Improve customer claims experiences using advanced analytics
Improve customer claims experiences using advanced analytics
2 min. read

Improve customer claims experiences using advanced analytics

The Big Picture

A leading P&C insurer wanted to build a market-leading analytical foundation. This would help it rapidly and profitably capture whitespace growth opportunity and identify ‘micro-drivers’ of customer satisfaction, profitability, cost, and ROI. The foundation was also needed to enable enhanced ongoing prediction, monitoring, and improvement in performance of claims settlement and the customer experience.

Transformative Solution

To solve the insurer’s challenges, more than 30 interviews were quickly conducted to understand the existing processes, challenges, and aspirations for the organization as well as to assess the current state and formulate the desired end state (including data/analytics maturity level). The solution identified focus areas around financials, customers, and employees. In addition, more than 50 use cases were identified and prioritized, which identified an opportunity of $100M+ in revenue across use cases.

The roadmap development approach consisted of

  • knowing the organization’s pain points, vision, and goals,
  • identifying the use cases by breaking down the problems using issue trees,
  • understanding the status of current processes, activities, and data accessibility, and
  • prioritizing use cases based on impact, need, and readiness.

The Change

The solution revealed a three-year impact of more than $100M that could be delivered through prioritized use cases from more than 50 analytical opportunities that were identified. The company identified $25M-$35M incremental EBITDA opportunity. Nearly 30 new analytics opportunities were identified to reduce costs, improve efficiencies, and enhance the customer experience.

More than 25 data sources were identified for building models around claims severity prediction and fraud detection. Robust models, leveraging 2k+ variables coupled with human evaluation, drove 10X more correct predictions and 10% fewer inaccurate predictions.