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Wide Group Automates Motor Policy Quotation with Gen AI

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Wide Group is one of Italy’s leading insurance brokerage firms, consisting of a team of experts committed to providing maximum protection for those who strive to push their limits. With global expertise and a constant focus on technological innovation, Wide Group offers exclusive insurance products, standing out for the quality and variety of its offerings.

The Challenge

Throughout its ambitious expansion, marked by acquisitions of local brokers and organic growth fueled by technological innovation, Wide Group encountered a critical challenge: integrating new brokers into a highly technological environment. New team members had to quickly adapt to a fast-evolving environment, often facing internal resistance.

Each acquired broker needed to use a dedicated portal for motor policy quotations. However, many brokers preferred to continue sharing necessary information via emails and attached PDFs, such as vehicle registration documents and driver’s licenses. This approach created a significant workload for employees, who were forced to manually input data, slowing down the quotation process and increasing the risk of errors.

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The Approach

Avvale’s strategy focused primarily on automating the extraction of information from emails and PDF documents. Leveraging advanced AI technology and large language models (LLMs), a dedicated team developed sophisticated algorithms capable of efficiently understanding and interpreting data. Thanks to API-based communication with the Motors portal, data extraction and automated entry became possible, greatly simplifying workflow and ensuring accuracy.

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The Impact

The results exceeded expectations: the accuracy rate of extracted information reached 90%, and with human intervention, it improved further to 98%.

This innovation delivered tangible benefits: automating the process led to significant time savings and freed up valuable resources at Wide Group, allowing the team to focus on higher-value activities. Additionally, reducing manual errors - vital in areas where precision in sensitive data like chassis numbers or licenses is crucial - made the motor policy quotation process more efficient and reliable, providing customers with higher-quality service and promoting greater sustainability, within and beyond the organization.

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