Traditional pricing models are based on economic and behavioral assumptions, aggregate risk pooling, customer’s standard rating factors, and prices set by competition. Customers increasingly expect to pay for only what they use, so dynamic pricing models based on an individual’s profile, lifestyle, and activities is the need of the hour in the digital world.
Goals and Objectives
Combine existing customer data and external data sources as well as apply analytics to draw insights to arrive at personalized pricing models. Partner with insurtechs and other industries to obtain the data and technology required to realize dynamic pricing.
Dynamic pricing engine, IoT, Advanced analytics (predictive, contextual), Cognitive capabilities
Use Case Summary
Convert the current pricing models into contextual customer-relevant models based on real-world, precise risk identification and assessment. Accurate automated real-time quotations depend on standardized tariff books ingestion, commercial options implementation, comparative market analysis, and AI-centered behavioral assumptions. Building up a future-proof ratemaking brain also requires API-led openness (i.e., continually accessing new information sources that can add nuance to the pricing decision tenets).