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
