Predictive Analytics in Insurance: Closing the Data Value Realization Gap

21 Aug 2020
by Ronak Doshi, Aaditya Jain, Supratim Nandi, Vigitesh Tewary, Shrey Kalawatia

Insurers are planning for rapid digital adoption to reimagine products, channels, and operations in a post-pandemic world and have been investing in digital capabilities such as analytics, automation, cloud, design thinking, Internet of Things (IoT), Machine Learning (ML), and mixed reality. Data is the foundational asset that powers these digital capabilities and insurers, which have amassed a massive volume and variety of data, both structured and unstructured, are now struggling to realize return on investment from their data initiatives.

Globally, insurance firms spent over US$6.3 billion on data and analytics services in 2019. As insurers accelerate their data initiatives, advancements in predictive analytics technology and use cases specific to the insurance industry are reaching enterprise adoption maturity. Predictive analytics assists insurers in adopting newer business models, driving superior operational efficiencies, and improving customer experience. Enterprise-wide adoption of predictive analytics is a key enabler to drive top-line growth and cost take-out initiatives. Thus, the insurance industry has experienced increased adoption of predictive analytics in recent times – driven by an explosion in data availability, increased adoption of the cloud, data democratization, and advances in Artificial Intelligence (AI) / ML.

In this report, we define predictive analytics, identify the business drivers for the adoption of predictive analytics in insurance, explore use cases, look at the challenges insurers face in their adoption journeys, understand the complex technology vendor and service provider landscape, and provide best-in-class examples of insurers leveraging predictive analytics.


  • Industry: insurance
  • Geographic scope: global


In this report, we study:

  • The adoption of predictive analytics in the insurance industry
  • Predictive analytics through a platform-based operating model
  • Drivers for the adoption of predictive analytics in insurance
  • Best-in-class examples of adoption of predictive analytics by insurers
  • Associated challenges and the technology vendor and service provider landscape


Insurance - IT Services (ITS)


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