The project involves analyzing claims data acquired from various insurance providers across 30+ countries to aid in premium negotiations and mitigate client losses. The project adopts file format standardization and data transformations to address these issues, ensuring a unified and usable dataset. Additionally, fuzzy matching techniques are employed to link claims data with incident numbers from the legal system to ensure data accuracy and integrity.

The client is a global ride-hailing and transportation network company that operates through a mobile app. With a presence in over 900 metropolitan areas worldwide, the client has revolutionized the transportation industry, providing convenient and affordable alternatives to traditional taxis. The client is also actively expanding into other logistics areas.

• Standardization and Transformation of Claims Data: Organize diverse insurance provider data into a coherent dataset by aligning inconsistent structures and languages.
• Data Linkage through Fuzzy Matching: Implement fuzzy matching to connect claims with incident records, providing a complete incident overview.
• Consolidation of Claims Data and Financial Metrics Derivation: Unify regional claims data for insights into financial performance, calculating metrics like losses and recoveries.
• Data Validation for Accuracy and Integrity: Ensure reliable data through thorough validation, thereby empowering informed decision-making for the client.