Incentivize existing policies for a leading insurance company
2
case in point blueoceanmi.com | 1 Title: Incenvize exisng policies for a leading insurance company Industry: BFSI Country: India Challenge • A leading Insurance company was required to access the lapsed insurance policies having a potenal of repayment (and hence reacvaon), within a specific me bracket • Idenfy in which criteria can the exisng in-force policies can be incenvized • Variety of datasets pertaining to different types of policies had to obtained and processed thereaſter Approach • The two policies Tradional and ULIP were in two states - Inforce & Lapsed • Data cleaning was done using a proprietary machine learning tool • A binary logisc regression was applied on each of the policies with lapsed and inforce data Result • Factors that effected the predicve model were • Premium to be paid • Income of the policy holder • Occupaon and the total sum assured at the end of maturity • It was derived that it was always good to approach the lapsed policies within a specified me bracket aſter which the policies may get permanently lapsed FPO
-
Upload
blueoceanmi -
Category
Documents
-
view
218 -
download
0
description
From the lapsed insurance policies, identified ones that potential of repayment and used predictive models to device approach on preventing permanent lapse.
Transcript of Incentivize existing policies for a leading insurance company
case in point
blueoceanmi.com | 1
Title: Incentivize existing policies for a leading insurance companyIndustry: BFSICountry: India
Challenge• A leading Insurance company was required to access the lapsed insurance policies having a potential of repayment
(and hence reactivation), within a specific time bracket • Identify in which criteria can the existing in-force policies can be incentivized • Variety of datasets pertaining to different types of policies had to obtained and processed thereafter
Approach• The two policies Traditional and ULIP were in two states - Inforce & Lapsed• Data cleaning was done using a proprietary machine learning tool • A binary logistic regression was applied on each of the policies with lapsed and inforce data
Result• Factors that effected the predictive model were • Premium to be paid • Income of the policy holder • Occupation and the total sum assured at the end of maturity• It was derived that it was always good to approach the lapsed policies within a specified time bracket after which the
policies may get permanently lapsed
FPO
© Copyright 2013 blueocean market intelligence, All Rights Reserved.
2889 152nd Avenue NEBuilding 12, Suite DRedmond, Washington 98059 USA
Phone: 425.615.7474Fax: 425.296.2110
Vrindavan Tech VillageBuilding 2A, Ground Floor, East TowerSarjapur Outer Ring RoadBangalore, 560 037 INDIAPhone: 91.80.40578100
Contact:
Durjoy PatranabishSenior Vice President, [email protected]
About blueoceanblueocean market intelligence is a next-generation services organization with a deep focus on analytics, market intelligence and social intelligence, all uniquely delivered under one roof by 750+ plus professionals with deep domain expertise.
Our blended offshore/onshore delivery model, coupled with our vast experience in scalable delivery, enables us to deliver richer, more actionable insights very cost-effectively and with the speed and quality that today's business climate demands. In addition, our 360 Discovery™ process insures the comprehensive utilization of all available structured and unstructured data sources, enabling us to bring the best to bear against each and every project that we execute.
www.facebook.com/blueoceanmi www.twitter.com/blueoceanmi
For more information, visit www.blueoceanmi.com
![Joint Caching and Pricing Strategies for Popular Content ...narayan/NSFProspectPricing/07859260.pdfnetworks [27]–[31]. Using contemporary pricing policies can-not incentivize the](https://static.fdocuments.us/doc/165x107/5f0e43ac7e708231d43e6689/joint-caching-and-pricing-strategies-for-popular-content-narayannsfprospectpricing07859260pdf.jpg)