Description: Explore similarities between actuaries and data scientists as speakers share personal experiences on the importance of organizational structure. Attend the Actuary, Data Scientist, or Actuarial Data Scientist webcast to discover the effectiveness of partnering both of these career paths in a life insurance atmosphere. In addition to personal experiences, speakers will discuss several success stories where collaboration and connection became the key to driving innovation. Come learn about the progress these professions have caused in pricing and risk management.

Description: You’ve written the position papers, updated the models, and designed the required disclosures, but you may be wondering how to explain the results during interim financial reporting periods to company management as well as other stakeholders. This session will explore ways to adapt existing management reporting and offer ideas for new metrics. We will explore various methods for analyzing DAC amortization patterns, market risk benefit reserve patterns, and Liability for Future Benefits (LFPB) movements. In addition, this session will explore ways to integrate changes in LDTI with business planning and other broader business issues such as product pricing considerations and reinsurance strategy.

Description: Actuarial Process Optimization (APO) is a technology-focused methodology for bringing together actuarial and technology teams to critically examine and optimize actuarial processes, specifically targeting processes that are most time consuming and have the highest risk of failure. From claims analytics to valuation and experience analysis, modern technology can help actuaries optimize their time and focus their attention on higher value tasks. In this session, we will explore opportunities for APO in the Health insurance industry and demonstrate how technology can be used to improve the actuarial process using an agile and targeted approach.

Description: This article provides an overview of activities that should be considered by an actuary when validating a predictive model. It describes several components to consider when validating a predictive model and provides an examination of various methodologies that can be implemented in the validation process.

Description: This article offers ways to make use of Excel more efficient, by applying software development and design principles

Description: During the2019 InsureTech Connect conference in Las Vegas, the Society of Actuaries (SOA) and Plug and Play today announced the two organizations are nearing an agreement on professional development offerings and learning opportunities. The SOA and Plug and Play plan to work together on a variety of InsurTech-focused events and projects for the SOA membership, insurance industry and technology sector.

Description: 2021 ImpACT Conference - Session 10E: Azure Synapse for Actuaries | Solvency II Use Case 10.28.21 Recording

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: The popular press plays an important role in public perception regarding automated vehicles. In contrast to the excitement generated two to three years ago, we are now in a period of “reverse hype” with most articles focused on the slow pace of development and concerns regarding possible negative impacts on traffic congestion and growth of suburbs. To help describe current trends and highlight differences between the cars we see on the street today and driverless vehicles, this report relies on three market-related definitions; safe, self-driving and driverless.