2020 Hachemeister Prize Announcement

The 2020 Hachemeister Prize has been awarded to Ronald Richman for his paper, “AI in Actuarial Science,” presented at the 2019 ASTIN Colloquium. The Charles A. Hachemeister Prize is awarded annually to ASTIN Bulletin or ASTIN or AFIR Colloquium paper(s) based on several criteria but with emphasis placed on the paper’s impact for North American actuaries and its practicality of application.

Mr. Richman's paper investigates how actuarial science may adapt and evolve in the coming years to incorporate new techniques and methodologies that fall under the headings of Artificial Intelligence, Machine Learning, and Deep Learning. The paper surveys emerging applications of artificial intelligence in actuarial science, with examples from mortality modeling, claims reserving, non-life pricing, and telematics, and then concludes with an outlook on the potential for actuaries to integrate deep learning into their activities.

The committee believes that Mr. Richman’s paper is impactful and valuable for Actuaries to read because:

• The paper is a well-written survey of various Artificial Intelligence and Machine Learning methods that represent a new frontier for practicing actuaries.
• Several practical examples in multiple areas of actuarial science are presented.
• To aid in understanding and application, the author has made the code for some of the examples available through his Github repository.

Mr. Richman has been invited to present his prize-winning paper at the 2020 CAS Annual Meeting in Washington, DC.

There were many good eligible papers this year, and the committee wanted to specifically recommend the following paper for additional reading for actuaries in any area of discipline:

• “Predictive Analytics of Insurance Claims Using Multivariate Decision Trees” by Zhiyu Quan and Emiliano A. Valdez provides clear explanations of tree-based modeling methods with an extension to multivariate response variables.

Furthermore, the committee thought the following papers would make for valuable additional reading for actuaries in specific practice areas, but may be of general interest:

• “Property Graphs – A Statistical Model for Fire and Explosion Losses Based on Graph Theory” by Pietro Parodi and Peter Watson presents a clear exposition of a method for developing a severity distribution from first principles using graph theory.
• “Cat Bond Pricing Under a Product Probability Measure With POT Risk Characterization” by Qihe Tang and Zhongyi Yuan describes an interesting approach to pricing catastrophe bonds based on both the underwriting and financial risks.
• “Index Insurance Design” by Jinggong Zhang, Ken Seng Tan, and Chengguo Weng addresses optimal index insurance design using an expected utility maximization framework, with applications under several non-linear utility functions. This paper is useful for insurance products where the loss is based on a specified index rather than on the insured's actual financial loss.