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Building Analytics into the Insurance Value Chain

In times when Insurance industry is facing stiff competition and harsh regulatory regime, it becomes imperative for Insurers to develop capabilities for pro-active interventions. Such a capability can be developed through embedding Analytics in   day to day decision making which when combined with business judgement can deliver powerful results. Infusing analytics into the DNA of the company can help Insurers identify opportunities that are not easily visible. Predictive Analytics has achieved successful results for many Insurers worldwide.
  • A leading Insurer utilized customer behavioral analytics to increase profits by more than 2% of GWP.
  • An Australian life insurer used Predictive analytics to reduce lapse rate by 1.1% in six months, resulting in 7% increase in NPAT.
Analytics across Value Chain
Predictive Analytics can be adopted across different functions, namely, Customer Acquisition, Customer Retention, Agency Channel Productivity improvement and retention, New product development etc. Below graph represents key areas where analytics can be applied.


Starting your journey?
Business leaders and stakeholders often think about right time to start looking at analytics and sometimes fall shy due to concerns surrounding data availability, quality of data, conflicts between internal stakeholders and ROI from such an initiative. However Insurers need to develop robust analytics capabilities now more than ever. Following things need to be addressed for achieving success
  • Define your business objective clearly & start with a Pilot project
  • Take all stakeholders on board from respective functional teams, IT teams and other key decision makers.
  • Identify and involve right talent for execution.
  • Ensure right implementation and training of team members.
  • Have a proper feedback mechanism to capture results that will measure the success of initiative and help refine it further. 

Key Initiative Areas

  • Persistency Improvement 
  • Agency Channel Retention and Productivity Improvement
  • Policy Mis-Selling
  • Claim Fraud Detection

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Comment by Rohit Pandey on July 21, 2014 at 6:15am

Hi Sagar, 

Good to hear from you. 

For fraud detection, customer retention, we used predictive models based on R, SAS platforms. It's good to know that you took interest in our article. You can visit our website for more info about our solutions. Be in touch 

Comment by Sagar Diwakar Uparkar on July 20, 2014 at 5:38am

Hi Rohit,

Good evening. How are you? I like your article but I would like to know more about it. Which predictive models do you use? for example for fraud detection, customer retention etc. Since I am not from insurance back ground so I am keen in it. 

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