The insurance industry is highly complex, with brokers managing extensive portfolios of products and their numerous variations. Finding the best policy for a client requires analyzing multiple factors, including coverage, pricing, terms, and suitability for individual needs. Manually navigating these options is time-consuming and prone to inefficiencies, often leading to suboptimal recommendations.
A Swiss fintech startup aimed to enhance the efficiency of insurance brokers by developing an AI-driven recommendation system. The challenge was to create an intelligent tool capable of delivering precise, data-driven policy suggestions while integrating seamlessly with existing data sources. The solution needed to handle large datasets, optimize recommendation accuracy, and reduce the time brokers spent searching for relevant products. Additionally, ensuring industry experts could evaluate and refine the system’s recommendations was essential for its successful adoption.

To address this challenge, we developed a customized recommendation system that combines traditional distance-based methods with modern multi-dimensional optimization techniques. This hybrid approach ensures high accuracy in matching clients with the most suitable insurance policies. The system analyzes various product attributes and client needs to generate personalized recommendations that align with broker expertise and industry standards.
A critical aspect of the project was designing an efficient and cost-effective data collection process. Collaborating with insurance experts, we structured a method for gathering high-quality training data while minimizing costs and time investment. The system was then built using this refined dataset, ensuring that the machine learning model accurately reflected real-world decision-making by brokers.
The final product included a core recommendation engine integrated with data sources. Additionally, an interactive application was developed for industry experts to evaluate and fine-tune the recommendation system. This validation process ensured that the AI-generated suggestions met the expectations and standards of professional brokers.

The AI-driven recommendation system successfully improves efficiency in insurance sales by streamlining the process of matching clients with the most relevant policies. Industry experts evaluated the solution positively, confirming its accuracy and usefulness in real-world applications. By reducing the complexity of insurance product selection, the system empowers brokers to provide faster, more precise recommendations, ultimately enhancing customer satisfaction and sales performance.
By leveraging AI to optimize insurance product recommendations, this solution transforms how brokers navigate complex policy offerings. The system not only enhances efficiency but also improves customer experiences by ensuring clients receive tailored, data-driven insurance options faster and more accurately.

The project began with a collaborative effort to define precise business and technical objectives, ensuring the recommendation model was tailored to the needs of insurance brokers.
Next, a custom mathematical model was designed, integrating traditional distance-based methods with advanced multi-dimensional optimization techniques to maximize recommendation accuracy.
We worked closely with industry experts to establish an effective data collection framework, ensuring the dataset captured the nuances of real-world insurance decision-making. The system was developed, trained, and integrated with external data providers, allowing for real-time updates and continuous model improvement (in the future). An additional interactive application was created to enable insurance professionals to evaluate and refine the recommendation system, ensuring its reliability and industry alignment.

The system was built using a combination of traditional distance-based models and modern multi-dimensional optimization techniques. Machine learning was applied to refine the recommendation process, ensuring accuracy and efficiency. The data collection process incorporated expert-driven validation to enhance the system’s alignment with real-world brokerage practices. Additionally, an interactive evaluation tool allowed industry professionals to assess and adjust the model’s recommendations, further improving its performance.