Ask the Expert - Implementing Applications on Pega Insurance Industry Foundation and Pega Underwriting for Insurance
- Pega Insurance Industry Foundation
- Pega Product Builder For Insurance
- All applications on Pega Underwriting for Insurance
- Follow the Product Support Community's Community Rules of Engagement
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Pegasystems Inc.
IN
Here are some of our documentation for Pega Underwriting for Insurance.
Pega Underwriting for Commercial Insurance
Pega Underwriting for Group Benefits
Pega Underwriting for Life Insurance
Pega Underwriting for Personal Insurance
Post your questions on any of these topics below!
Pegasystems Inc.
IN
Underwriting made easy with PUI 7.4 Decisioning capabilities
1. With latest decisioning capabilities,PUI predicts customer Chance of acceptance of proposal,so that underwriter can take effective decisions on premium management.
we run an adaptive model behind the scenes, which uses customer characteristics like Annual Revenues, SIC Group etc and LOB specific submission characteristics like no:of vehicles (for comm auto), total insured values (for comm property), premium amount (generic to all LOBs) etc as predictors. Adaptive model presents the propensity (chance of acceptance) which will be displayed to the underwriter in a color coded widget.
This can be customized based on customer needs with new predictor or turn on the existing predictors to active state based on the data feed.
2. Now that underwriter knows the chance of acceptance, he would like to see how much wiggle room he has to modify (increase) the premium before which the chance of acceptance starts reducing.
Underwriting made easy with PUI 7.4 Decisioning capabilities
1. With latest decisioning capabilities,PUI predicts customer Chance of acceptance of proposal,so that underwriter can take effective decisions on premium management.
we run an adaptive model behind the scenes, which uses customer characteristics like Annual Revenues, SIC Group etc and LOB specific submission characteristics like no:of vehicles (for comm auto), total insured values (for comm property), premium amount (generic to all LOBs) etc as predictors. Adaptive model presents the propensity (chance of acceptance) which will be displayed to the underwriter in a color coded widget.
This can be customized based on customer needs with new predictor or turn on the existing predictors to active state based on the data feed.
2. Now that underwriter knows the chance of acceptance, he would like to see how much wiggle room he has to modify (increase) the premium before which the chance of acceptance starts reducing.
Internally this is calculated based on the adaptive model bins that have been created. Each bin, which indicates a range of premium values (Considering only premium here, since it is one of the predictors and all the other predictors remain the same for a particular submission) has a different propensity or chance of acceptance. We identify the bin in which the current submission premium lies in. Then we identify the maximum premium value in that bin. The difference between the current premium and the max bin value indicates the maximum amount the premium can be increased, if at all, without reducing the chance of acceptance.