Description: Often actuarial practitioners are faced with working with data that is less than ideal. The data may be observed with gaps in it, a model may suggest variables that are observed at different frequencies, and sometimes predictive analytic results are very fragile to the inclusion or omission of just a few observations in the sample. Data, particularly big data, are often "messy" and something must be done about it. What is the actuary to do about these very practical matters? These types of questions are what this presentation is intended to address. Concerns on missing data, outliers, nonlinearity, mixed-frequency, cross-correlations are illustrated using real datasets in the Asia-Pacific countries. Some possible solutions to the messy data problems will be discussed.

Description: A description of the operation of an R package for experience studies. Premium patterns, lapse rates, and mortality rates can be calculated.

Description: This is an Editorial describing the need for actuaries to upgrade skills or risk minimization from artificial intelligence.

Description: In this video, Dave Ingram FSA MAAA, a SOA board member and podcast host, discusses the ten AI risks that insurers face and the specific mitigations that can be used for each of them. He also explains the importance of having an AI Use Policy that covers various aspects of data governance, ethical guidelines, transparency, fairness, human oversight, and compliance. He concludes by noting that AI will evolve and insurers will need to monitor and remediate their AI applications accordingly. Contributors: Dave Ingram FSA, MAAA; Chis Lombardi, FSA, MAAA; Jon Forster, ASA, MAAA

Description: Often actuarial practitioners are faced with working with data that is less than ideal. The data may be observed with gaps in it, a model may suggest variables that are observed at different frequencies, and sometimes predictive analytic results are very fragile to the inclusion or omission of just a few observations in the sample. Data, particularly big data, are often "messy" and something must be done about it. What is the actuary to do about these very practical matters? These types of questions are what this presentation is intended to address. Concerns on missing data, outliers, nonlinearity, mixed-frequency, cross-correlations are illustrated using real datasets in the Asia-Pacific countries. Some possible solutions to the messy data problems will be discussed.

Description: Description of methods for extracting and reformatting the existing mortality tables using R. Methods and formats for storage are discussed in the second article. Intent is to generate discussion regarding the format of our stored mortality tables. Value is in the demonstration of task automation in R and in the reduction of duplicate work across companies related to reformatting tables.

Description: This video by Arthur da Silva, FSA, FCIA provides a primer on Application Programming Interfaces (APIs), which are mechanisms that enable communication and integration between different software components. The video explains what APIs are, how they work, and how they are used by actuaries today and in the future. The video also discusses the benefits and trade-offs of APIs, as well as some examples of common and actuarial-specific use cases.

Description: In this video Jing Lang emphasizes the importance of ethical considerations in AI utilization, particularly focusing on privacy, bias, transparency, and creativity. It highlights the need for collaboration among various stakeholders to address these concerns through regulations, diversity promotion, and ongoing discussions. Sources of bias in AI, such as underrepresented datasets and historical biases, are identified along with strategies to mitigate bias, including careful assessment of inputs and retaining algorithms. Contributors: Jing Lang, FSA, FCIA, MAAA FLMI; Scott Allen, FSA, MAAAA, Henry Chen FSA, FCIA, MAAA; Jon Forster ASA, MAAA

Description: Yi-Ling Lin, FSA, MAAA, FCA, started the SOA’s Predictive Analytics Certificate Program to keep pace with the impact big data and predictive modeling is having on the health care industry

Description: Article describing content and key takeaways from experience completing the SOA PA Certificate