Carpe Data provides predictive scoring and data products that enhance fraud detection and streamline claims processing for property and casualty insurance companies. By utilizing AI and advanced data science, the platform automates workflows, improving efficiency by up to 10 times and significantly reducing the time spent on data analysis.
Funding
$20M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Property and casualty insurance companies face challenges in efficiently detecting fraudulent claims and accurately assessing risk during underwriting due to the time-consuming nature of manual data analysis. Sifting through vast amounts of online data to validate information and identify potential risks is a significant drain on resources.
Solution
Carpe Data provides AI-driven predictive scoring and data products designed to automate and enhance fraud detection and streamline claims processing for the insurance sector. The platform leverages advanced data science and large language models to analyze online data, identify relevant insights, and deliver them directly into existing workflows. By automating the process of gathering and refining online content, Carpe Data enables insurance companies to improve efficiency, reduce costs, and make more informed decisions.
Target Audience
The primary customers are property and casualty insurance companies, including claims and underwriting teams, seeking to improve efficiency, reduce fraud, and enhance decision-making through automated data analysis.
Features
- AI-powered platform for automated fraud detection and risk assessment
- ClaimsX Ultra: Automates fraud detection and deterrence
- Minerva Suite: Streamlines and accelerates underwriting
- Predictive scoring based on online data analysis
- Integration with existing claims processing and underwriting workflows
- Identification of dishonest claimants through online evidence gathering
- Data cleanliness practices including frequent data source audits
- Scalable, integrated, and ethical AI built without bias