POWERING WORKER’S COMPENSATION CLAIM HANDLING WITH MACHINE LEARNING

Workers’ compensation is a large and important segment of insurance business. And like elsewhere in the domain, this field too is witnessing an ever increasing role of advanced analytics in core operations: marketing, risk management, underwriting, actuarial pricing and reserve calculation. So far, one of the most important application has been in predicting the future of a claim to facilitate early intervention for containing the prospective damage or loss. Successful commissioning of intervention mechanisms driven by analytics in general and Machine Learning in particular, is now a fast emerging way to grow profits in the insurance industry.

This talk presents a high level introduction to some of the predictive models under use in claim handling business. Although it is the hope that experts enjoy the contents, special effort is towards making it stimulating and motivating for non-technical audience as well. Actually anyone interested in the idea of automated data driven decision engines for business applications is welcome.

 
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Outline/Structure of the Talk

  • Introduction to Workers' Compensation Insurance and Claim Handling
  • Opportunity areas for Advanced Analytics and Machine Learning
  • Use-cases like finding opportunities for recoveries on claim losses, identification of claims with high risks and so on
  • Recommend solutions to business e.g. alert activation and early intervention
  • Summing it all, future vision and conclusions

Learning Outcome

  • Identification of Advanced Analytics and Machine Learning Opportunities in Business
  • Going from Concept Stage to a Final Product

Target Audience

executive decision makers, data scientists, business analysts and anyone interested in data driven solutions

Prerequisites for Attendees

A general interest in data driven decision making

schedule Submitted 3 months ago

Public Feedback

comment Suggest improvements to the Speaker
  • Usha Rengaraju
    By Usha Rengaraju  ~  2 months ago
    reply Reply

    Dear Lovedeep,

    Thank you for the proposal submission .Kindly mention the predicitve models which you will be covering as part of this talk.

    Thanks and Regards,

    Usha Rengaraju

    • Lovedeep Saini
      By Lovedeep Saini  ~  2 months ago
      reply Reply

      Hello Usha, Many thanks for your email.

      I would talk about the usage ensemble methods in ML to flag the Litigation and Loss Recovery opportunities in insurance claim handling.

      Thanks,

      Lovedeep

       

      • Dr. Vikas Agrawal
        By Dr. Vikas Agrawal  ~  1 month ago
        reply Reply

        Dear Dr. Saini: Thanks for your submission! Will you be sharing specific details of the algorithms included in the ensemble so that the ODSC audience has a chance to go back and apply those in their workplace, or do you expect to share a general overview of the process? Warm Regards, Vikas

    • milan talreja
      By milan talreja  ~  2 months ago
      reply Reply

      I like these aspects of the submission, and they should be retained:

      • ...role of advance analytics

      I think the submission could be improved by:

      • ...more on implementation and allowing for adoption of these ideas and recommendations
    • Nirav Shah
      By Nirav Shah  ~  2 months ago
      reply Reply

      Hello Lovedeep ,

      Thanks for your submission. As part of Program Committee, we request you to provide a short video of your session. You can record it on your phone or camera and upload here.

      Also, if you have some relevant slides, please upload it for review.

      Thanks,

      Nirav

      • Lovedeep Saini
        By Lovedeep Saini  ~  2 months ago
        reply Reply

        Hello Nirav,

        I've linked slides along with audio/video recording at relevant links above, please have a look. 

        thanks for your time and effort ,

        Lovedeep

    • Jeffrey Austin White
      By Jeffrey Austin White  ~  3 months ago
      reply Reply

      I like these aspects of the submission, and they should be retained:

      • practical information on how to use predictive models in a business setting

      I think the submission could be improved by:

      • mentioning some of the data modeling techniques that will be discussed
      • milan talreja
        By milan talreja  ~  3 months ago
        reply Reply

        I am familiar with Dr. Saini's work and looking forward to her presentation with great anticiaption