Advanced Analytics in Insurance

Data-driven transformation of the insurance business model


The digital transformation continues to impact the entire insurance industry value chain; exponential growth of available data has not yet been integrated into the insurers’ business and operating models. Initial pilot and proof-of-concept (PoC) projects have not yet proven successful due to IT infrastructure and data quality issues, lack of persistence and static, isolated approaches.

The use of AI and as a result predictive analytics as part of the underwriting (and claims management) process could help to
– accelerate the process and increase customer satisfaction
– substantially minimize costs
– identify and select “good” risks
– significantly grow revenues and profitability
– transform the business model from “detect and fix” towards a “predict and prevent” paradigm


Internal data (e.g. from agent interactions, customer dialogue) and external data sources (e.g. from telematics, wearables, smart homes, credit history) provide the basis for cutting-edge predictive analytics with the help of AI. Harvesting and structuring data, effectively analyzing data with appropriate AI models can result in valuable and actionable insights for P&C insurers as well as health insurers.

Seamless integration of AI model outcome into the underwriting (and claims management) enables data-based decision making and enhances process automation.

Underwriters benefit from decision-making support via data-based “second opinions” or via instant decision-making for many applications and can focus on cases which require most expertise.

Why Modulos

Portfolio of additional use cases and training how to structure your datasets
Easy-to-use AutoML platform with state-of-the-art models and convenient access for your AI teams; ongoing coaching to enhance data usage and model outcome

Go with Modulos to give your data science team an edge