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Learn More01Eight powerful tips for managing complex implementation projects
Managing implementation projects becomes more difficult as their technical complexity progresses. The challenges faced by project managers may include the lack of technical expertise required in quality assessment and staffing activities, failure to address the communicative issues or letting go of the underperforming project staff. This article suggests eight powerful tips for leading convoluted IT implementation projects, which would protect the workflow from the common pitfalls and help you reach your goals.
Read More02May 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.
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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.
The modern wealth management industry still relies on the 50-year-old approaches to portfolio management, widely popularized by Markowitz's Modern Portfolio Theory (1952). Despite heavy criticism within the academic circles, the alternative methods remain undeservingly overlooked in practice. In the context of the modern leap for hyper-customization, we look into one of the alternatives to Modern Portfolio Theory in greater detail - the Utility-based approach.
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.
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.
What we do
We improve decision making under uncertainty
Our work empowers millions of people to make, or benefit from, informed financial decisions under uncertainty. Asset liability management, capital requirements and automated financial advice - everything we do helps support our vision that everyone should have access to world class risk management tools.