Accurately measuring risk is essential for insurers seeking to balance competitive offers with profitability. Customers who appear similar at first glance can have very different probabilities of making a claim, making more detailed analysis valuable when setting coverage, deciding whether to accept a policy and determining its price.
Serasa Experian combines financial, behavioral and demographic information with data specific to the insurance sector to support this analysis. In home insurance, for example, analytical models can assess property details alongside regional, climate and crime-related variables and information about the owner to produce a more detailed estimate of claims risk. The approach can also be adapted to other types of insurance, according to the features of each coverage.
Predictive analysis can inform decisions even before a policy is issued. During prospecting and quotation, insurers can compare customer characteristics with potential risk and profitability to prioritize customer profiles, direct sales efforts and develop more suitable offers.
After a policy is issued, models can help estimate how often claims may occur and how severe they could be, while also highlighting changes in the overall portfolio. In auto insurance, combining behavioral information with anti-fraud tools can help flag potential warning signs when a claim is made, while supporting prompt service for policyholders who need coverage.
Used throughout the customer journey, from quotation to renewal, data analysis can help insurers assess risk, adjust pricing and monitor their portfolios as they make decisions before and after issuing policies.
Sources: CNN Brasil.