FinTech

Automated Compliance Audit

Executive Summary

We reduced manual document review time by 85% using a custom RAG pipeline. This system replaced a bottleneck of 12 junior analysts with an intelligent, citation-based document processing engine.

1The Challenge

The client, a mid-sized lender, was spending 400+ hours per month manually reviewing loan applications against constantly changing regulatory guidelines. The manual process had a 15% error rate, leading to potential fines and delayed approvals. They needed a way to scale without hiring more headcount.

2The Solution

We architected a specialized RAG (Retrieval-Augmented Generation) pipeline using Python and LangChain. Unlike generic chat tools, we enforced a 'strict citation' constraint: the model could only answer if it could cite the specific regulatory clause. We used Pinecone for vector storage and GPT-4 for the reasoning layer, wrapped in a secure FastAPI backend.

3The Result

Manual review time dropped by 85% within the first month. Accuracy improved to 99.5%, significantly outperforming the human baseline. The system now handles 5,000+ documents monthly with a total infrastructure cost of under $500.

The Tech Stack

Python
LangChain
OpenAI API (GPT-4)
Pinecone
FastAPI
React

Key Takeaway

Specialized, citation-based RAG systems outperform generic LLMs for compliance tasks where accuracy and audit trails are non-negotiable.