. I will │ | |
| │ activate the Application Pipeline." │ | |
| └───────────────┬───────────────────────┘ | |
| │ Pass JSON Plan | |
| ▼ | |
| ┌───────────────────────────────────────┐ | |
| │ PHASE 2: THE MUSCLE (Execution) │ | |
| │ (Strict Sequential Assembly Line) │ | |
| │ │ | |
| │ 1. DATA AGENT ──▶ Fetches PII & Score│ | |
| │ (Output: Score 780, Good Status) │ | |
| │ │ | |
| │ 2. RAG AGENT ──▶ Checks Policy │ | |
| │ (Output: "750+ is Low Risk") │ | |
| │ │ | |
| │ 3. JUDGE AGENT ──▶ Makes Decision │ | |
| │ (Output: "Approved @ 3.175%") │ | |
| │ │ | |
| │ 4. RESOLUTION AGENT ──▶ Final Report │ | |
| └───────────────────────────────────────┘ | |
| Key Features | |
| Smart Orchestration: The "Strategy Agent" acts as a Manager effectively by deciding the intent before the crew starts, eliminating the risk of the AI getting confused during execution. | |
| Whole-Sheet RAG: Implements a specialized 1500-character chunking strategy to preserve entire "Risk Matrix" tables from PDF policies, preventing the AI from seeing fragmented data. | |
| Hallucination Guardrails: The Data Agent is strictly bound to real customer lookups, preventing the invention of fake customers (like "John Doe"). | |
| Professional Reporting: The Resolution Agent ensures the final output is a clean, client-facing communication. | |
| Project Structure | |
| Quick Start | |
| 1. Environment Setup | |
| Create a .env file in the root: | |
| MISTRAL_API_KEY=your_api_key_here | |
| CODE=Access_code | |
| 3. Ingest Policy Documents | |
| Crucial Step: Run this once to build the Vector Database. | |
| python rag/ingest_policies.py | |
| 4. Installation | |
| pip install -r requirements.txt | |
| python app.py | |
| Sample Interaction | |
| User: "Can Andy get a loan?" | |
| System Logs: | |
| Context Analyst: Intent detected: Loan Application. Data Agent: Successfully retrieved Customer ID 9982 (Andy). Score: 780. RAG Agent: Found rule: "Credit Score 750-850 = Low Risk". Underwriter: Mapping "Low Risk" to Interest Rate Table -> 3.175%. | |
| Final Output: | |
| "We are pleased to inform you that Andy's loan application has been APPROVED. Based on a credit score of 780, he qualifies for our Low Risk tier with an interest rate of 3.175%." |