Knowledge Base Articles
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.
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.
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.