This paper introduces an innovative framework for the periodic evaluation of defined-contribution pension funds. The performance of the pension fund is evaluated not only at retirement, but also within the interim periods. In contrast to the traditional literature, we set the dynamic risk measure as the criterion and manage the tail risk of the pension fund dynamically. To effectively interact with the stochastic environment, a model-free reinforcement learning algorithm is proposed to search for optimal investment and insurance strategies. Using U.S. data, we calibrate pension members' mortality rates and enhance mortality projections through a Lee-Carter model. Our numerical results indicate that periodic evaluations lead to more risk-averse strategies, while mortality improvements encourage more risk-seeking behaviors.

Artificial intelligence seems to have touched every part of our lives. But can it help us manage our money? We put some common personal finance questions to the free version of ChatGPT, one of the most well-known AI chatbots, and asked for its help. Then we gave the answers to some – human – experts and asked them what they thought. We asked: I am 35 years old and want to ensure I have a comfortable retirement. I earn about 35,000 a year and have a workplace pension, in which I have saved 20,000.

Large Language Models (LLMs) have emerged as formidable instruments capable of comprehending and producing human-like text. This paper explores the potential of LLMs, to shape user perspectives and subsequently influence their decisions on particular tasks. This capability finds applications in diverse domains such as Investment, Credit cards and Insurance, wherein they assist users in selecting appropriate insurance policies, investment plans, Credit cards, Retail, as well as in Behavioral Change Support Systems (BCSS). We present a sophisticated multi-agent framework wherein a consortium of agents operate in collaborative manner. The primary agent engages directly with user agents through persuasive dialogue, while the auxiliary agents perform tasks such as information retrieval, response analysis, development of persuasion strategies, and validation of facts. Empirical evidence from our experiments demonstrates that this collaborative methodology significantly enhances the persuasive efficacy of the LLM. We continuously analyze the resistance of the user agent to persuasive efforts and counteract it by employing a combination of rule-based and LLM-based resistance-persuasion mapping techniques. We employ simulated personas and generate conversations in insurance, banking, and retail domains to evaluate the proficiency of large language models (LLMs) in recognizing, adjusting to, and influencing various personality types. Concurrently, we examine the resistance mechanisms employed by LLM simulated personas. Persuasion is quantified via measurable surveys before and after interaction, LLM-generated scores on conversation, and user decisions (purchase or non-purchase).

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To many consumers, buying life insurance can be painful. Despite insurance companies' substantial investments over the past several years in digitizing customer onboarding and policy binding, progress has been slow and incremental and, for many companies, has fallen short of expectations. Many companies have failed to meaningfully scale their efforts to modernize underwriting. The recent COVID-19 lockdowns and ongoing physical-distancing protocols reinforce the need to rethink underwriting. More than ever, insurance companies must address customer and agent frustration with the still lengthy, high-touch, manual process. With COVID-19, paramedic home visits to conduct medical exams have become highly undesirable--especially for a "pushed" product that is not immediately crucial to the customer. In this environment, risk assessment must shift toward more remote, data-driven models, while distribution must shift from in-person interactions to more online interactions.

If you ask a lay person on the street what life insurance is, and they'll tell you it is a policy you buy that pays a sum of money to your family when you die. Ask them to explain how life insurance works, and they will probably tell you it is a contract between an insuring company and a policy owner. Now ask them how artificial intelligence (AI) and aging research can help life insurance firms and policy buyers make decisions with conviction, and they'll scratch their heads and likely walk away from this conversation, or give very general answers. And while the customers are pretty much in the dark, some of the more innovative insurance companies are building substantial internal and external capabilities in both aging research and artificial intelligence. And there are hundreds of startups with more or less credible technologies that the life insurance companies are partnering with directly or through the open innovation hubs.

JERSEY CITY, N.J., Aug. 19, 2021 (GLOBE NEWSWIRE) -- Many consumers may soon be able to buy life insurance more quickly without undergoing inconvenient at-home medical tests. Management Research Services (MRS), a cutting-edge technology and data provider for the life insurance industry, is integrating Verisk's groundbreaking proprietary voice analytics into its telephone medical interviews to help flag potential tobacco users early and streamline the underwriting workflow. MRS is implementing Verisk's Tobacco Usage Propensity Model, which uses artificial intelligence and machine learning to analyze audio interviews. MRS will then apply the rules it has developed, based on Verisk's model output, to identify a small percentage of applicants who may require lab testing to verify their tobacco usage status, while enabling the vast majority of applicants to bypass lab testing. "Verisk will add tremendous value for our customers," said Tim Dineen, CEO of MRS.

How to get paid to ride in self-driving cars. Self-driving cars are gradually getting ready for prime time. To-date, the public roadway tryouts of self-driving cars have primarily consisted of having a human back-up driver at the wheel, serving to monitor the driving, and acting as a safety operator that can take over the driving controls if needed. This meant that any passengers in the self-driving car were still potentially reliant upon a human driver, albeit that much of the time the AI driving system was driving the vehicle. You've likely seen in the news that some of the self-driving car companies are now aiming to remove the back-up driver and let the AI do all the driving. As such, the occupants in the self-driving car will entirely be passengers, going along for the ride and not taking part in doing any of the driving.

Artificial intelligence is here, and here to stay. Whether you realize it or not, you feel its impact through the marketing appeals you receive online or in the mail; in the placement, packaging, and pricing of items in a supermarket; and in a myriad of other ways. AI is also embedded in life insurance operations, helping agents match products with prospective clients with a precision that was previously unimaginable. It's understandable, however, that some life agents might be apprehensive about the growth of AI in a field that prides itself on providing thoughtful, individualized solutions to the unique situation of each household. Things will certainly change as AI advances in life insurance, but agents that embrace AI and the changes it brings will actually find themselves to be more valuable to the carriers and customers who rely on them.

One of the most frequently-used phrases at business events these days is "the future of work." It's increasingly clear that artificial intelligence and other new technologies will bring substantial changes in work tasks and business processes. But while these changes are predicted for the future, they're already present in many organizations for many different jobs. The situation brings to mind the William Gibson comment, "The future is already here--it's just not evenly distributed." The job and incumbent described below is an example of this phenomenon.