Description: Crossword puzzle for the readers of Risks & Rewards newsletter.

Description: This paper is an introduction to AI technology designed for actuaries to understand how the technology works, the potential risks it could introduce, and how to mitigate risks.

Description: You will be reminded how to spot and avoid statistical biases and fallacies that are common to health care actuarial data and problems. We plan to explain various statistical biases and fallacies and for each show one or two examples of how they might pop up in healthcare data and what strategies can be used to avoid/account for them. Likely topics include simpson's paradox, regression to the mean, base rate fallacy, sampling bias, survivor bias, Berkson's fallacy, and others.

Description: Welcome to the future of insurance, for actuaries and non-actuaries! Discover how cutting-edge AI models are revolutionizing underwriting, risk assessment, and customer experience for the insurance industry. Experts in management consulting and actuarial consulting firms will provide valuable insights into the consideration and challenges surrounding the adoption of Generative AI.

Description: Actuarial modernization is transforming the profession, and coding plays a central role. Coding skills enable actuaries to automate data manipulation, streamline model development, and effectively harness advanced analytics. This session explores why coding is foundational to the future of actuarial work, highlighting practical use cases, essential technologies, and career benefits.

Description: A typical course has some "hard" parts. These "hard" parts are the places where weaker students begin to fail, lose interest, and give up. The paper introduces a metric by which to evaluate each course topic. High metric values indicate "hard parts" of courses. The paper recommends spending more course time on hard parts and less course time on easy parts. The paper also recommends using disclosure: Tell students - this part of the course is "hard" and they may feel uncomfortable which is why more time is being spent on it." This approach to hard parts works! Failure rates are significantly reduced, while retention, satisfaction, and student scores improve.

Description: The actuarial profession is marching into the age of Big Data. Increasingly, employers in various actuarial fields are requiring new hires to have sophisticated knowledge in statistics and data science. To meet the new demands from the industry and the profession, one of the key additions to the SOA’s new curriculum is an exam in Statistics for Risk Modeling (SRM) which includes modern statistical and data science techniques. There is also a new proctored project on Predictive Analytics, as a capstone requirement following the completion of the SRM Exam. At the University of Toronto, we recently underwent a major actuarial science curriculum redesign, in order to stay current with new trends and demand from the profession and in line with the new professional curriculum.

Description: In this article the author discusses the many hats an actuary wears and how risk management comes to play in each one. The roles discussed: 1. Pricing actuary. 2. Catastrophe modeling actuary. 3. Ceding reinsurance actuary. 4. Corporate actuary. 5. Loss reserving actuary. 6. Predictive modeling actuary. 7. Marketing actuary. 8. Underwriting actuary. 9. Investment actuary.

Description: This article is about the annual call for ERM-related research papers that present the very latest in ERM thinking and move forward principles-based approaches. The call discussed in this article was issued in October 2005 and attracted nearly 30 entrants, from which there was one winner and eight more that were selected for presentation at the 2006 ERM symposium. They were listed in the article and can be found, along with the 20 other entrants on the ERM Symposium Web site at: www.ermsymposium.org.