Description: Discuss various analytics and use cases using payer published reimbursement contract data under federal mandate of Transparency in Coverage. These contract rates are publicly available in machine readable format (MRF) starting July 1, 2022, and refreshed monthly. By attending the session, you will have a better understanding of the state-of-art development and analytics using MRF data, including the technologies used to extract and analyze huge MRF files, validation and limitations of published rates, and their use cases.

Description: The Introduction to Modeling: Summer Boot Camp series provides an overview of modeling topics and techniques aimed at the beginner modeler. This interactive series is comprised of five 90-minute sessions with opportunities to discuss the topics in small break-out groups.

Description: Machine learning models are not black-boxes; these models can easily be interpreted using tools like SHAP, LIME and Anchor. These tools can facilitate the understanding of the model and the business problem. We can used these tools to build better models and communicate how predictions are generated. This article demonstrates their use in a mortality model.

Description: This is a discussion of the paper, New Possibilities in Graduation by Kingsland Camp. From Transactions of Society of Actuaries 1955, Vol. 7, No. 19.

Description: Dive into this review on Artificial Intelligence and Machine Learning (AIML) techniques for actuarial modeling for life & annuities. Presenters will showcase learnings from the recent SOA research paper "Predictive Analytics and Machine Learning – Practical Applications for Actuarial Modeling" (Nested Stochastic). The researchers will go over models and methodologies used to develop high-fidelity proxy models for various life & annuity applications.

Description: Cluster modeling is emerging as a powerful technique to manage cloud costs and run time. It also enables quick responses to "what if" scenarios, and aids in attributing results between reporting cycles. In this webcast, we provide insight into cluster modeling, present case studies that focus on various key performance indicators and discuss the benefits and challenges to cluster modeling from an insurer’s perspective. We also discuss the considerations associated with moving from an on-prem based computing solution to a cloud-based approach.

Description: Health Insurers commonly face challenges regarding data including: Limited knowledge of how to handle large datasets Lack of a common language to discuss data challenge Limited understanding of how to use data and analytics to inform and facilitate decision-making We are seeing a turn in the market to use data and analytics to cultivate an insights-driven culture. But we are noticing a disconnect between the actuaries that are able to glean these insights and the new technologies required to handle the amount of data available to them. During this session you will look at moving from MS Excel to more advanced data handling and modeling tools (e.g., Alteryx), visualization tools (e.g., Power BI & Tableau) and expanding utilization of these tools and analytics. By attending this session, you will: • Understand data and analytics (D&A) concepts, methods, and fundamentals to help drive value. • Gain an appreciation for data privacy, protection protocols, and related ethical considerations when working with data. • Be able to identify of how to use data and analytics to inform and facilitate decision-making.

Description: Modeling teams are getting larger to deal with more complex models and to support more enhancement requests from business and actuarial teams. Actuarial modeling teams can adapt tools and techniques used in the broader IT industry to be more efficient, handle large number of requests while maintaining their quality of work. Explore DevOps tools to track requests, progress made, work assignment and version control systems. See Azure DevOps in action as presenters use it as an illustrative tool to showcase these activities and how they can be beneficial to an actuarial modeling team (regardless of their preferred actuarial modeling system).

Description: This is a presentation of recursive APL programs for Steffenson's and Romberg's algorithms. The programs call themselves repeatedly until terminating conditions are reached. From the Actuarial Research Clearing House ARCH 1987 Vol. 1.

Description: Ever wanted to do a Kaggle data science competition, but didn't know where to start? This presentation meets the needs of actuaries interested in predictive analytics and competitive data science. The presentation will describe the nature of competitive data science, and provide insights into lessons learned by competing in the Airbus Ship Detection Challenge at Kaggle, as well as other Kaggle competitions. The presenters will provide an introduction to neural networks and describe the nature of the Airbus Ship Detection Challenge and other Kaggle competitions, plus a solution approaches that can be used to obtain top scores using Convolutional Neural networks (CNNs), random forests and boosted trees.