Knowledge Base Articles
Case Study: Machine Learning Applications in Fair Value Measurement
The rapid evolution of computational technologies has enabled businesses to leverage machine learning methods to tackle challenging, labour-intensive tasks involving various degrees of judgement and decision making. Financial markets are no exception. In this article we present the case of using our AI-driven solution to tackle a common challenge in finance – the fair value measurement of illiquid financial instruments.
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 II: Self-Normalizing Neural Networks - Bond Liquidity Classification
In the second part of the article series, we outline a framework utilising both the Self-Normalizing Neural Networks (SNNs) and the logistic regression for bond liquidity classification. This framework is subsequently applied to the Swedish bond market in an investigative case study.
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.
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. Mercifully, no lobotomisation or bionic implants will be needed. 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.
August 2019 News Update
In August, we distinguished three themes gaining momentum in the financial industry's innovation landscape. The first one concerns the positioning of the robo-advice on the Gartner hype cycle, from the peak of inflated expectations to the trough of disillusionment. The second trend explores the meaning of sustainability in the provision of financial advice. Finally, looking into the potential flaws of the machine learning-driven models sums up the third theme of the August press on the financial industry's innovation.
The "Kryptonite" for Machine Vision in Finance
Currently, machine learning algorithms are steadily gaining prominence in multiple different sectors of the financial industry. The use cases include chatbots assisting the customers with small inquiries, valuation of financial instruments, option hedging, marketing and many other tasks which were traditionally performed by human employees. Although it sounds exciting that artificial intelligence takes over huge volumes of challenging human work, it would be irresponsible not to wonder how credible and accurate these systems are. Therefore, in this blog entry, we explore the flaws and opportunities of machine learning algorithms using machine vision solutions as an example.