Two disciplines familiar to econometricians, factor analysis of equities returns and machine learning, have grown up alongside each other. Used in tandem, these fields of study can build effective investment-management tools, according to City University of Hong Kong's Guanhao Feng (a graduate of Chicago Booth's PhD Program), Booth's Nicholas Polson, and Booth PhD candidate Jianeng Xu. The researchers set out to determine whether they could create a deep-learning model to automate the management of a portfolio built on buying stocks that are expected to rise and short selling those that are expected to fall, known as a long-short strategy. They created a machine-learning algorithm that built a long-short equity portfolio from the top and bottom 20 percent of a 3,000-stock universe. They ranked the equities using the five-factor model of Chicago Booth's Eugene F. Fama and Dartmouth's Kenneth R. French.

The retail banking services are one of the pillars of the modern economic growth. However, the evolution of the client's habits in modern societies and the recent European regulations promoting more competition mean the retail banks will encounter serious challenges for the next few years, endangering their activities. They now face an impossible compromise: maximizing the satisfaction of their hyper-connected clients while avoiding any risk of default and being regulatory compliant. Therefore, advanced and novel research concepts are a serious game-changer to gain a competitive advantage. In this context, we investigate in this thesis different concepts bridging the gap between persistent homology, neural networks, recommender engines and reinforcement learning with the aim of improving the quality of the retail banking services. Our contribution is threefold. First, we highlight how to overcome insufficient financial data by generating artificial data using generative models and persistent homology. Then, we present how to perform accurate financial recommendations in multi-dimensions. Finally, we underline a reinforcement learning model-free approach to determine the optimal policy of money management based on the aggregated financial transactions of the clients. Our experimental data sets, extracted from well-known institutions where the privacy and the confidentiality of the clients were not put at risk, support our contributions. In this work, we provide the motivations of our retail banking research project, describe the theory employed to improve the financial services quality and evaluate quantitatively and qualitatively our methodologies for each of the proposed research scenarios.

As consumers continue to drive the trajectory of fintech with demands for more convenience and a better experience, more providers are teaming with tech firms to develop algorithms to automatically manage portfolios. Is this a financial fad or the future of investing? How does this type of technology impact business finance and money management? Which is better – a human or a robot? The answer to these questions is the same: it depends on your preference.

Whether you blame robots, artificial intelligence, or automation more broadly, the proactive professional needs to plan for a successful career in a future without jobs . It is not enough to know how to navigate a company hierarchy because that company may completely restructure. It is not enough to understand how to find another job because it might make more sense to freelance or launch a business. It is not enough to develop deep expertise in any one industry because your industry might be disrupted beyond recognition (my two main industries, financial services and media, look nothing like they did when my career started 25 years ago!). In a future without jobs you have to be far more self-reliant and prepared to constantly drum up your own opportunities.

Meet Dave: an AI dressed up in a bearsuit that's just launched to save you from the evils of expensive overdraft fees. Hand Dave access to your checking account and the app's machine learning algorithms will get busy crunching your spending data so the bear can warn you about pending transactions -- like a monthly subscription for Netflix or your typical Saturday night Uber bill -- which might push you into the red and incur an expensive bank penalty. The US-only app predicts a user's "7 Day Low", aka the lowest it thinks your bank balance will drop in the next seven days, in order to encourage and support better money management. The ultimate aim being to help people avoid having to fall back on their overdraft as "an expensive form of credit", says co-founder Jason Wilk, describing it as a sort of "weather forecast" for money management. Dave also includes a payday loan facility -- so users who face the inevitability of having to dip into a negative balance can opt to borrow up to $250 ahead of their next paycheck to see them through.

Artificial intelligence (AI) is slowly becoming a bigger part of our lives, including our personal finances. Over the past few years its importance has grown behind the scenes, where AI is deployed in administration and even fraud detection. Dominic Baliszewski, director of consumer strategy at financial wellbeing company Momentum UK, says: "It is already used in a number of sectors, from banking and advisory to mortgages and pensions, and in many cases, it's led to a quicker, more streamlined customer experience, while easing employee workloads." RBS, for example, has rolled out a virtual assistant called Luvo for call centre staff: when a customer asks a question, the staff member can ask Luvo. In the past year, however, its presence has moved from the back office to the front line, and we have started to see AI serving customers.

Artificial Intelligence (AI) is making unending waves, both positive and negative. While some experts are going gaga over its productivity orientation and task precision, a few others are expressing concerns over the social impact due to the loss of jobs to robots. A single fact overrides these opinions--all sectors are without question exploring how they can leverage AI to better business, banking being no exception. For example, last year, RBS replaced some of its human employees with automated services. This is just the beginning, as machines slowly but steadily make their presence felt in the fintech sector.

Neither artificial intelligence nor fintech – or even the union of the two – is really anything new, despite the recent buzz regarding all of the above. With that being said, we are indeed approaching that point where the underlying technology begins to make a noticeable difference in people's lives. From there, it won't be long before we begin to wonder how we ever lived without artificially intelligent financial advisors implementing our own personal monetary policy. Well, did you know that digital camera technology has been around since the 1970s but simply wasn't good enough to get mainstream traction until decades later? Don't worry, Kodak didn't see it coming, either.