Description: A disscussion of actuar R, an R package for actuarial science, and its facilities in the fields of loss distribution modeling, risk theory and credibility theory.

Description: Feature article discussing image recognition and how it may affect actuaries in the future.

Description: This article discusses the publication of the “Encyclopedia of Quantitative Risk Assessment”

Description: an article to offer readers the background of the early career award and its benefits to the actuarial profession and academic community

Description: James Dunseth's MBA journey at Yale’s School of Management and his startup internship taught him invaluable entrepreneurial lessons. Key takeaways include the importance of customer feedback, flexibility, technical skills, effective metrics, and achieving product-market fit. These insights emphasize customer-centric approaches, agile methodologies, technical understanding, and strategic metric use to guide business decisions and foster innovation.

Description: We're introducing a new “cutting edge” wealth accumulation predictive framework, the predictive equity analytics (PEA) for life products (annuity, insurance, group employer benefits, retirement plan). PEA enables life insurers and policyholders to avoid market downturns and capture returns from equities (stocks, ETFs and mutual funds) predicted to increase in next one to three trade days via fund prediction advisory by insurers, additionally by individual policyholders. The capability does not exist in insurance markets nor in financial intermediary markets.

Description: Biases can creep up in models in numerous ways. After discussing various forms of bias, presenting a framework useful to manage bias in models, and contextualizing this effort in the current environment, this session will focus on practical issues faced by the industry.

Description: As data continues to become more robust and readily available, there has been a push in the actuarial field to transition away from Excel to SQL and Python native cloud-based platforms. While these platforms are widely used and powerful, they present their own challenges for the actuarial industry, requiring processes to be production-ready from the onset, technical expertise for integration, and robust requirements documentation. Low-code tools provide a unique alternative solution, which works well when the organizations operating model allows for actuarial and IT to work together, giving actuaries the flexibility they need while maintaining a level of control followed by the organization. Make use of demonstrations to showcase low-code tools (such as Alteryx and Dataiku) and their use case in the actuarial space. Learn how these low barrier-to-entry tools can be leveraged to create robust and controlled processes. Discuss how low-code tools can integrate with coding languages such as Python as well as efficiently prepare data for downstream visualization platforms such as Tableau and PowerBI.

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).