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.

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.

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.