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.
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.
June 2019 News Update
This June, we analysed three topics that gain prominence in the context of rapidly digitalising financial industry. As widely known, the machine learning solutions become more widespread in addressing the operational and compliance issues within banks and insurers. However, we highlight that the interpretability of such models is as relevant as their performance. Moreover, in the context of maturing robo-advisory offerings, we see that the common strategy is to focus on the space of clients which are underserved by traditional financial advisors. Finally, we look into the process of building trust by the emerging challenger banks, which may threaten the positions of the centuries-old incumbents in the industry of tomorrow.
May 2019 News Update
The introduction of automated financial advice services did not go successfully for some of the large and reputable wealth managers. As some of the industry players cease their robo advice offerings, we explore the reasons why big banks struggled to tap into the customers' demands. Meanwhile, machine learning solutions continue to expand to various business functions throughout the increasingly digitalising economies. However, little attention is paid towards the quality and the transparency of the decision-making powered by these "black boxes". Finally, in the world of accelerating personalisation standards, it is crucial to expand the innovation efforts beyond the interfaces and use the technological capabilities to improve the actual offerings.