WealthTech

Knowledge Base Articles

Today’s case study examines a real-life experience of a Swedish family who struggled to receive adequate help from the local wealth management service providers.

Skandia strives to build communication channels in a digital space that would match the physical experiences in engagement levels and even improve the service quality in a way that has not been achievable before.

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

Fredrik Daveus, CEO at Kidbrooke®, explores how to build trust in digital wealth management for the Swiss WealthTech Landscape Report 2021 by The Wealth Mosaic.

The financial guidance and advice services, which constitute the life insurer’s core business, were among the first to go through the transformation. Joakim Pettersson, the digital strategy and innovation lead at Skandia, believes that digitalisation is “the only way to scale financial advisory services”.

Evida began its path as a family office managing a wide range of assets for wealthy families. Initially, the Swedish financial advisor outsourced the management of equity and fixed income positions to other parties. However, the combination of their interest for factor-based investments and dissatisfaction with wealth management services provided by the largest banks in Sweden, Switzerland and Luxembourg convinced Evida to build their own digital advisory service.

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.

In the third and the final part of our “Portfolio Construction” article series, the findings of the previous sections are applied to a broader and more realistic set of assets to evaluate the performance of the proposed methods against more conventional techniques.

The second part of the “Portfolio Construction”-series explores whether introducing parameter uncertainty to the model would improve the out-of-sample performance of the optimal portfolio. Additionally, the article proposes and tests two adjustments to regular utility optimisation.

There is a number of challenges associated with portfolio construction based on historical data. This three-part article series explores some of the most common issues attributed to the model-based portfolio optimization: the sensitivity to changes in data, large variations in portfolio weights and the bad out-of-sample performance.