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.

For the assessment of the financial soundness of a pension fund, it is necessary to take into account mortality forecasting so that longevity risk is consistently incorporated into future cash flows. In this article, we employ machine learning models applied to actuarial science ({\it actuarial learning}) to make mortality predictions for a relevant sample of pension funds' participants. Actuarial learning represents an emerging field that involves the application of machine learning (ML) and artificial intelligence (AI) techniques in actuarial science. This encompasses the use of algorithms and computational models to analyze large sets of actuarial data, such as regression trees, random forest, boosting, XGBoost, CatBoost, and neural networks (eg. FNN, LSTM, and MHA). Our results indicate that some ML/AI algorithms present competitive out-of-sample performance when compared to the classical Lee-Carter model. This may indicate interesting alternatives for consistent liability evaluation and effective pension fund risk management.

THE INSTITUTE Artificial intelligence is transforming the financial services industry. The technology is being used to determine creditworthiness, identify money laundering, and detect fraud. AI also is helping to personalize services and recommend new offerings by developing a better understanding of customers. Chatbots and other AI assistants have made it easier for clients to get answers to their questions, 24/7. Although confidence in financial institutions is high, according to the Banking Exchange, that's not the case with AI.

Nearly two-thirds of large enterprises are running mainframe-based apps dating back two decades, according to the recent Mainframe Modernization Business Barometer Report from Advanced. Over a quarter of businesses run production applications that are as much as 30 years old–some even go back to the 1960s. For example, in a conversation with a friend at a U.S. public pension fund with nearly $100 billion under management, he told me they decided to take action and migrate most of their remaining mainframe applications from COBOL to Java. Well, for one thing, it was hard to find developers who knew the language, or wanted to, with COBOL ranking #1 as the "most dreaded" programming language in Stack Overflow's annual survey. But there were more reasons for embracing Java, starting with a desire to make better use of DevOps to improve software delivery. When migrating from COBOL (or any language) to Java (or any language), it's smart to start with testing requirements.

Nearly two-thirds of large enterprises are running mainframe-based apps dating back two decades, according to the recent Mainframe Modernization Business Barometer Report from Advanced. Over a quarter of businesses run production applications that are as much as 30 years old–some even go back to the 1960s. In other words, as much as we like to tout the cool, new tech, many enterprises are mired in not-so-cool, old tech. For example, in a conversation with a friend at a U.S. public pension fund with nearly $100 billion under management, he told me they decided to take action and migrate most of their remaining mainframe applications from COBOL to Java. Well, for one thing, it was hard to find developers who knew the language, or wanted to, with COBOL ranking #1 as the "most dreaded" programming language in Stack Overflow's annual survey.

In July, the California Department of Fair Employment and Housing sued video-game giant Activision Blizzard, alleging, more or less, that the company has a workplace environment from hell. Regulators said a two-year investigation into the company revealed an alcohol-drenched "frat boy" culture that included inappropriate conduct by executives, men openly joking about rape, and a general "breeding ground for harassment and discrimination against women." The company called the lawsuit "truly meritless and irresponsible" (though it seemed to have some trouble figuring out how to respond), and more than 2,000 current and former employees responded by putting their names on an open letter that said, "We no longer trust that our leaders will place employee safety above their own interests." In early August, employees shared their salaries en masse, Bloomberg reported, to pressure the company into confronting pay inequities. One executive, Blizzard head J. Allen Brack, resigned.

Communicating effectively with customers is a challenge for many marketers, but especially in a context that is both pivotal to individual long-term financial well-being and difficult to understand: pensions. Around the world, participants are reluctant to consider their pension in advance, it leads to a lack of preparation of their pension retirement [1], [2]. In order to engage participants to obtain information on their expected pension benefits, personalizing the pension providers' email communication is a first and crucial step. We describe a machine learning approach to model email newsletters to fit participants' interests. The data for the modeling and analysis is collected from newsletters sent by a large Dutch pension provider of the Netherlands and is divided into two parts. The first part comprises 2,228,000 customers whereas the second part comprises the data of a pilot study, which took place in July 2018 with 465,711 participants. In both cases, our algorithm extracts features from continuous and categorical data using random forests, and then calculates node embeddings of the decision boundaries of the random forest. We illustrate the algorithm's effectiveness for the classification task, and how it can be used to perform data mining tasks. In order to confirm that the result is valid for more than one data set, we also illustrate the properties of our algorithm in benchmark data sets concerning churning. In the data sets considered, the proposed modeling demonstrates competitive performance with respect to other state of the art approaches based on random forests, achieving the best Area Under the Curve (AUC) in the pension data set (0.948). For the descriptive part, the algorithm can identify customer segmentations that can be used by marketing departments to better target their communication towards their customers.

In the next several years, FinTech, AI, and data-driven technologies will converge into a single ... [ ] advanced technology Over the past 100 years the financial industry has largely excluded people in retirement. Even today tech entrepreneurs are ignoring financial inclusion for people over 60, who make up the wealthiest part of the financial system, and instead, are developing new financial products designed for younger people. The most valuable and capable client demographic in terms of purchasing power are the citizens of the 7th Continent which is made up of 1 billion people over 60. The global spending power of this demographic is expected to be $15 trillion this year. Who will serve this market?

In the next several years, FinTech, AI, and data-driven technologies will converge into a single ... [ ] advanced technology Over the past 100 years the financial industry has largely excluded people in retirement. Even today tech entrepreneurs are ignoring financial inclusion for people over 60, who make up the wealthiest part of the financial system, and instead, are developing new financial products designed for younger people. The most valuable and capable client demographic in terms of purchasing power are the citizens of the 7th Continent which is made up of 1 billion people over 60. The global spending power of this demographic is expected to be $15 trillion this year. Who will serve this market?

There are over 1 billion people currently in retirement. New types of financial institutions are evolving to satisfy the needs of the aging population. Investment banks, pension funds, and insurance companies are developing new business models, and are using AI to improve the quality of the analytics used to formulate them. In the near future, the synergy between innovative AI and wealth management will lead to the creation of a new financial institutions optimized for the aging population and age-friendly Longevity banks will make banking easier and safer for seniors. Over 150 financial companies are already developing innovative WealthTech and AgeTech products and services and AI is central to the process.