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: 2021 Life Meeting - Session 7D: Emerging Trends in Lapse and Mortality for Life Products 9.1.21 Recording

Description: Focus on Accelerated Underwriting (AUW) in the life insurance industry, which is an expedited underwriting process that utilizes predictive models. Expect to address the challenges in designing the process, from modelling to performance monitoring. Additionally, you'll address risk management, particularly in mortality and proxy discrimination andhighlight the role an actuary can play in the AUW process, as well aswhat they should know to contribute effectively.

Description: Learn more about the predictive modeling that was applied to industry data for the lapse and mortality experience in the post-level term period. Novel approaches were taken to model the shock lapse, subsequent lapse, and mortality experience. Observe a demonstration of the Tableau tool, which was built to allow users to analyze data on their own.

Description: The session will highlight trends in recent industry mortality and lapse studies that have been conducted, including areas where individual companies typically have limited data. The sources of data feature SOA studies as well as other industry studies conducted by consultants and/or reinsurers. Gain an understanding of current drivers of mortality and lapse experience for life insurance evidenced in industry studies, and an understanding of available data to supplement experience in areas where you may have limited internal experience.

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: 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: Learn how modernization of data, models, and technology are influencing workflows. This webcast will address how workflows are changing in response to new regulatory requirements and the impacts the changes have on modeling and end-to-end future state operating models. Industry leading actuaries will discuss: How team collaboration can increase quality and speed of reporting to internal and external stakeholders How to strengthen controls and compliance frameworks across finance and actuarial departments How to modernize actuarial input/output for strategic analysis and insights How to attract and retain top talent by improving opportunities for actuaries There will also be dedicated time to ask questions of those who have made the transition.

Description: Get an in-depth explanation on how data scientists and actuaries can work together to battle the issues arising from big data. Join our focus on the insurance industry as a whole and look at data scientists' and actuaries' roles from the views of different areas of the industry. We will also focus on how data science can be a valuable tool for actuaries. After the session, you will be able to understand: - How developments in data science are shaping Insurance Industry practices - How data scientists can help actuaries - How data science can be used to improve analysis of industry experience

Description: Volatile interest rates, repositioned investment portfolios, and stress in the banking sector are driving insurers to manage market risk and counterparty credit risk. Our experts in investments, derivatives, and hedge accounting will discuss this new market environment and its impact on hedging. The conversation will include practical case studies, including hedging future bond purchases, hedging floating-rate CLOs, and counterparty credit risk management.