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Insurance Smart Quote

Business Challenge

It is widely acknowledged within the Insurance industry that data-driven analytics based human judgment would help minimize the subjectivity in Underwriting decisions and significantly improve business efficiency

Other Challenges

  • Uniqueness of applicant’s data from a risk assessment standpoint
  • Inefficiencies while handling huge datasets related to risk proles
  • Risk selection and competitive pricing to avoid under/over pricing
  • Deciding between risk averseness and applicant’s propensity to buy

 

Insurance_Smart_Quote_Business_Challenges

 

Powered by Pega AI/ML based decisioning models, Smart Quote will augment the Underwriting process by:

  • Providing real-time quote acceptance propensity
  • Underwriter decision feedback loops into the predictive model
  • Customer risk data from D&B and Pitney Bowes
  • Data driven Pega Predictive models

 

Insurance_Smart_Quote_Solution_Overview

 

Insurance_Smart_Quote_Solution_Overview_1

Underwriters are no longer just responsible for risk selection and pricing, they are now expected to:

  • Support Sales function and increase new business
  • Significantly decrease the loss ratio
  • Increase retention rates of existing customer base

The information used by underwriters can vary widely. Also, underwriting actions are not always truly risk-based, but instead influenced by:

  • Market dynamics
  • Subjective decision making
  • External competition

Solution Benefits

  • Higher hit-ratio, lower loss-ratio with a more mature and self-learned predictive model
  • Improved CSAT scores with possibility of offering

Differentiators

  • Pega predictive and adaptive modelling covering the real-time aspects of business
  • Providing a holistic risk assessment of the act to aid better business decisions

Brochure-DPASmartQuote_3

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