Description: The management of the investments of an insurance enterprise has undergone a considerable evolution since the pioneering work of Redington in the mid 20th century. Best practices have evolved to incorporate ever more complex insurance liabilities, expanding investment asset classes and global market opportunities. Strategic asset allocation (SAA), liability driven investing, and other robust optimization approaches form the core of the professional management of insurance company investments.

Description: Institutional investors such as pension plans and insurance companies have enjoyed strong equity returns for a length of time with few historical precedents. Nearly a decade after the end of the financial crisis, strategies to provide downside protection remain expensive and the performance of traditional risk-managed funds has disappointed investors.

Description: Maintaining solid financial performance is a KPI for senior management. With the advancement of technology, the management can leverage new techniques to improve their decision making and focus on higher value activities in a timely manner. This presentation sets out the use of 1. correlation analysis to draw relationship between financial metrics and 2. smart analytics techniques, including text mining and analytics, to identify trends and drivers that impact the valuation of a company. We will also assess how the results of a comprehensive financial analysis could influence risk management considerations, including business steering and risk strategy.

Description: Since the international financial crisis, accounting standards have become an important part of financial supervision rules, which objectively requires us to study the reform and development of accounting standards from the perspective of financial stability. Since the release of IFRS17 in 2017, the research and discussion of IFRS17 in the insurance industry has focused on technical details and implementation, while the economic impact and pros and cons analysis of IFRS17 are few. As a result, the industry's understanding of IFRS17 is not comprehensive enough, and there are still many disputes, and no industry consensus has been formed. In the future, the insurance industry should strengthen the research and analysis of IFRS17 from the perspective of financial stability, deepen the understanding and cognition of IFRS17, and improve the ability to control and use IFRS17.

Description: After International Accounting Standards Board (“IASB”) issued International Financial Reporting Standard No. 17 – Insurance Contracts (“IFRS 17”), IASB received much feedback and suggestions for improvement. As a result, in early 2019, IASB made limited changes to IFRS 17. The presentation will briefly discuss the main contents and effects of those changes.

Description: The advent of machine learning algorithms has taken predictive analytics and risk scoring to a new level. From gradient boosting machines to k-nearest neighbors regression, the use of these techniques has gained significant traction in healthcare - including health systems like the Cleveland Clinic. This session will discuss the use of predictive analytics to go beyond just assessing the risk of a patient, and instead focus on prescribing a specific intervention of care. In practice, the recommendation of a care plan goes beyond just attributing a score to a patient. It provides physicians and care coordinators the most probabilistic options - e.g. referral to social worker, depression screening, weight-loss program - for positively impacting a patient's future healthcare outcome.

Description: In Medicaid rate development, actuaries have not historically shared models that developed the margin (i.e., underwriting gain) assumption. This is unfortunate, given the margin assumption is one of the most critical components of the rate development in ensuring financial solvency of the Medicaid managed care organizations (MCOs). In this session, we will discuss the primary components of the this assumption and will share working models that help actuaries quantify these components—including the cost of capital and risk (or contingency) margin. We expect a lively discussion as we “open the black box” and discuss these actuarial assumptions in the public for the first time.

Description: The session will talk about the basic knowledge of GLM model that is relevant for insurance professionals. It will consist of explaining the concept, issues, tools and applications especially from insurance industry perspective. The session will also talk about how actuaries can benefit from predictive analytics. Advanced business analytics provides employers with insightful decision making and affords the opportunity to assess a marketplace from a totally new perspective. In this session, I will share some modelling techniques used in general insurance company for reference.

Description: In this session, we will discuss the advantages of using stochastic simulation of decrements in actuarial projections. We will also discuss practical approaches for parameterizing assumption probability distributions for simulation using predictive models as well as challenges the practitioner may face in implementation.

Description: Data science and predictive analytics are rapidly growing fields and the tools are evolving at a formidable pace. The actuary who wants to be most effective will most likely have to learn at least the basics of more than one programming language, such as R, Python, or Julia, depending on the problem at hand. The Jupyter Notebook App allows you to create interactive notebook documents that are relatively language agnostic, so you only need to learn a single integrated development environment. More importantly, Jupyter Notebooks allows you to easily combine input (code), output (including visualizations), and documentation in a single, shareable, document. Learn how to make better use of your time with these very useful notebooks. This demonstration-based talk will also cover JupyterLab, the next generation of notebooks.