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01May 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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02April 2019 News Update

We are delighted to present our analysis of the top April trends within the financial industry! This month we identified the growing need for risk expertise among the asset managers striving to provide truly sustainable financial advice. Moreover, we see that a different set of factors determines the competition among the digital offerings in asset management compared to the traditional financial advisory services. Additionally, we firmly believe that it is crucial for the financial institutions to measure and prepare for the impact of the looming -IBOR transition early on, and come up with an appropriate action plan to minimise the adverse PnL effects.

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Digital Advice

Financial advice is being digitalised and is increasingly provided on an automated basis. Download our summary of the latest developments within this exciting field.

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Risk
Beyond Modern Portfolio Theory: Expected Utility Optimisation

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.

Machine Learning
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.

Machine Learning
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.

LSMC
Part III: Asset and Liability Management Using LSMC - Allocation Optimisation

In the third and concluding article in the ALM using LMSC series, we focus on analyzing the optimal asset allocations in the context of changing asset classes as well as finding the optimal allocation by maximizing the risk-adjusted net asset value. The estimates based on the LSMC method are then compared to the estimates obtained from the full nested Monte Carlo method.

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.