Knowledge Base Articles
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.
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.
March 2019 News Update
We've selected the most relevant global news within the fields of automated financial advice, data intelligence and balance sheet risk this March. The rise of the robo-advisors concern the brokers, although many see this development as a helpful complement to the traditional wealth management business, stressing the regulatory burden as well as IT legacy systems' challenges. Meanwhile, Brexit leads to a spike in risk-aversion among the customers of Do-It-Yourself investment platforms. On the balance sheet risk side, the experts stress the importance of timely preparations to IFRS 17, FRTB and the transition from LIBOR. The machine learning tools are put to use in fraud detection in the banking industry context, and the Positive-Incentive ESG-based Loans gain prominence among the banks and corporations.