External Data and AI are
Transforming Commercial
Insurance Underwriting.
Improve profitability, optimize costs, and increase
customer loyalty with our Insurance Analytics Service
Business Objective
In the face of shrinking premium, commercial P&C insurers are constantly having to hold the line on underwriting expenses even as they strive for higher submission and policy counts. Additionally, with around 20% of data from agents and customers found to be inaccurate, insurance companies need to leverage external data and AI to:
– Check the price correctness of information given by agents and clients.
– To provide a better user experience, pre-fill application forms.
– Increase efficiency significantly by automating information validation and pre-filling.
Challenges
- To achieve appropriate (above 90%) hit/fill rates and information accuracy, many data sources must be triangulated.
- Application form business names and addresses are frequently not a perfect match to data discovered in other sources.
- External data is often largely unstructured, and it is necessary to extract the appropriate signals.
Solution Methodology
- Based on the predictive potential of data to inform risk, prioritize underwriting questions for validation and pre-filling. For instance, industry class, XMod, past losses, safety violations, years in company, salary, property features, car data, operating hours, and benefit programs.
- To extract the correct information and map it to the right company, use fuzzy matching, natural language processing, and machine learning.
- User interfaces and APIs are used to orchestrate and incorporate external data into the workflows of the client’s underwriting process.
Business Impact
- More than half of the critical questions may be pre-filled and evaluated with greater than 90% accuracy.
- Straight-through-processing (STP) rates can be improved by up to 40%.
- Impact estimated at USD 100-150 million with business teams from several carriers for General Liability, BOP, and WC underwriting.