With insurance benefiting so greatly from predictive analytics,
PAW Business offers these insurance-related sessions in San Francisco & Chicago.
Whether auto, health, life, P&C, or other forms of insurance, predictive analytics adds value. Predictive models play a role across many aspects of insurance, from determining risk scores and premiums to calculating expected value and managing claims. With interest and activity rapidly growing in this arena, PAW Business includes numerous insurance sessions to amplify your potential with predictive analytics.
5 sessions in a row covering insurance applications
at PAW San Francisco (March 29 - April 2, 2015) |
Keynote: Advances in Predictive Analytics Deployment at MetLife
Keynote Speaker: Bin Mu, MetLife
Discover how to establish value and make an impact through a strong analytics framework demonstrative of a high ROI
 What Unique Data Assets Reveal about Auto Insurance Shopping & Consumers
John Ittner, TransUnion
Analyze personal passenger auto insurance shopping levels and trends and how credit data and shopping activity are combined with demographic data.
 A Success Story: Sales and Revenue Forecasting through Predictive Analytics
Dominic Fortin, Co-operators
Hear a success story about employing predictive models at TD Insurance to predict sales, revenues, cancellations and more.
  Predictive Claims Management:
Identifying the Optimal Repair or Loss Channel
John Haller, CCC Information Services
Joseph Laurentino, Esurance
Learn how a decision engine and diligence turned an idea into a successful predictive model.
 Predicting Extreme Behavior to Improve the Rating Structure for Travel Insurance
Richard Boire, Boire Filler Group
Witness how predictive tools were applied in order to build accurate models that optimized the prediction of loss and saved $1.2MM.
3 sessions covering insurance applications
within a dedicated "Financial Services" track
at PAW Chicago (June 8 - 11, 2015) |
What Unique Data Assets Reveal about Auto Insurance Shopping & Consumers
John Ittner, TransUnion
Analyze personal passenger auto insurance shopping levels and trends and how credit data and shopping activity are combined with demographic data.
 
Laying the Groundwork to Predict Risk Trends around Worker's Compensation Insurance
Paul Hughes, Risk Transfer & Mitch Hamilton, Data Blueprint
This case study will highlight Risk Transfer's journey from simply getting data into a usable format to actually using it for data-driven decision-making.
 Journey: Customer Retention from the bricks of Data to the Dome of a Model
Boris Kerzhner, Slalom Consulting
Take part in a very exciting journey of going from customer data to churn modeling that predicts the retention of a customer at a healthcare giant.
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211 E. Victoria Street, Suite E
Santa Barbara, CA 93101 |
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Topics: Coronavirus | AI | Data Science | Deep Learning | Machine Learning | Python | R | Statistics
