AI Dispute Resolution System: Application of Multi-Agent Architecture in Financial Dispute Handling

Original Author/Maintainer: Hardikdhawan2904 Source Platform: GitHub Original Title: ai-dispute-resolution-system Original Link: https://github.com/Hardikdhawan2904/ai-dispute-resolution-system Source Publish/Update Time: 2026-06-16

Core Viewpoints: This project is an enterprise-level AI dispute resolution and fraud investigation platform for banking and financial transaction scenarios. It uses a multi-agent architecture to automate the entire process of dispute acceptance, transaction analysis, fraud verification, and decision support. It aims to address pain points of traditional manual processing such as long cycles, high costs, and insufficient consistency, while improving the efficiency and compliance of financial dispute handling.

In the financial service industry, dispute handling and fraud investigation are two core challenges:

• Global annual losses from payment fraud amount to tens of billions of dollars, and the labor cost of dispute handling remains high;
• Traditional manual processing has pain points such as long cycles (cases closed in weeks), high labor costs (a large number of professionals), poor consistency (inconsistent judgment standards), difficult fraud identification (complex patterns hard to recognize quickly), and high compliance pressure (need to meet updated regulatory requirements).

This system is precisely an intelligent solution designed to address these pain points.

The system adopts a multi-agent architecture, decomposing complex processes into specialized agents that work collaboratively:

1. Dispute Acceptance Agent: Parses multi-channel inputs, extracts key information, classifies and sorts cases, and generates case files;
2. Transaction Analysis Agent: Queries historical records, analyzes transaction patterns and anomalies, and generates visual graphs;
3. Fraud Verification Agent: Uses rule engines to detect known fraud, ML models to assess risks, and marks high-risk cases;
4. Evidence Verification Agent: Verifies document authenticity, cross-checks evidence consistency, and extracts key points;
5. Compliance Check Agent: Compares against regulations, checks procedural compliance, and generates compliance reports;
6. Decision Support Agent: Evaluates winning probability, recommends handling strategies, and generates decision reasons;
7. Workflow Orchestration Agent: Coordinates the execution order of various agents, dynamically adjusts processes, and triggers manual intervention.

Core Function Modules:

• Intelligent case classification: Identifies types like unauthorized transactions and duplicate deductions, and determines subsequent processes;
• Automated evidence collection: Extracts internal transaction records, calls external APIs, and integrates metadata and logs;
• Risk assessment engine: Multi-dimensional risk assessment of customers/transactions/merchants/patterns;
• Intelligent decision recommendation: Provides suggestions such as supporting/rejecting disputes or partial refunds, with confidence levels and reasons.

Technical Implementation Details:

• Large language models: Process unstructured data (document understanding, dialogue summarization, rule interpretation, report generation);
• Knowledge graphs: Build financial entity relationships, mine hidden connections and anomalies;
• Rule engine + ML: Hybrid decision strategy to ensure interpretability and accuracy, with continuous learning to update models.

Application Scenarios:

1. Bank credit card disputes: Quickly identify fraudulent transactions, verify merchant services, and generate regulatory reports;
2. E-commerce platform disputes: Verify delivery/signature status, coordinate refunds and exchanges, and assess seller responsibility;
3. Insurance claim investigation: Analyze claim credibility, identify gang fraud, and recommend investigation directions.

Value Proposition: Significantly reduces operational costs, improves customer satisfaction (faster response + consistent results), and helps financial institutions handle disputes efficiently.

Compliance and Audit Support:

• Automatic regulatory report generation: Statistics on dispute handling timeliness, fraud detection accuracy, customer satisfaction, etc.;
• Full audit trail: Records all decision timestamps and operators, saves agent reasoning logs, evidence version control, and manual review annotations.

Ensures the handling process meets regulatory requirements, is traceable and auditable.

Current Limitations:

• Complex cases (multi-party/cross-jurisdictional) require manual leadership;
• Cannot handle customer emotional comfort scenarios;
• Possible deviations in legal provision interpretation;
• Vulnerable to adversarial sample deception.

Improvement Directions:

• Multi-modal analysis (integrate voice/video evidence);
• Real-time learning of new fraud patterns;
• Optimize human-machine collaboration mechanisms;
• Support multi-language case handling.

Outlook: As technology matures, human-machine collaboration will become an industry standard, and the ideal model of "human supervision, AI-led processing" will be realized in the future.