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Business Issue:
Essential conclusive risk data is not easily available for growth stage P&C insurance carriers to automate policy decision-making and ratemaking.
Solution:
Estated offers solutions to integrate essential conclusive risk data into the policy and ratemaking making process.
Benefits:
- Automated workflows improve efficiencies and time saved on underwriting, renewals, and claims processes.
- Data validation from several sources bolsters fraud detection systems and prevents claims fraud.
- Estimates of future obligations can be conducted using recorded data.
A proper mix of datasets, machine-learning and human review can create efficiencies and accurate analysis in various stages of the policy process. Property data combined with area statistics are a vital share of the information needed by underwriters and actuaries to conduct their business in an efficient manner.
Available data offered by the Estated API
- Property characteristics
- Owner info
- Disaster risk
- Crime
- County health statistics
Initial underwriting and ratemaking:
Carriers can use data for initial decisions on underwriting and setting premiums. Property and area data help underwriters evaluate risks and automate the decision-making process as well as assist in setting the premium. Actuaries can use specific information about properties and their surrounding areas as a deciding factor in approval and rate-making.
Renewal underwriting and ratemaking:
At the time of renewal, the insurer has the option to decline further coverage or increase the premium, based on updated risk data and related profitability. Marginal differences in disaster risk, crime, and demographic statistical data will all affect renewal or premium variation.
Claims management:
Property and owner data can be used as a key tool in the process of detecting and preventing claims fraud, managing claim costs, identifying the appropriate support for claims handling expenses, and for reinsurance and retention purposes. Fraud detection systems become worthless when errors are introduced, which means a 100% automated experience is very difficult to achieve. A proper mix of datasets, machine learning and human review can bring fraud detection to new heights, and analytics can serve as the backbone to assure the highest level of objectivity.
Reserving:
The same data can be used to create estimates of future obligations based on external factors and trends. Carriers are able to identify new risk opportunities and identify growing or shrinking risk obligations based on variations in statistical trends in the area or changes to specific property details.
