Machine Learning for Insurance Industry

India alone has about 360 million life insurance policies in a year which makes it one of the biggest markets of life insurance in the world

Data Science | 12/08/2017 UTC
Blog Hashbrown

India alone has about 360 million life insurance policies in a year which makes it one of the biggest markets of life insurance in the world, and is expected to increase at a CAGR of 12 to 15 per cent over the next 5 years. The penetration levels are touted to be hiked by 5 per cent by 2020. (A study by KPMG).

However, use of data insights in insurance industry is comparatively low. Insurance industry by harnessing AI capabilities are able to position themselves to handle market in the ever-changing insurance business. By harnessing machine learning and the ability to read un-structured data, insurance industry can develop a real-time prospects’ be¬havioral and demographic actions, recognize imperceptible changes in the mar¬ket forces that dictate those changes and forecast optimal responses.

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The companies should seek to transform the small commercial segment by harnessing data, artificial intelligence capabilities and advanced modeling techniques. Robotic Process Automation (RPA) is conceived as the answer which combines all the systems and processes end – to – end and makes the entire process uncomplicated.

Key features of RPA:

  • Real – time data available in the system with audit trails and other dashboard functionalities present
  • Automation of claim settlement through well-defined rules in the system
  • Data can be extracted from all channels
  • Claims can be processed from any channel
  • Integration of data, IT and all relevant sources to process the claim
  • Precision matching of all records before authorization                                  (Source: Digital Disruption by KPMG)

The insurance industry has vast amount of data in silos and in unstructured way. We apply machine learning algorithms to identify purchase intent signals. And, by the combination of normalizing the data and converting it into structured data sets and/ or training algorithms the customer need for insurance products can be predicted by analyzing the purchasing patterns. It helps in creating the building blocks for sustainable competitive advantage. Building blocks of segmentation is set of priority life events such as marriage, having a child, buying a home, financial change etc.; these are reliable predictors of life insurance purchase. As it helps to overcome the outdated marketing strategies which are solely based on demographic/ socioeconomic levels.

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It is no wonder that insurance industry must be a practicing statistician to gain more insights from data and a greater understanding of their business to enhance competitive advantage and customer satisfaction.

 

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