Description: Modern Actuarial work, being quite data intensive, provides many opportunities for actuaries to use the statistical skills that are part of their basic training. Furthermore, modern computing allows us to analyze vast quantities of data intensively, and each package will provide statistical 'answers' to any questions the actuary might ask of it. However, in this particular field, it is often worth taking a step back, and ensuring that we recall and consider the warnings that come with each of the statistical procedures we might attempt to use. This will allow us to ensure that we guard ourselves against drawing inappropriate conclusions, which could come back to haunt us later.At the conclusion of the session, attendees will be able to avoid drawing inappropriate statistical conclusions, while doing their work. They will leave informed about the types of pitfalls that exist, and where those might come into play in actuarial work.

Description: The insurance industry hinges on accurate actuarial assumptions. This session explores how insurers can leverage AI/machine learning (ML) to unlock greater accuracy and granularity in assumption setting, while effectively managing risks, implementing strong governance frameworks, and meeting validation expectations from regulators.

Description: This report covers the latest emerging trends in U.S. population mortality. The SOA relied upon data furnished by the Centers for Disease Control and Prevention (CDC). Observations are based on the CDC’s recent release of 2021 mortality experience, along with prior mortality experience data from 1999 through 2020.

Description: Come check out opportunities and challenges associated with using predictive analytics for LTC. You'll learn how carriers are using predictive analytics to support a range of business functions, including: - Pricing - accelerate underwriting without jeopardizing the risk profile for your new products - Experience studies - enhance your understanding of block performance and capture more granular experience trends in your assumptions with less subjectivity - In-force management - facilitate insight-driven interactions that improve policyholder satisfaction and optimize operational costs Expect panelists to discuss the infrastructure required to deploy predictive analytics and the ways in which predictive analytics can help drive key outcomes, including, improved market competitiveness, enhanced understanding, improved efficiency, and better policyholder outcomes. They will also talk about key hurdles to adoption, and considerations for overcoming these hurdles.

Description: The global doubling of information content has been estimated to occur at a rate of every 12 hours, and conceivably faster. This ‘infowhelm’ is a double-edged sword for insurers. While maximization of the global knowledge base can fuel innovation, it can occur only in the context of reasonable cost for a higher benefit. The insurance industry – underwriters and actuaries in particular – are challenged to constantly examine new innovations that promise to raise confidence and protective value for insurability, within legal limits. This report aims to be the first of several to alert the SOA of promising innovations for the profession.

Description: Artificial intelligence (AI) models have become increasingly popular in various industries, including finance. Such models can help lenders assess the risk of loan applicants, identify potential defaults and make better decisions about granting credit. However, the complexity of many machine-learning and, particularly, deep-learning models makes it difficult to understand how they arrive at their decisions. This lack of interpretability can be a significant problem in the legal and ethical realms, as it hinders transparency and accountability. Join us to learn interpretability techniques that are essential to address the challenges of using complex AI models in credit risk modeling. These techniques aim to provide a better understanding of how an AI model arrived at a particular decision or prediction. Techniques include surrogate models, which aim to provide a simpler model that approximates the behavior of the original model, and decision trees, which show the decision-making process in a more transparent way. However, applying interpretability techniques to AI models can be a challenging task. For example, some techniques can reduce the performance of the model, which can be unacceptable in applications where high accuracy is critical. Additionally, some techniques may only be effective for certain types of models or datasets, which limits their applicability. Despite these challenges, interpretability techniques are becoming increasingly important in the field of credit risk modeling. Financial regulators have recognized the importance of interpretability, and are starting to require financial institutions to use models that can be explained and validated. In addition, interpretability techniques can be used to improve the performance of credit risk models. By understanding how a model makes decisions, it is possible to identify areas where the model can be improved or where additional data may be needed.

Description: This webcast will walk through several statistical methods for imputing race and ethnicity from U.S. datasets, highlighting their strengths and weaknesses, talking about how to use them and evaluating their accuracy, and telling you where you can get code packages as well as a free dataset to try the methods out.

Description: Hot topics and emerging trends in assumption development in US life insurance companies

Description: Dive into an overview of potential customer value propositions, trends in consumer acceptance and adoption, solving the data portability issue, potential impacts of emerging partner ships with OEMs and more within the auto insurance industry during our latest webcast. Experience interactive dialogue that will allow another dimension of thinking as industry experts provide insights and expertise. Throughout this webcast, you can expect developments in the personal lines automotive telematics-based insurance landscape that will develop a further understanding of the present and future of this industry.

Description: Have you ever struggled to make business decisions because your internal mortality studies lack credibility? Have you ever launched a new product or initiative and wanted to understand mortality risk before claims experience emerged? This session will discuss how predictive models can help actuaries enhance their traditional mortality studies with leading mortality leading indicators to be more nimble to market demands.