Description: As technology changes the insurance landscape, it is essential that companies adapt quickly while staying focused on customer needs. This presentation will show how Agile and Design Thinking methodologies can help business and technology teams quickly test new ideas and optimize value delivery.

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: How diverse and inclusive (D&I) are we as pedagogical practitioners in the ever-changing student and actuarial science industry landscape? Given that our in-class experience impacts many populations, how successful are we dealing with conflicting belief structures and creating a welcoming and productive environment for members of the underrepresented (LGBTQIA+) and minority groups in our actuarial science classes? This interactive session will aim to provide a working knowledge of issues that arise and how to successfully address them.

Description: A flipped classroom moves the traditional lecture component of teaching outside of normal classroom time, so that lectures can be replaced with active learning activities during class time. In this talk, I first present a brief overview of flipped classrooms, with strategies for implementation and resources provided. Second, I will discuss the details of its application replacing two lectures in my third-year Life Contingencies course. Students’ responses to the technique are also provided in the form of data from anonymous post-activity surveys.

Description: Traditionally the SOA formulates pedagogic excellence in terms of the Marzano Taxonomy. For example, the "analysis level" consisting of generalization, specification, classification, contrast, matching, and error identification, is considered superior to the "retrieval level" consisting of rote memorization such as recognition, recall, and using. In this presentation, we first show that pedagogic excellence can be equivalently, but more simply, formulated in terms of executive function and goal-setting. Here executive function refers to problems requiring multiple mental modalities - such as, formal, verbal, computational, and graphical - for their solution. Goal-setting refers to the skillful breakup of a complex problem into a sequence of subgoals each of which is clearly formulated, challenging, but achievable timely. Light examples from the literature illustrate these concepts. Five "challenging FM problems": reinvestment, refinancing, multiple rate/deposit problems, pricing deferred bonds using a term structure, and obscure loan problems, illustrate the following fundamental techniques of goal-setting and executive function: i) a fellowship approach to teaching preliminary exam material, ii) using subgoals to give difficult homework, iii) alternating between different mental modalities to achieve elegant brevity in solutions, iv) goal-setting reflecting familiar vs. new, and v) skillful definition of unit subgoals.

Description: In the insurance industry, machine learning models are increasingly used to make decisions that affect people's lives. In order to ensure that models are fair, they should be audited for bias. There are no hard and fast rules for bias audit; metrics that can be used to evaluate fairness vary depending on the context in which the model is deployed. In this session, we present a flexible framework that highlights key considerations in bias evaluation that can be adjusted to suit your process. In this session, we will learn about different metrics to assess fairness, discuss open source tools that can be used for bias audit, and walk through a case study.

Description: Transitioning from college life to actuarial practice can be intimidating for students. Even after almost four years of studying actuarial science, many still wonder “what does an actuary do all day?” Our capstone course, Actuarial Applications in Practice, is designed to bridge the gap by exposing students to projects similar to what they might encounter in their first years in the industry, including how to present their results in both written and oral presentation formats. The course also incorporates Ethics and Professionalism topics to give them the tools they need to make sure the job is done right.

Description: Predictive modeling is a powerful toolset for assumption setting (mortality, lapse, etc.). The multivariate approach has its advantages in handling high-dimensional data, address correlations, and statistical significance. However, building insurance assumptions is far more than a simple modeling practice. One must take care to address how the model is applied and its business implications. This session will share the learnings from multiple projects.

Description: The arrival of ASU 2018-12 (a.k.a Targeted Improvements to the Accounting for Long-Duration Contracts or GAAP Targeted Improvements) presents insurers with augmented financial reporting risk across all areas of the valuation process. Naturally, the internal control frameworks that serve to mitigate these risks must evolve and expand. Valuation actuaries should view this transformative event as an opportunity to take an active role in developing new and improved controls that are both effective and sustainable.

Description: Simple mathematical equations often outperform very complex ones in predictive analytics (PA) situations. This session will cover "why" and "how" to guard against overfitting and other potential pitfalls that might not be intuitive.