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: 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: Artificial Intelligence (AI) has become a key component of the insurance landscape and getting full value from an AI depends on much more than simply building the model. Please join us to hear the challenges and potential solutions around successfully deploying an AI-driven initiative within an organization. Hear from panelists as they describe the human impacts of model implementation, how to assess a model for effectiveness and (lack of) bias, and detail the journey of implementing AI-driven underwriting models at a large insurance company.

Description: Pandemics are a main driver of losses for both life and non-life (re-)insurance. Until COVID-19, risk assessment mostly relied on a few historic examples, most importantly the Spanish flu. The COVID-19 pandemic has shown that other pathogens (namely the coronaviruses) need to be taken into account. Also, modern societies and governments drastically respond to novel threats and implement measures such as social distancing, mask mandates, and lockdowns. Besides the properties of the pathogen itself, these reactions and the medical progression (namely the development of vaccines) are key influences on the course of a pandemic, and thus also the resulting outcomes. To account for the new insights, join us as we take a look at a stochastic, age-structured compartmental model that is calibrated on recent pandemics with several parameters sets to model different levels of reaction speed and stringency between groups of countries. Other important factors also must be defined in close collaboration with experts. Get ready to explore the development of a model and framework that can provide potential outcomes to be used as the basis to derive both summary and tail statistics to assess pandemic risk and help price complex deals.

Description: Web3, Crypto, and Generative AI are no longer just buzzwords as they reshape the tech landscape. Viewed by many as unconventional technologies, their potential to now revolutionize the insurance industry cannot be ignored. Join us for an enlightening session that delves into the transformative power of these technologies and their current applications within the insurance sector, as well as discover how forward-thinking insurance companies are embracing these tools to unlock innovation and challenge the insurance status quo.

Description: Attend if you are an organization interested in utilizing various kinds of client data to improve the whole insurance sales process, and to develop new applications. Interested parties may include distribution sales force, underwriting, claims, actuarial and finance departments within a typical insurance company, online distribution partners, health care providers and other consulting service providers. The research looks into various use cases, which are typically main issues in modeling in the insurance industry. The research also helps benchmark performance using different languages that may serve as a good reference when best to use a certain language to enhance the development cycle.

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: ALM and hedging can reap significant benefits from innovation in computation and operations research. Explore applications of static and dynamic optimization in two different ALM and hedging use cases. Insurers are increasingly moving offshore by establishing Bermuda captive reinsurers. In calculating the Bermuda Best Estimate Liability ('BEL'), reinsurers will need to segment assets to support liabilities in each treaty. A Linear Programming ('LP') problem can be formulated to tactically select assets in a way that minimizes the BEL and consequently the total asset requirement. Learn more about what US Variable Annuity Insurers face with the regulatory requirement to reflect 'future hedging strategy' in their statutory funding calculation. The traditional numerical method of finding the optimal hedging target and then allocating assets may encounter the curse of dimensionality with expanding product and economic environment complexity. A Reinforcement Learning algorithm may well fit this problem and provide insurers a solution for dynamic hedging.