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Derivative Lasso: Credibility-based signal fitting for GLMs

Description

PLEASE NOTE: REGISTRATION WILL CLOSE 10 AM ET THE DAY OF THE WEBINAR. DUE TO HIGH VOLUME OF DEMAND, WE ARE ONLY ABLE TO GUARANTEE A SEAT TO THOSE WHO REGISTER 48 HOURS AHEAD OF THE WEBINAR TIME.
Derivative Lasso is a cutting edge machine learning technique that seamlessly merges actuarial credibility, robustness and interpretability into a transformative actuarial pricing tool. This method is consistent with the Lasso Credibility concept covered in the upcoming CAS monograph. Where traditional GLMs are viewed as highly manual due to feature engineering being an overly iterative process, Derivative Lasso advances the field, embedding this process directly within its core. Using real-world data, this session will spotlight the challenges in current GLM modeling and unveil the power and precision of the Derivative Lasso framework. Attendees will discover how it automates feature engineering, fortifies model robustness, and elevates interpretability, marking a significant leap in penalized regression modeling that keeps GLMs on par with newer modeling frameworks.

Learning Objectives:
  1. Evaluate the limitations of traditional GLMs in terms of manual feature engineering and iterative processes.
  2. Apply the Derivative Lasso technique to automate feature engineering while maintaining model robustness and interpretability.
  3. Compare the performance and efficiency of Derivative Lasso to traditional GLMs, focusing on improvements in automation, robustness, and interpretability.
Registration Information and Fees

PLEASE NOTE: REGISTRATION WILL CLOSE 10 AM ET THE DAY OF THE WEBINAR

Registration Fees (in U.S. Dollars) Received on/by
May 30, 2024
Received after
May 30, 2024
Individual $50 $75
Multiple Connections**
(Unlimited internet connections for individuals working for the same company. Please note that audio for this presentation will be streamed via the web)
$600 $650

*Multiple Connection Registrations should contact Mallory Peebles directly at
mpeebles@casact.org. The registering party for the Multiple Connection Registration will be responsible for distributing all event details to attending individuals within their company.

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Cancellations/Refunds
Registrations fees will be refunded for cancellations received in writing through email, refund@casact.org, by June 6, 2024 less a $25 processing fee.

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The CAS Continuing Education Policy applies to all ACAS and FCAS members who provide Actuarial Services. Actuarial Services are defined in the CAS Code of Professional Conduct as “professional services provided to a Principal by an individual acting in the capacity of an actuary. Such services include the rendering of advice, recommendations, findings or opinions based upon actuarial considerations.” Members who are or could be subject to the continuing education requirements of a national actuarial organization can meet the requirements of the CAS Continuing Education Policy by satisfying the continuing education requirements established by a national actuarial organization recognized by the Policy. For further information regarding the CAS Continuing Education Policy please visit the CAS web site.

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Contributors

  • Max Martinelli

    Max Martinelli is an Actuarial Data Scientist for Akur8. He has more than 9 years of experience in actuarial and data science roles, working primarily in predictive modeling for P&C insurance. He has a background in machine learning and computational mathematics. When he isn’t helping Akur8 clients get the most out of their data, he can be found working on STEM projects with his sons.

  • Mattia Casotto

    Mattia Casotto is the Head of Product US and Principal Scientist for the pricing software Akur8. He has more than 9 years of experience on predictive modeling in insurance and is part of the founding team of Akur8. He is one of the co-authors of the paper ‘Derivative Lasso‘ and ‘Credibility and Penalized Regression’.

June 13, 2024
Thu 12:00 PM EDT

Duration 1H 30M

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