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Knowledge Base Articles

OutRank User Guide: The Walkthrough of the Digital Investments Use Case

Welcome to our brand-new series describing the elements of digital financial experiences you can build using OutRank API!

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.

Hierarchical Clustering: Prediction of Systematic Underperformance

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.

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

Fast and slow data: How to enable fast, interactive customer journeys based on slow mathematical models

When it comes to digital journeys, one characteristic defines quality beyond industrial specifics: speed. While rule-based apps or websites are relatively easy to keep lean and quick, the financial industry may be the area where the speed of underlying calculations could be an issue. Unlike e-commerce or media, the digital and physical solutions provided by the financial sector are riddled with computationally heavy models trying to grasp the uncertainty of real-world economies. The more granular and elaborate the underlying model is, the more realistic and accurate its results are. Does it mean that the financial institutions will have to compromise on quality to deliver fast solutions? Today we have spoken to Erik Brodin, an ex-McKinsey quant expert at Kidbrooke, who doesn’t believe a compromise is necessary.

A little more talk, a lot more action

Elvis Presley sang “A little less conversation, a little more action please” famously in the 1968 film Live a Little, Love a Little. But when it comes to customer engagement, a little more talk with customers can lead to beneficial actions. Customer engagement is one of the most important activities that a financial services firm can undertake so let's take a look at how you can maximise your customer relationships.

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. 

Is Your Robo Advisor Fit for The Job?

Amidst the strategic decisions and the fears of a mysterious AI stealing the jobs of financial advisors, we believe one important detail remains overlooked. Do we properly understand the machines that are to automate an essential part of our value chain or that may become an alternative to our human operators?