How accurately can AI predict real-world insurance claim costs based on limited inputs like text and images?
Learn more about the challenge
Q&A Session for Hacker Wednesday 17 Sep at 14:30
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Problem
Insurance companies receive thousands of claims every day, often with limited information: short text descriptions and a few images. Manually reviewing and estimating the damage cost is time-consuming, inconsistent, and expensive.
Objective
The goal is to develop an AI-based system that can automatically analyze insurance claims and estimate the likely cost of damage, using limited input data such as text descriptions and images. This system should support insurance companies in making faster, more consistent, and data-driven decisions during the claims process. Key Capabilities: - Understand claim descriptions to identify what happened and classify the type of damage. - Analyze images to detect and assess visible damage. - Estimate repair or replacement costs based on historical data and external sources. Validate predictions by comparing them with actual payout data.
Support for Hackers
- Text descriptions and images from damage reports
- Historical data on typical damage costs
- Contact to a claims manager from VZ
Technical Preferences
- While Azure Foundry is the preferred platform, there is no obligation to use it. The focus is on delivering strong results regardless of the tech stack.
Why hack? - Real-World Impact: Helps automate and speed up insurance processes, improving customer experience. - Measurable Results: Predictions can be validated against real payouts – perfect for scoring and demos.
About the Challenge Partner
VZ VermögensZentrum is a Swiss financial services provider specializing in independent advisory for private and corporate clients on investments, pensions, taxes, insurance, and estate planning.