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01End of LIBOR Event Summary

We are delighted to present a free summary of a recent event hosted by Kidbrooke Advisory in a partnership with FinCAD and Erik Vynckier dedicated to analysing the consequences of moving away from -IBOR. The discussion involved exploring differences between the -IBOR and the alternative reference rates, technical aspects of the transition, fallback contracts' intricacies and their potential impact on the trading positions, as well as the situation at the Swedish market.

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02Summary: Swedish FSA releases consumer protection report

The Swedish Financial Supervisory Authority (FI) releases a yearly consumer protection report featuring customer security highlights in the Swedish financial industry. As in the previous years, the main risks related to customer security relate to mortgages and loans. The interest payments can potentially threaten the economies of the individuals in case of the economic downturn or the increase in interest rates. Another threat that is amplifying in the context of the digitalising society relates to customer data protection. The FI calls for more advanced security systems that would protect the consumer at all stages of a payment transaction. The improvement of these solutions is especially relevant in the context of increased instances of financial fraud. Finally, FI announces that the protection of the wealth management customers and the enforcement of the MiFID II requirements regarding third-party inducements becomes a vital area of the regulators' future work.

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Free Resource:

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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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.

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.

Part II: Asset and Liability Management Using LSMC - Accuracy and Performance

The second part of the series exploring the use of Least Squares Monte Carlo in Asset and Liability Management is focused on evaluation of accuracy and performance of this method in comparison to full nested Monte Carlo simulation benchmarks.

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.