Case Study: Fleet Auto improves data quality and operational efficiency with Quantemplate

Addressing the Invisible Data Quality Issue in Program Business

Fleet Auto, a leading P&C fleet auto carrier, was struggling to manage rapidly growing data complexity across 200+ countries, 30+ fronting partners, 40+ formats, and 25+ languages. The company needed a better way to measure profitability and volatility, deliver claims insights to risk managers, and provide consistent information to customers, brokers, and underwriters. Quantemplate’s insurance-focused platform was used to address these data preparation and harmonization challenges.

Quantemplate implemented a solution in less than two months, using machine learning capabilities to automate data preparation and combine legacy claims data with monthly feeds from multiple countries. The result was clean, validated data made available to internal and external stakeholders, improving operational efficiency and positioning Fleet Auto for future growth, new data sources, and enhanced customer engagement.

Fleet Auto