---
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%."