Description: Walk through real-life examples of how artificial intelligence (AI) can be used to drive insights and aide actuaries in improving the management of their company's inforce book of business. Hear a panel of experts go step-by-step through case studies, showing how practitioners leveraged predictive analytics for solutions to tap further into their data and draw improved insights.Leave the session with knowledge of how to partner advanced machine learning techniques with the actuarial skills and insights to better understand policyholders' behavior to more efficiently enable accelerating revenue and further profit realization.

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.

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: Amid macroeconomic uncertainty, proactive lapse management is becoming a growing strategic focus for Life and Annuity carriers. Interest rates have been increasing rapidly during the last eighteen months and insurers, especially annuity writers, have started to see higher lapses and withdrawals. After a prolonged low interest period, many insurers will have to (re)build capabilities to enable proactive lapse management.

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: Emerging Applications of ESG Methods offers ground-up insights on cutting edge applications of economic scenario generation. The course will focus on the incorporation of climate change risk in scenario modeling and investment applications of ESGs. The following topics will be covered: Approaches to climate risk modeling. Climate risk is very different than classical financial economic risk. Radical uncertainty is amenable to scenario analysis and lack of data. Building special deterministic scenarios that go beyond NGFS requirements as well as bespoke scenarios. Holistic Approach to Climate Risk Modeling. Overview of different Climate Risk Modeling Applications. Use of ESG scenarios for Strategic Asset Allocation (SAA). Liquidity risk management.

Description: Anti-obesity medications have been met with reluctance from payers and employee benefits consultants who often lack analogs or empirical data to estimate the cost impact of new therapies coverage. Health plans considering coverage of anti-obesity medications will need to assess and manage the potential costs of covering these new therapies in context of their overall obesity management strategy, including coverage of nutrition counseling, weight management, and bariatric surgery.

Description: This session will bring together public health care leaders in COVID-10 vaccine delivery from both the U.S. and the UK to discuss and compare operational implications of the COVID-19 vaccine rollout in their countries, and how those programs ultimately affected hospitalization utilization and mortality rates for members of their communities.

Description: Become informed about the approach and adverse effects of the regulation of third parties and advance consent by the proposed NAIC Consumer Privacy Protection Model Law #674. You will hear an explanation of the regulation of third parties through their contracts with insurers. Learn specifics on the negative effect on companies contracting with third parties. Find out more about the requirement for advance consent for use of personal data for actuarial and marketing studies, and hear two potential solutions .At the conclusion of this session, you should be able to explain concerns about contracting with third parties for external data under the Model Law and learn potential solutions to using personal data for actuarial and marketing studies.

Description: The United Kingdom makes public an impressive body of data about its population, its health system (the NHS), and the care which the NHS provides to the nation. This session will take the audience on a tour of many of these sets - with a focus on uncovering healthcare disparities. For context, a brief description of the NHS in England, population characteristics of England, and how social determinants of health (SDOH) are quantified will be given. With these as a background, we will explore data on chronic disease prevalence and incidence as well as physician performance against targets. We will present examples of health disparities from this data for certain conditions. Datasets of UK prescribing will be shown for further detail. The closing portion of this talk will present similar public domain US data - and how the UK body of information can assist in understanding health disparities in the US.