According to Juniper Research, “The total insurtech premiums generated by AI systems will reach $20.6 billion in 2024, from just below $1.3 billion in 2019.”

This is a considerable increase. Predictive models are being used by CSRs, underwriters, product managers, sales and marketing, agents, and brokers to solve problems such as policyholder retention as well as to pursue new cross-sell and upsell business opportunities.

Predictive models involve the introduction of technologies such as machine learning, natural language processing, and even natural language generation, which can be daunting. The good news is the development and deployment of predictive models do not have to be complicated.

Developing predictive models start with the understanding of how a particular LOB intends to use the model and what the specific needs are for their business users consuming the output of the predictive model. It is essential to identify realistic goals and strategies for business users to use the output.

Join Jackie Vergne and Dr. Nilesh Karnik from Aureus as they review a customer case study where the life cycle of two predictive models were developed for improving policyholder retention. 

You'll learn from this customer case study:

  • The customer's problem definition and business strategy to act on the output from the predictive models
  • The type of data available and identification of significant variables
  • What was involved in training and testing
  • The requirements for implementation
  • The results yielded from both predictive models

Fill out the form to listen to the live recording and access the presentation of this webinar we held on 11/14/19.

Dr. Nilesh Karnik

Chief Data Scientist, Aureus Analytics

Dr. Karnik is responsible for the development of algorithms and mathematical models that power Aureus' platform and products which help large organizations with advanced analytics solutions.

Nilesh holds a Doctorate in Electrical Engineering from University of Southern California (1998). His PhD dissertation made a substantial contribution to the theory of Type-2 Fuzzy Logic Systems and his work is still widely referenced.

In 2015, Analytics India magazine named Nilesh one of the top 10 data scientist to watch.

Jackie Vergne

Director, Customer Success, Aureus Analytics

Jackie is a senior operations executive with over 20 years of experience in global multi-line property and casualty insurance companies.

She has made significant contributions by strengthening the financial, competitive, operational, and client service performance of insurance businesses.

In her previous roles, Jackie held various operations and underwriting positions with Franklin Mutual Insurance, Swyfft, Duck Creek Technologies, Chubb, Selective Insurance, Fireman’s Fund, and State Farm.

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