Intelli-Credit - Intelligent Corporate Underwriting

An autonomous AI Credit Officer designed to simulate how Tier-1 bank credit committees operate.

Intelli-Credit is an end-to-end B2B credit decisioning platform. It ingests structured and unstructured borrower financial data, conducts autonomous web-scale due diligence, computes an explainable composite risk score using Machine Learning ensembles, simulates stress tests evaluating RAROC capital impact, and automatically generates a structured, downloadable Credit Appraisal Memo (CAM) in PDF format.

🔥 Key Differentiators

1. "AI Credit Officer" Persona & LLM Integration: The system isn't just a traditional ML pipeline. It uses Google Gemini to extract unstructured data, read financial PDFs, and automatically author conversational narrative summaries for risk and compliance.
2. Web-Scale Research Simulation: Includes NLP sentiment analysis (using FinBERT), regulatory filings intelligence, and ESG scores.
3. Modular Decision Studio: A dynamic workflow engine that allows credit risk managers to visually construct underwriting logic, configure dynamic scoring rules, and trigger external webhooks interactively.
4. Capital Impact (RAROC) Simulation: Elevates from basic scoring to bank portfolio management by assessing Risk-Weighted Assets (RWA) and tier capital requirements.
5. SHAP-Based Explainability: Avoids "black box" models. The top contributing risk drivers are extracted for every decision.

🏗 System Architecture

The project consists of a Python FastAPI backend acting as the Machine Learning, LLM, and pipeline orchestration layer, paired with a modern Next.js frontend featuring real-time state synchronization, drag-and-drop workflow canvases, and Firebase authentication.

Core Modules

• Ingestion Engine: Parses financial PDFs, Bureau JSONs, and Bank Statement CSVs (using Gemini Vision & regex).
• Dynamic Scorer & Rules Engine: Evaluates nested risk rules built via the Decision Studio UI.
• LLM Research Agent: Leverages a LangChain-powered agent to perform RAG-based Vector Search and external intelligence aggregation.
• Risk Synthesis: Combines ML Probability of Default (Gradient Boosting), qualitative LLM summaries, and macro-economic factors.

🚀 Quick Start (Local Development)

1. Backend Setup

cd backend
python -m venv venv
venv\Scripts\activate  # On Windows
pip install -r requirements.txt

Note: A .env file is required in the backend containing your GEMINI_API_KEY, POSTGRES_USER, and database strings for Alembic migrations.

Run the FastAPI server:

uvicorn main:app --reload --port 8000

2. Frontend Setup

cd frontend
npm install
npm run dev

Note: Ensure your .env.local contains valid Firebase configuration keys (NEXT_PUBLIC_FIREBASE_API_KEY, etc.) for user authentication to function.

Access the platform at http://localhost:3000.

🧠 Using the Platform

1. Authenticate: Use the Firebase login page to sign in to the dashboard.
2. Upload & Ingest: Go to the New Proposal flow. Upload a financial PDF or Bureau data. The system uses Gemini Vision for intelligent OCR.
3. Build Workflows: Use the Decision Studio to visually drag and drop Risk Policies and Decision Nodes.
4. Review the Output:
   • Observe the final decision (APPROVE / CONDITIONAL / REJECT).
   • Review the Stress Test simulator and SHAP charts.
   • Check the Governance Audit Trail.
   • Click "Generate CAM" to receive the final professionally formatted Credit Appraisal Memo PDF.

🛠 Tech Stack

• Machine Learning & AI: Scikit-Learn (Gradient Boosting), SHAP, HuggingFace (ProsusAI/finbert), Google Gemini API, LangChain, FAISS (Vector DB)
• Backend API: Python 3.11, FastAPI, Uvicorn, PostgreSQL (with asyncpg & Alembic), ReportLab
• Frontend App: Next.js (App Router), React 18, TailwindCSS, Recharts, React Flow (Nodes), Zustand (State Management), Firebase Auth
• Infra: Context-driven REST APIs, Webhooks, Docker (Optional)