Knowledge Base Articles
Part I - Introduction to Artificial Neural Networks
In this article series, we present a machine learning-based approach to solving a common problem in financial modelling where one is faced with the task of estimating the value of a function which requires a significant amount of computation to evaluate. More specifically, a function that corresponds to a so-called nested simulation aimed at, for example, estimating a capital requirement for a financial institution or the risk associated with a structured product for a retail investor.
Hierarchical Clustering: Prediction of Systematic Underperformance
As machine learning methods grow in use and popularity, we explore yet another dimension of wealth management that our experts consider fit for applying such frameworks. In this article, we deploy hierarchical clustering to find more consistent ways of predicting the relative future performance of funds.
Part I: An Introduction to Self-Normalizing Neural Networks
Machine learning applications have become more prominent in the financial industry in recent years. Our new article series is exploring the benefits and challenges of using self-normalising neural networks (SNNs) for calculating liquidity risk. The first piece of the series introduces the main concepts used in the investigative case study for the Swedish bond market.
Is Your Robo Advisor Fit for The Job?
Amidst the strategic decisions and the fears of a mysterious AI stealing the jobs of financial advisors, we believe one important detail remains overlooked. Do we properly understand the machines that are to automate an essential part of our value chain or that may become an alternative to our human operators?
Navigating the Modern Financial News Feed
Regardless of your level of professionalism as an investor, having access to relevant news with different perspectives on your assets is valuable. However, although useful for some, the high volume of the accessible news can be exhausting for non-professional investors. That is why we decided to discuss the way of delivering news in a concise and intuitive manner. Rather than having a large set of news titles summarizing your portfolio, it can be neatly compressed by using natural language processing. To explain, NLP is used to model human language and transform spoken words into written text, translate languages, answer questions and even generate synthetic text pieces. Using technological progress as a tool to increase the value for the client isn’t necessary insignificant as it indeed serves a purpose of delivering information in a way which engages the end user.
Humans, Robots or Cyborgs?
We think Cyborgs will win out in the battle for the hearts and minds of the next generation of wealth customers. Human advisors will be empowered, not replaced by AI fuelled solutions. Consumers will welcome a new generation of services which more holistically meet their demands from financial services. In the end, the robots may take their revenge but until then we should expect tech to continue to be a catalyst for positive change in finance in the years to come.
September 2019 News Update
During September, we distinguished three trends gaining prominence in the financial industry's innovation landscape. The first one explores the tendency of the WealthTech FinTechs moving towards B2B business models aimed at the DIY investment platform providers with established customer bases. The second trend concerns the definition of the appropriate customer base for B2C robo-advisors. While many automated financial advice providers still target millennials, the generations approach was widely criticised at the recent Robo Investing conference, with many delegates favouring adjusting the offerings to life situations experienced by the consumers regardless of their generation. The third theme of the month concerned the rising importance of explainability in automated decision-making, already reflected in Article 22 of the GDPR. Such a requirement may hinder the providers of digital services from using some of the machine learning methods without appropriate validation frameworks.