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Knowledge Base Articles

Part II - Artificial Neural Networks as a Substitute to LSMC and Nested Simulations

In this article series we present a machine learning-based approach to solving a common problem in financial modelling where one is faced with the task of estimating the value of a function which requires a significant amount of computation to evaluate. More specifically a function that corresponds to a so called nested simulation aimed at for example estimating a capital requirement for a financial institution or the risk associated with a structured product for a retail investor.

Part I - Introduction to Artificial Neural Networks

In this article series, we present a machine learning-based approach to solving a common problem in financial modelling where one is faced with the task of estimating the value of a function which requires a significant amount of computation to evaluate. More specifically, a function that corresponds to a so-called nested simulation aimed at, for example, estimating a capital requirement for a financial institution or the risk associated with a structured product for a retail investor.

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.

Blog Articles

Kidbrooke qualifies as an AIFinTech100 company

Kidbrooke is named as one of the world’s most innovative solution providers developing artificial intelligence (AI) and machine learning technologies to solve challenges or improve efficiency in financial services in FinTech Global's annual AIFinTech100 list – a list that highlights tech companies transforming the global financial industry by leveraging AI and machine learning. 

WealthTech Trends for the Second Quarter 2021

Every quarter, we summarize the three most prominent trends in WealthTech in a short video. At the same time, we know that some of you might prefer to have our summary in a text format! Therefore, we share the transcript for your convenience as well. In the second quarter of 2021, we noted more customers become comfortable with the idea of AI managing their funds. Additionally, there is a growing demand for holistic platforms allowing wealth management customers to access their overall financial health information. Third, although hybrid solutions gain even more prominence, poor digital experiences may cost financial planners their customer's loyalty, and therefore attention to detail is as critical as ever.

WealthTech Trends for the First Quarter 2021

Every quarter, we summarize the most important trends in WealthTech in a video summary. However, we know that some of you might prefer to have it in a text format! Therefore, we share its transcript for your convenience as well. In the first quarter of 2021, we identified the three most prominent trends in our emerging field. First of all, the pandemic served as a catalyst for advisory services and the accompanying experience becoming the wealth management industry’s core value proposition. Secondly, as advisory relations and inheritance transition from the older generation to the younger, the industry must be ready for the tectonic shifts concerning customer demands. Third, we see a rise in demand for hybrid solutions, where wealth managers leverage financial analytics to optimise the quality, administrative side and timeliness of the asset allocations.

Choosing Analytical Tools for Digital Financial Journeys: What matters?

The digitalisation process in finance is rarely linear and intuitive, therefore, it might be challenging to find a tool that best suits one’s vision. While there are not (yet) any established criteria to consider when building cutting-edge analytical capabilities within digital financial planning, we have identified a few that we frequently use to secure efficient product development on a day-to-day basis. From model quality and granularity to transparency, we believe these elements to be beneficial when one is planning their digitalisation journey.