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.

This concentration features a multi-disciplinary curriculum that draws on insights from computer science, statistics, and business management. You will learn the statistical and computational methods for collecting, storing, and processing data; identifying patterns in large data sets; predicting and interpreting the findings; and making data-driven decisions. Developing additional skills will make you especially attractive to employers, and enable you to tap into more than one job market. Areas of study include actuarial science, marketing, quantitative risk analysis, law, and business. This concentration will prepare to use text mining, machine learning, and A.I. to detect patterns, predict outcomes, and derive insights related to regulation, compliance, litigation, and transactional law.

Before droves of people descend on a convention center for a trade show or conference, the hall must be carefully divided up to accommodate corporate show booths, walkways for attendees, spaces for administrators/security and much more. The process of defining the layout and marking it up for construction crews is often done with humans laboriously measuring and marking distances, but Lionel can do all of this for you. Once given a plan, it zooms along empty convention halls while precisely marking all of the dimensions for the schematics that you have in mind. Lionel, the floor-marking robot, was made specifically for the organizers of trade shows and conferences. Lionel isn't a robot that you'd typically think of when you imagine new applications of technology, but it fills a niche that there is strong demand for.

Data Scientists have consistently been around – it is only that nobody realized that the work that these individuals are doing is called data science. Data Science as a field has emerged distinctly over the recent few years yet individuals have been working in the data science field as analysts, mathematicians,learning and actuarial scientists, business analytic practitioners, digital analytic consultants, quality analysts and spatial data scientists. Individuals working under these jobs are well furnished with data scientist skills and they are most demanded in the business. Data science has quickly developed as a challenging, lucrative and highly rewarding career. While developed nations got comfortable with it part of the way through the last decade, data science has received consideration on a worldwide scale after the exponential development of e-commerce in developing economies, particularly India and China.

Just a month ago, data scientists at Google's DeepMind attempted to find out through the game of chess. The effort revealed just as much about AI's vulnerabilities as its possibilities. Google's AlphaZero artificial intelligence or "AI" system defeated the most advanced chess program in existence. True mastery of chess is something that few humans achieve in a lifetime; AlphaZero achieved it in four hours. This is the promise of AI – a combination of layered computer algorithms known as neural networks that attempt to process information like a human brain, only with exponentially greater efficiency.

This article illustrates how Geocoding uncovers the untapped value within generally overlooked insurance categories, such as Life and Annuity, and how it can help address modern-day business challenges remarked by Orszag. While Geocoding in Big Data is gaining prominence within Property and Casualty (P&C), we believe the real opportunity lies in the actuarial adoption of AI framework capable of processing consumable inputs that weren't visible in the erstwhile "Ease of Geocoding" era. Establishing this premise for Life and Annuity, we then pivot towards crafting a general purpose Geo-inclusive architecture that can help actuaries of all disciplines apply Machine Learning to solve new generation of business problems, such as, dwindling subscribers or risk-attributed challenges, such as, Adverse Selection. Nearly all of the data in the insurance business has a location attribute, e.g. However, many insurance companies have not fully utilized this component besides billing and mailing purposes.