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Insurance Smart Quote

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THE FACTS

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

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

ANALYST VIEW

Augmenting Underwriting with AI/ML

Of respondents believe that predictive model solutions are amongst the top 3 technological investments for underwriting

Machine Learning is extensively used across the Insurance value chain

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Data-driven AI/ML based policy pricing and risk selection help control the Loss Ratio and contribute to Underwriting excellence

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INTRODUCING SMART QUOTE, POWERED BY PEGA AI

BUSINESS PROBLEM

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

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Future State/ Strategic Benefits

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

Differentiators

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

Solution Overview

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

ADOPTION OF SMART QUOTE

Scope of Activities

Duration for Discovery (1 week) and Dev: 4 - 5 weeks

  • Understanding of AS-IS workflow and business use case
  • Extract historic data and create the prediction model
  • Create the Pega Decision Strategy and link to data model
  • Link Decision Strategy to Underwriter workflow
  • Creation of Underwriter UI components

SIT/UAT/Go-Live: Along with next release of the Underwriting application

Team: 1 Business Analyst, 1 Pega Developer, 1 Data Analyst

Pre-requisites

  • Pega 7.x or 8.x platform license, Pega Decisioning license
  • Datasets with good Data Quality and Quantity of data of at least 1 - 2 years
  • Unbiased data which is representative and balanced

Commercials

  • Smart Quote framework provided free of charge
  • 4-6 week framework customization cost to be provided to the customer as part of consultation process
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