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Chaitanya1934 / repository
Developed a multi-agent AI system that analyzes insurance claims using multimodal AI, computer vision, and LLMs to validate evidence, assess risk, and automate claim decisions.
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An AI-powered multimodal claim verification system that analyzes insurance claims using visual evidence, user claim descriptions, historical patterns, and intelligent decision-making agents.
Built as part of HackerRank Orchestrate 2026, this project explores how AI agents and multimodal reasoning can improve the insurance claim review workflow by automating evidence validation, damage analysis, risk assessment, and final claim decisions.
Traditional insurance claim processing requires manual inspection of images, documents, and user statements. This can be slow, inconsistent, and difficult to scale.
This project implements an AI-based review pipeline that evaluates:
The system generates structured decisions explaining whether a claim is:
Analyzes uploaded claim images to identify:
Supported categories:
The solution follows an agent-based workflow where specialized components handle different responsibilities.
Responsible for:
Checks:
Evaluates:
Selected from shared topics, language and repository description—not editorial ratings.
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flowchart TD
A[User Claim + Images + User History]
A --> B[Image Analysis Agent]
B --> C[Evidence Validation Agent]
C --> D[Risk Assessment Agent]
D --> E[Decision Engine]
E --> F[Final Claim Status]
F --> G[Structured Explanation]
| Field | Description |
|---|---|
| evidence_standard_met | Indicates whether sufficient evidence was provided |
| evidence_standard_met_reason | Explanation for the evidence validation decision |
| risk_flags | Detected suspicious patterns or anomalies |
| issue_type | Identified damage type |
| object_part | Affected component of the object |
| claim_status | Supported, Contradicted or Not Enough Information |
| claim_status_justification | AI-generated explanation behind the decision |
| supporting_image_ids | Images used as supporting evidence |
| valid_image | Indicates whether uploaded images are usable |
| severity | Estimated damage severity (Low / Medium / High) |
AI-Insurance-Claim-Review-System
| Directory | Purpose |
|---|---|
| code/agents | Core AI agents responsible for claim analysis |
| code/evaluation | Evaluation scripts and benchmarking |
| code/utils | Helper functions and utilities |
| code/main.py | Entry point of the application |
| README.md | Project documentation |
The system uses a structured AI reasoning pipeline instead of a single model call.
The approach focuses on:
A single AI call can mix responsibilities and produce inconsistent reasoning.
Breaking the workflow into agents provides:
More analysis steps improve reasoning quality but increase processing time.
The system assists decision-making but complex insurance scenarios may still require human validation.
Higher capability multimodal models provide stronger reasoning but increase operational costs.
Future enhancements:
Through this project, I explored:
Developed by Chaitanya Rastogi
Computer Science Undergraduate
Interested in AI, Cloud Computing, and Scalable Software Systems
This project is licensed under the MIT License.
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