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Ask the Expert - Implementing Applications on Pega Insurance Industry Foundation and Pega Underwriting for Insurance

Join Sowmya in this month's Ask the Expert session!

Meet Sowmya Desu: Sowmya Desu is working as a Principal Solutions engineer from IAD Insurance team. She has more than 3 years experience with Pega Insurance team and an overall experience of 7.6 years on building applications and customizations on Pega and Java.

Message Sowmya Desu: I hope to help answer all your questions related to:

  • Pega Insurance Industry Foundation
  • Pega Product Builder For Insurance
  • All applications on Pega Underwriting for Insurance

Ask the Expert Rules

  • Follow the Product Support Community's Community Rules of Engagement
  • This is not a Live Chat - Sowmya will reply to your questions over the course of this two-week event
  • Questions should be clearly and succinctly expressed
  • Questions should be of interest to many others in the audience
  • Have fun!

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Mod
June 19, 2018 - 4:23am

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!

Lochana | Community Moderator | Pegasystems Inc.

June 28, 2018 - 8:54am

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.