Description: This paper suggests that individual decisions to retire are based on personal resource allocations, institutional influences, and social standards. The paper then attempts to create a decision-making framework in which to accommodate this range of influences. From Retirement Needs Framework, M-RS00-1.

Description: The abstract for a paper that focuses on default distributions and why they are important.

Description: For a long time the primary focus of private LTC insurance companies has been to educate the public about the need for private LTC insurance and to make the product more acceptable to a wider population. To this end there have been numerous changes to the product design to make this product more acceptable to general population. However, one of the things that may have been overlooked in those product design changes is the need for alignment of policyholder and insurance company’s interests, which is to make coverage more affordable and to reduce adverse selection. Incentivizing good behavior from policyholders can not only make LTC insurance policies less expensive but also help policyholders make better health choices, which can help them stay healthy for a longer time. These incentives may also reduce some of the inherent risks in a long term care policy and may help in increasing the sales of private LTC insurance.

Description: Retirees and their advisors should construct diversified portfolios of retirement income, allocating among various retirement income classes that include Social Security, annuities, and systematic withdrawals from invested assets. This essay discusses the pros and cons of various retirement income generators and the issues with constructing retirement income portfolios.

Description: Traditional predictive tools are not built to deal with the variability the world currently faces. The COVID-19 pandemic amounts to a prolonged black swan event, for which one must look past conventional assessments to arrive at any level of confident predictions. Behavioral analysis factors for biases and heuristics designed specifically for uncertain evolutionary contexts, and which are the primary drivers of market participants’ decisions at present. Through understanding the most likely behaviors of individuals, it becomes possible to arrive at an aggregate understanding of market outcomes.

Description: While the major market crash subsequent to the widespread contagion of the COVID-19 virus was readily predictable, the rapid recovery and ongoing growth subsequent to it was not. Market dynamics around the recovery resist characterization by most traditional analytical approaches. In examining the circumstances around the emergence of a large retail class of investors, one sees that ingrained human tendencies, particularly optimism bias, have fueled much investor behavior responsible for the recovery. Understanding this makes it possible to predict potential future crashes and develop behavioral interventions for subsequent market recoveries.

Description: Review of the address given by Richard H. Thaler, Robert P. Gwinn Professor of Behavioral Science and Economics at the University of Chicago at the Investment Section at the Society of Actuaries meeting in New York on October 20.

Description: This report presents the results of six focus groups with retirees who have at least $100,000 of investable assets and must depend on that money to maintain their lifestyles because they do not have sufficient amounts of annuitized income from Social Security and defined benefit plans to meet all of their expenses. The purpose of the focus groups was to gain an understanding of how current retirees approach key financial decisions about managing their money in retirement. The benefits of this qualitative study are multidisciplinary. It can help: 1. actuaries design better retirement financial systems, 2. the financial services industry understand areas of misperception and causes of poor management, and 3. provide policy-makers an understanding of how to drive public policy goals.

Description: Summary of the July, 2013 newsletter with an executive overview of each article.

Description: Fraud is often a dynamic and challenging problem in credit card lending business. Supervised modeling/segmentation techniques are commonly used in fraud detection to distinguish risky transactions from non-risky transactions. However, these techniques frequently rely on identifying risky behavior at a global level. In this paper, along with the classical approach, a new technique has been studied to improve the behavioral fraud detection capability. The application of this proposed technique enables us to identify risky behavior at the account level.