Skip to main content

Overview and Practical Applications of Machine Learning Methods in Pricing - Part 2

Loading video

This video is currently being processed. It will be ready for viewing shortly.

A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.
A small thumbnail of this item.

Have an attendance code? Sign In to enter it.

For those who watched live in a group, to be marked as completing this webinar archive, please enter the Attendance Code that was e-mailed out post-event to your group organizers.

Description

This topic is considered Part 2 of the Machine Learning Webinar given on February 7, 2019.

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

The term “machine learning” covers a range of methods that can be powerful, with very practical benefits, in pricing and other insurance applications. Such methods can aid in further improving Generalized Linear Model (GLM) results or more broadly bring valuable insights to complex problems. There can also be a number of practical challenges in using these methods effectively. This session reviews a range of commonly used methods and illustrates different ways they are being applied in insurance, including some case study results. This session will focus on the high-level mechanics of each method and the benefits/challenges of their application – as opposed to the underlying technical details.

Intended Audience:
This webinar will be of interest to all CAS members, and of particular interest to actuaries working with Accounting/Financial Reporting, Predictive Modeling, Pricing/Ratemaking, and Reserving.

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

Registration Information and Fees

Registration Fees (in U.S. Dollars)
Received on/by
February 13, 2020
Received after
February 13, 2020
Individual $50 $75
Group*
(more than one person using the same internet connection)
$250 $300
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)
$500 $550
*Group Registrations will receive a code during the webinar with which they can use to count their attendance.

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

Cancellations/Refunds
Registrations fees will be refunded for cancellations received in writing at the CAS Office via fax, 703-276-3108, or email, refund@casact.org , by February 20, 2020 less a $25 processing fee.

CAS Continuing Education Policy
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.

CAS Webinars may qualify for up to 1.8* CE Credits for CAS members. Participants should claim credit commensurate with the extent of their participation in the activity. CAS members earn 1 CE Credit per 50 minutes of educational session time, not to include breaks and/or lunch.
*The amount of CE credit that can be earned for participating in this activity must be assessed by the individual attendee. It also may be different for individuals who are subject to the requirements of organizations other than the Casualty Actuarial Society. 

Contributors

  • Ben Williams

    Ben is a director with Insurance Consulting and Technology, and the Regional Product Leader for Willis Towers Watson’s Pricing, Product, Claims and Underwriting practice for the Americas. He is based in the New York office. His background is in P&C pricing, and he is a specialist in predictive modeling and applications. He is a frequent speaker at conferences on predictive modeling and applications, and has authored several articles on these topics. Prior to joining the New York office, Ben consulted for Willis Towers Watson in Madrid, Spain. Before then, Ben worked for Liberty and Allianz in Europe, in internal consulting roles aimed at establishing best practices in pricing and underwriting across those groups.

  • Graham Wright

    Graham is a senior director at Willis Towers Watson with over 12 years of experience working mainly in the UK personal lines market where he leads Willis Towers Watson’s UK P&C Personal Lines Pricing practice, within the Pricing, Product, Claims and Underwriting division Graham’s particular areas of expertise include personal lines pricing, data, underwriting and distribution best practice, and claims reserving. He has led multiple client projects ranging across data and data enrichment, underwriting, technical pricing methods and approaches, retail pricing and price optimization. He continues to promote the ongoing development of analytics within pricing, as part of which he has led global projects around the world embedding machine leaning techniques within business as usual pricing. Graham has led a number of best practice pricing and underwriting reviews for personal lines clients and has a deep knowledge of the UK personal lines market, especially for motor and household lines.

February 27, 2020
Thu 12:00 PM EST

Duration 1H 30M

This live web event has ended.

For Technical Support
+1 (858) 201-4136