Bank-grade credit intelligence. Automated. Auditable. Agentic.
Intelli-Credit is an autonomous, multi-agent AI system that transforms the way banks and NBFCs evaluate loan applications. It ingests raw financial documents — balance sheets, GST returns, ITRs, bank statements — and produces a fully reasoned, evidence-cited Credit Appraisal Memorandum (CAM) with fraud screening, regulatory compliance checks, and a simulated stress test, all without human intervention until the final decision gate.
Credit underwriting in India is slow, inconsistent, and dangerously manual.
A typical SME loan application sits on a credit analyst's desk for 10–21 working days. The analyst manually extracts numbers from PDFs, builds ratios in spreadsheets, cross-checks GST filings against P&L statements, and writes a CAM from scratch — every single time. The process is:
- Opaque — decisions rely on analyst intuition with little documented reasoning
- Inconsistent — two analysts evaluating the same file often reach different conclusions
- Slow — multi-week turnarounds kill SME cash flow and borrower trust
- Fraud-blind — manual review misses sophisticated document manipulation and GST mismatches
- Non-compliant by default — RBI and Basel III ratio checks are often done informally or skipped
| Impact Area | Real-World Effect |
|---|---|
| SMEs | Loan rejections due to poor documentation, not poor creditworthiness |
| Banks | NPA risk from approving loans based on fabricated financials |
| Analysts | Burnout from repetitive, low-value extraction tasks |
| Regulators | Audit trails are weak; CAMs rarely cite their evidence sources |
| Economy | Credit gap for India's 63 million MSMEs exceeds ₹25 lakh crore |
Intelli-Credit deploys a pipeline of specialized AI agents, each owning a distinct slice of the underwriting workflow:
- Document Routing Agent — classifies uploaded files (structured PDFs, scanned images, unstructured text) and routes them to the right parser
- OCR Engine — extracts text from scanned documents with layout awareness
- Financial Agent — parses balance sheets and P&L statements into structured financial data
- GST Agent — reconciles GSTR-1 vs P&L, GSTR-2A vs GSTR-3B, flagging turnover mismatches
- Fraud Radar Agent — runs OSINT collection, cross-references registry data (CIN/GSTIN), and scores fraud probability
- Forensic Audit Agent — detects document tampering, round-tripping, and anomalies using a causal reasoning engine
- Verification Agent — validates all evidence items, classifying them as Green / Yellow / Red
- CAM Agent — generates a bank-grade Credit Appraisal Memorandum with formula citations and source references
- Committee Agent — runs a three-persona AI debate (Risk Officer, Compliance Officer, Business Officer) to simulate credit committee review
- Regulatory Engine — checks RBI exposure norms, DSCR thresholds, and Basel III capital ratio compliance
- Simulation Engine — stress-tests the borrower across worst-case revenue, rate, and expense scenarios
The result: a complete, auditable credit decision — in minutes, not weeks.
| Dimension | Existing Tools | Intelli-Credit |
|---|---|---|
| CAM Generation | Analyst writes manually in Word | AI generates structured CAM with [Source: Doc, Section] and [Logic: Formula] citations |
| Fraud Detection | Rule-based flag lists | Multi-signal fraud radar: OSINT + GST reconciliation + forensic document analysis + causal engine |
| Credit Committee | Human meeting, unrecorded debate | Autonomous 3-persona AI committee with full transcript |
| Stress Testing | Static sensitivity table | Monte Carlo-style simulation across revenue, rate, and cost shock scenarios |
| Regulatory Checks | Post-approval compliance review | Real-time RBI/Basel III compliance enforced during underwriting |
| Evidence Auditability | Analyst notes in margin | Every claim tagged Green/Yellow/Red with traceable evidence trail |
| Document Handling | Analyst reads PDFs manually | Automated routing: OCR for scans, structured parser for digitals, LLM for unstructured text |
| SMT Validation | None | Satisfiability Modulo Theory validator cross-checks financial logical consistency |
| Knowledge Graph | None | Entity relationship graph links company, directors, GST, and registry data |
| Federated Privacy | Centralized sensitive data | Federated learning stub isolates sensitive borrower data per institution |
| Layer | Technology |
|---|---|
| Runtime | Python 3.11+ |
| API Framework | FastAPI |
| Agent Orchestration | Custom async multi-agent pipeline (asyncio, AsyncGenerator) |
| LLM Provider | Ollama (local) — llama3.2, lfm2.5-thinking; optional OpenAI / Anthropic / Google APIs |
| OCR | Custom OCR engine (core/ocr.py) |
| Knowledge Graph | core/knowledge_graph.py — entity relationship mapping |
| Causal Reasoning | core/causal_engine.py — anomaly and causation detection |
| SMT Validation | core/smt_validator.py — logical consistency verification |
| OSINT Collection | core/osint_collector.py + core/web_search.py |
| Regulatory Engine | core/regulatory_engine.py — RBI / Basel III checks |
| Forensic Engine | core/forensic_engine.py — document integrity analysis |
| Session Store | models/database.py — in-memory session management |
| Data Schemas | Pydantic (models/schemas.py) |
| Testing | Pytest |
| Layer | Technology |
|---|---|
| Framework | Next.js 14 (App Router) |
| Language | TypeScript |
| Styling | Tailwind CSS + custom global CSS design system |
| UI Pages | Upload → Processing → Intelligence → Results → Regulatory → Simulator |
| Fonts | Inter, Public Sans (body) · JetBrains Mono (data/code) |
| Layer | Technology |
|---|---|
| Environment | .env with OLLAMA_BASE_URL, optional cloud API keys |
| API Bridge | NEXT_PUBLIC_API_URL connects Next.js frontend to FastAPI backend |
| Package Management | pip (backend) · npm (frontend) |
./
├── backend/
│ ├── agents/ # Specialized AI agents (CAM, GST, Fraud, Committee, etc.)
│ ├── core/ # Engines: OCR, LLM client, Forensic, Regulatory, OSINT, Knowledge Graph
│ ├── models/ # Database session store and Pydantic schemas
│ ├── tests/ # Pytest test suite
│ ├── main.py # FastAPI application entry point
│ └── requirements.txt
└── frontend/
├── app/ # Next.js App Router pages
│ ├── upload/ # Document upload
│ ├── processing/ # Live agent processing stream
│ ├── intelligence/# Analysis results
│ ├── results/ # CAM output
│ ├── regulatory/ # Compliance dashboard
│ └── simulator/ # Stress test simulator
├── styles/ # Global CSS design system
└── next.config.js
# 1. Clone and configure
cp .env.example .env
# 2. Start Ollama and pull models
ollama pull llama3.2:latest
# 3. Run backend
cd backend
pip install -r requirements.txt
uvicorn main:app --reload
# 4. Run frontend
cd frontend
npm install
npm run devOpen http://localhost:3000 — upload your financial documents and let the agents work.
Built for Indian banking. Designed to RBI standards. Evidence-first by default.