Description: This research utilized an interview-based approach that delves into the utilization of alternative data in the Australian life and health insurance markets.

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: Asset liability management is an important task for any insurer, where tools like Excel play an important role from an analytics perspective. Dashboard tools like Power Bi and Tableau can supercharge an organization’s ability to perform ALM with real-time precision and monitoring of risk/imbalances. The dashboard can provide insights into potential risks posed by asset allocation and concentration, allowing for informed decision-making to mitigate those risks. The presenters will walk the audience through an example of ALM use case, including how to build key performance indicators such as cash flow, asset allocation, and risk exposure. Additionally, we will demonstrate how these types of dashboards can be customized to fit the specific needs of your organization.

Description: Mortality Improvement is fundamental to setting your mortality assumptions. Creating your own Mortality Improvement assumption may seem like a Herculean task. The truth is creating a mortality improvement assumption with tools provided by the SOA, or the Continuous Mortality Investigation (CMI) make the process easy. Leaving you with the Herculean task of deciding if the assumption you created fits your product. Expect to learn about the CMI tool and the SOA's Mortality Improvement Model (MIM) tool, how you might get data to use in those tools, and what assumptions you need to make outside of data like what will the MI be in 20 or 30 years? Whether it is the general population, a segment of the general population, or certain causes of death, you will get very different answers.

Description: How might retirement income strategies be designed, and their suitability assessed, when retirees may differ substantially in their objectives, preferences and personal circumstances? This webcast provides a wide-ranging overview of the key elements involved in formulating retirement strategies, drawing on the “Primer on Retirement Income Strategy Design and Evaluation” that was recently released by the SOA. It will cover three types of income objective – income floor, income target and income optimization – and how they imply different retirement strategies along with a different set of metrics to evaluate outcomes. It will also touch on: key member attributes to account for; the nature of income risk; approaches to investing and drawing down on available assets to generate income; and how stochastic modelling can support the analysis. This webcast should deliver something of interest for all attendees, regardless of prior familiarity with strategies for retirement.

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 session will explore recent and historical advancements in computing relevant to actuarial analysis. We will provide a comprehensive overview of the current landscape, tracing the journey of how we arrived here and how actuaries are already harnessing these innovations. This session will primarily be focused on hardware and as software programming interfaces - the latter, in effect, helps accelerate code and ease of access to specialized hardware. We will also examine optimization opportunities involving the intersection of the two. Hardware discussed may include CPUs, GPUs, memory, and motherboard architecture, as well as bottlenecks such as bandwidth, power and heat management, and input/output management. The session will also touch on quantum computing and what problems relevant to actuarial analysis may benefit from quantum algorithms. Coming out of this session, the audience will gain valuable insight into the forefront of technological advancements that are transforming computing efficiency in actuarial valuation and analysis. They will also learn about the key hardware and software optimization techniques that can enhance existing actuarial processes."

Description: A deep dive into utilizing open-source tools for cash flow projection or valuation tasks when Excel and vendor software may not be the right fit. Speakers will cover common concerns about open-source tooling, a real-world example of Excel at its limits, a walkthrough of building a valuation engine in Python, performance comparisons of multiple languages and implementation approaches, and perspectives on using open-source tooling in the real world. Attendees will learn the pros and cons of open-source tools, when incorporating these tools might make sense, ways to get started, and gain exposure to advanced topics like profiling and optimization, concurrency, and adding a graphical user interface.

Description: Based on the work of the American Academy of Actuaries, this session will share insights on auditing an algorithm and provide an artificial intelligence governance checklist.

Description: Cluster modeling is emerging as a powerful technique to manage cloud costs and run time. It also enables quick responses to "what if" scenarios, and aids in attributing results between reporting cycles. In this webcast, we provide insight into cluster modeling, present case studies that focus on various key performance indicators and discuss the benefits and challenges to cluster modeling from an insurer’s perspective. We also discuss the considerations associated with moving from an on-prem based computing solution to a cloud-based approach.