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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

A Step Towards Data Readiness: Improving Financial Data Aggregation

The diversity of financial data sources and forecasting requirements are common challenges that banks, insurers and investment managers face when creating digital financial journeys. Robust data management is an important prerequisite to creating personalised experiences matching the complex financial situations of consumers to relevant offerings or helping financial professionals create portfolios that meet their needs. Here, we summarise the common hurdles that firms must overcome as well as strategies to update their businesses.

Key Trends in Wealth Management Q3 - 2023

How can wealth management companies ensure profitable growth during this volatile era? As the industry continues navigating an increasingly difficult financial landscape, several key trends have continued reshaping how wealth is managed. Among these trends, the rise in importance of the environmental, social, and governance (ESG) factors, more regulatory attention to consumer treatment, more recognition of the financial analytics tools, and the deeper integration of AI, machine learning and automation, all stand out as notable drivers of change.

Democratising Wealth Management by Utilising Financial Analytics

The wealth management sector is embracing emerging tech like machine learning and gamification to enhance workflows. Despite inflation and conflict challenges, technology is poised to reshape finance. A key concern though is how to leverage tech to expand services while maintaining margins for mass affluent clients.

Enhance Mutual Fund Grouping Using Machine Learning

The methods used to recommend mutual funds to customers vary greatly between companies. Often the recommendation techniques used rely on using metadata of the mutual funds, such as region, category, or investment objective. By grouping using these properties investors are given an overview of funds with similar classifications and can select funds from the groups they are interested in. And while grouping mutual funds in this way may provide investors with a convenient way to explore funds that align with their preferences and investment strategy, this method of recommendation has some potential limitations and risks.