AI-Powered Intelligent Loan Approval System: Credit Automation via Multi-Agent Collaboration

This project builds a multi-agent loan approval system based on LangGraph, achieving end-to-end automation from data collection to automatic approval through six specialized AI agents. The system integrates core capabilities such as conversational interaction, OCR document processing, financial scoring, risk RAG assessment, and intelligent decision-making, aiming to solve the problems of low efficiency and high error rates in traditional loan approval.

Traditional loan approval relies on manual operations, taking days to weeks to complete. It has problems like high repetition, low efficiency, and high error rates, leading to high operational costs and large risk exposure for financial institutions. The development of AI technology brings opportunities for automation, but a single model struggles to meet multi-domain needs such as multimodal data processing and complex rule reasoning. Building a multi-agent collaboration system has become a key challenge.

The project uses the LangGraph framework to build a collaborative system of six specialized agents:

• Data Collection Agent: Dynamically collects application information through natural language dialogue and has the ability to clarify and confirm;
• Document Processing Agent: Integrates OCR to extract structured information, verifies document authenticity, and triggers supplementary requests when information is missing. All agents exchange information through defined interfaces to achieve end-to-end automation.

The financial scoring agent extracts financial indicators to calculate solvency and identifies abnormal patterns (such as unreported liabilities). The scoring model combines traditional credit scorecards with large language model reasoning: predefined rules are used for scenarios with clear rules, and flexible reasoning for boundary cases, balancing consistency and flexibility.

• Risk Assessment Agent: Uses RAG technology to retrieve knowledge bases such as historical cases and regulatory requirements, generating risk ratings and detailed reports;
• Decision Agent: Synthesizes outputs from all agents, directly handles clear cases or marks boundary cases requiring human intervention;
• Compliance Review Agent: Independently supervises process compliance, checking authorization, discriminatory factors, and information disclosure.

• Hybrid Deployment: Local open-source models are used for sensitive links (data does not leave the country), and cloud-based large models are called for complex analysis, balancing security and performance;
• Differentiated Support: Personal loans focus on standardized analysis such as income stability; corporate loans integrate functions like business registration inquiry and public opinion monitoring, supporting complex financial analysis and industry benchmarking.

After implementation, approval time has been reduced from days to minutes, risk identification capabilities are equivalent to or better than manual work, and humans are freed up to focus on complex cases. In the future, real-time data streams will be introduced, more external data sources (such as social media) will be integrated, and explainable AI functions will be developed to promote the development of inclusive finance.