Description: As actuaries, we’re trained to see patterns in numbers and to design solutions that mitigate risk. But I can tell you from experience that while quantitative analysis and modeling are critical actuarial tools, looking at the numbers alone can lead to insurance products that don’t succeed in the real world the way the math says they “should.” With new digital technologies, we have more data at our fingertips than ever before, but I believe it’s equally as important to bring qualitative research methodologies into your product development process if you want to design solutions that meet real needs in the market. In this article, Amanda Turcotte shares what happens when qualitative research is neglected or ignored, what happens when you incorporate it into the product design process, and how to implement qualitative research practices in your organization.

Description: As actuaries in health care, we spend most of our time focused on reviewing claims, enrollment and clinical data. That information is captured while patients are in health care facilities. Yet most people spend only a small fraction of their time in these facilities. This article explores what types of data are available to actuaries the provide a more holistic view of the member so that a members physical and emotional needs, the social determinants of health can be taken into consideration along with their clinical needs. Two uses cases are presented so actuaries can see how these types of analyses can be leveraged to improve clinical, operational, and financial performance of risk bearing health entities. Understanding the customer (member) not just the patient enables actuaries to provide new insights and values to our stakeholders across a wide range of business and risk applications.

Description: Lifetime pension pools convert a lump sum into lifelong income, periodically adjusting benefit payouts based on mortality and investment experience. Volatility-targeting strategies can enhance benefit stability by dynamically adjusting the investment portfolio’s exposure to risky assets in the context of these pools, demonstrating superior performance compared to traditional static asset allocation methods.

Description: When designing and pricing a healthcare product, it is not only important to know how to model frequency and severity, but it is essential to understand human behavior. Of course, the issue of anti-selection in medical insurance is well known as individuals know more about their health and prior expenditures than an insurer does. This type of behavior is certainly rational, and to be expected.

Description: Our previous article, “Introduction of Cashflow Matching Strategic Asset Allocation (SAA) Framework,” introduced the concept and included a real-world case study that developed a liability-driven investment strategy for a single line of business (LOB) for a life insurer. This article expands the framework and the case study from a single LOB to simultaneously optimize asset allocation across multiple LOBs. It discusses how this multi-line SAA framework can enable an insurance company to efficiently tag its asset holdings to each LOB, identify opportunities in the public and private asset markets to enhance risk-adjusted returns, and develop a deeper understanding of the trade-off between the degree of cashflow matching and risk-adjusted returns across LOBs.

Description: This article highlights the fact that new requirements for disclosure of average liability duration and interest rates could lead to diversity in practice. It also recognizes that elements of the new disclosure requirements will enable financial statement users to perform their own crude estimates of these averages in ways that could help them to better understand the liabilities and to compare companies using consistent methods. Finally, the article describes how companies might also find value in such crude estimates.

Description: How does mortality experience impact earnings under the new GAAP financial framework? This article discusses how to isolate key components from financial models related to decrements, as well as how to handle the changes in the financial reporting landscape including: the aggregation of insurance policies into cohorts, dynamic reserving, and the limitations imposed by caps and floors. Looking at the components of earnings, such as the present value of benefits, expenses, and premiums, and how these elements interact is critical to form a comprehensive earnings story.

Description: This article provides an overview of activities that should be considered by an actuary when validating a predictive model. It describes several components to consider when validating a predictive model and provides an examination of various methodologies that can be implemented in the validation process.