The problem ZK-EVALUATE Enabling on-chain rainfall assessment solves

The ZK-EVALUATE project addresses major challenges in agricultural insurance, particularly in managing the impact of natural adversities like rainstorms on farming. It tackles several key problems:

1. Trust Deficit: There's a significant trust gap between farmers seeking efficient claim processing and insurance companies focused on profit maximization. Traditional mechanisms lack transparency, leading to distrust.

2. Operational Inefficiencies and High Costs: Current systems are inefficient and costly due to the need for on-site assessments and brand investments by insurance firms, resulting in delayed claim settlements.

3. Inaccurate Loss Assessment: Existing methods struggle to accurately estimate rainfall and agricultural loss, often leading to claim disputes.

4. Privacy Concerns: Standard approaches might compromise farmer data privacy.

Solutions Offered by ZK-EVALUATE:

1. Decentralized System: The project proposes a decentralized, transparent system for loss assessment, leveraging publicly authenticated meteorological data and machine learning models, ensuring unbiased and equitable processes.

2. Machine Learning for Rainfall Prediction: Utilizing XGBoost's Gradient Boosted Decision Trees classifier enhances rainfall estimation accuracy, critical for determining damage and insurance claims.

3. Zero-Knowledge Proofs (ZKPs): ZKPs validate model predictions' integrity without exposing underlying data or specifics, maintaining privacy and data security.

4. Reduced Costs and Conflicts: Automation and intelligent processing reduce operational costs and minimize conflicts of interest between farmers and insurers, promoting an impartial, data-driven approach.

5. Improved Claims Processing Efficiency: Integrating meteorological data and refining prediction mechanisms speeds up claims processing, essential for farmers' recovery from natural adversities.

Challenges I ran into

The whole process involved several very new toolchains, including forust, RISC Zero XGBoost Model and Bonsai, and the debugging and matching work took a lot of time.