INTELLIGENT-LOAN-APPROVAL-AGENT.MD
AI AGENT + RAG SYSTEM
Intelligent Loan Approval Agent
A decision-support system for loan workflows that combines multi-step LLM orchestration, retrieval, and structured knowledge grounding.
Overview
This project was built as a decision-support system for loan-review workflows where policy context, applicant evidence, and supporting case material are spread across multiple document types. Instead of acting like a simple chatbot over uploaded files, the system is designed to retrieve relevant evidence, ground the response, and support more consistent reasoning for operational users.
Problem
Loan review teams need faster and more consistent decisions when policy context and applicant evidence are spread across multiple document types. Traditional manual review slows down turnaround, introduces inconsistency, and makes it difficult for operators to connect evidence, policy rules, and decision rationale in one place.
Role
I shaped the system end to end: workflow design, retrieval setup, document grounding, decision-support output design, and user-facing delivery. I focused on making the system useful for real operational review instead of building a generic demo chatbot.
Implementation
- Implemented a 6-tool agent workflow in Python with Streamlit and Anthropic SDK orchestration.
- Built a RAG pipeline using ChromaDB and MiniLM embeddings to ground reasoning in indexed policy and case material.
- Structured multi-format ingestion so policy files, case inputs, and supporting documents could be indexed together in under one minute.
- Connected retrieved evidence to downstream decision-support outputs so the system could return rationale, not just text snippets.
- Added fallback handling to keep outputs resilient when retrieval confidence or inference paths degrade.
Outcomes
- Accelerated review setup by indexing multi-format files in under one minute.
- Improved review consistency by combining retrieval, LLM reasoning, and ML-style decision support in one workflow.
- Delivered a system focused on explainability, consistency, and operator trust rather than black-box output.
- Created a foundation that can later evolve into a broader multi-agent decision workflow.