Realtime
Pricing
(Insurance)

AI-based pricing, portfolio control and smart questioning allow for the optimisation of premiums and shortening of the quotation phase

Payment behaviour Claims history Realtor CRM Lead- Management Risk-Modelling Personal data Fraud Profiling/ Internal Data Data Enrichment & Profiling

Problem:

Insurance companies lack sufficient data to precisely calculate the price of a customer’s future risk. Simultaneously, the quotation phase takes too long from the customer’s perspective. Potential customers have to complete an extensive questionnaire with only partial relevance to them personally. This process leads to customer dissatisfaction.

KENDAXA’s Solution:

KENDAXA will process the insurance companies’ existing data in order to provide a qualified dataset that is sufficient to correctly assess the individual risks. This result is achieved by performing data aggregation and data enrichment with secondary and tertiary sources as well as historical data and data profiling, leading to the optimisation of the risk-pricing-ratio. At the same time, adaptive smart questioning is used to measure the customer risks prior to quotation.

Result:

Using data optimisation, the insurance benefits from risk-adapted pricing and the simultaneous improvement of the client experience with a shortened quotation phase.

3 6%

Increase in
Customer Satisfaction

1 5%

Increase in
Target Quota

1 1%

Reduction of
Claim Settlement Costs

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