Description: Hop into a panel discussion between InsurTech Advisory Council and a startup, followed by a demo of life event prediction and analyses with multiple applications across financial services. You will also learn about: the SOA office hours program and member participation, the startup ecosystem filled with incubators, company contests, opportunities and outcomes, how to build a data set providing additional insights for customization and enhanced user experience, and - actuaries' engagement with startup strategic advisory boards.

Description: This session includes a new idea based on dynamic treatment regime work (OWL) and integrating cost into this work. This will enable identifying the best treatment for a patient based on both outcomes and cost. This is novel for actuaries because in previous analysis we have used cost only in the after-analysis, whereas here we use it in the actual analysis and treatment identification. Moreover, this analysis provides a way to consider multiple treatments for a patient (including the no treatment option) and choosing among those multiple treatments, for both outcomes and cost. This session will share the background theory and set up a case study. The case study will parallel what is in the analysis in the book by Ian Duncan 'Predicting Hospital Readmission with Case Management Recommendation.' This analysis will then go further to compare a situation where there are multiple potential case managers. Also, this will potentially explore care pathways and choosing the best care pathway for a patient based on health outcomes and cost combined.

Description: Artificial intelligence brings immense opportunities for productivity gains—and valid concerns. It can both support innovations in insurance products and in the customer experience as well as enhance our profession’s work. Actuaries share a responsibility for caution and professional judgment to use it without jeopardizing the fair and equitable treatment of individual consumers and the principals we serve.AI creates significant challenges for consumers: opacity, reliable accuracy, privacy, and verification of personal data. The Academy and its members have been working to educate and inform stakeholders, insurance commissioners, and regulators toward improving the transparency of algorithms that underlie the use of AI by insurers, to explain how the algorithms can be used to achieve equitable treatment, and to identify the issues and trade-offs related to ensuring consumer protections. We must amplify the actuarial perspective on how adequate disclosures, documentation, and governance of AI can help protect users. This session will examine how we can govern and support the evolution of better practices. We will discuss how AI is addressed via standards such as ASOP No. 56, Modeling, and other guardrails that are being developed to best utilize AI’s real-world potential and fulfill our professional responsibilities.

Description: The expanded use of predictive models, algorithms and external consumer data has drawn the attention of the life insurance industry to the matter of fairness and bias in underwriting, including proposed regulation in states like Colorado and New York and a draft NAIC Model Bulletin. Insurers will benefit taking a proactive approach to verifying that their practices, both current and future, do not result in unfair discrimination. This session will help attendees in understanding potential requirements and reporting insurers must address, techniques and tools for evaluating bias, and challenges in doing so. At the conclusion of the session, attendees will be able to: o Understand proposed regulations and practices regarding fairness and bias o Identify tools and techniques for evaluating bias in their processes o Discuss the challenges the industry will face in addressing fairness in underwriting

Description: "A crucial importance in forecasting is to be able to distinguish cases when temporary slowdowns in health care utilization are followed by a “rebound” period of higher utilization. Come explore an analysis of a new mathematical model describing the return-of-care process. By means of simulation and case studies using the R programming language, the relationship between the visibility of these effects and the endogenous variance of the utilization time series is explored. After attending this session, you will be able to: Describe the parameters and mathematical form of the model Apply the model to real-world utilization data"

Description: This session will discuss essential theories and practical applications of deep learning for actuaries. Participants will gain insights on leveraging these cutting-edge techniques to enhance actuarial models and drive predictive accuracy.

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: 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: Expect to learn how recent advances in AI and Generative text technology can help unlock data from medical records to solve many key underwriting and actuarial challenges. Specific topics include the following: - What is NLP/AI/machine learning and how is it currently being used in underwriting? - What technological advancements have Insure Techs made in the use of AI and GPT, and how are carriers/reinsurers utilizing these enhancements? - How can you avoid some of the challenges with GPT such as Hallucinations and 'black box' outputs? - How can extraction of data from medical records assist actuaries with various pricing and analysis challenges? - What legal/compliance challenges should users be aware of and how to manage those risks?

Description: Regression to the mean (RTTM) is inflating your health care intervention evaluations if you are not carefully managing it's omnipresent and significant impact RTTM is the statistical phenomenon which arises when comparing sequential data points when an initial observation is extreme or an outlier, and states that with no other intervention, future observations will be closer to the average. Members who had high costs last year will, on average, have lower costs this year, even in the absence of any intervention or clinical program, simply by 'regressing' to the mean. This means that if your clinical programs are simply targeting members who had adverse medical experience last year, you’re targeting the wrong people. And if your savings methodologies are simply last year’s costs minus this year’s costs, the savings are inflated. By attending this session, you will be able to identify situations where RTTM will be a significant factor, how to eliminate it's impact when performing retrospective evaluations, how to identify prospects for clinical intervention with RTTM in mind, and more.