Knowledge Base Articles

Skandia case study III: Using OutRank to Enhance their Investment Customer Journeys

Skandia, the Swedish life insurance company, has ramped up its initiatives in using technology to improve the overall experience of its customers. The goal is simple – developing a digital space to offer touchpoints relevant and meaningful enough to drive engagement across all of Skandia’s channels.

Personal Accident Insurance: Would My Savings Suffice?

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 Case Study II: Building Channel-Agnostic Wealth Experiences

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.

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!

Steps to heaven - How to take your customers on a journey to the land of digital trust

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.

Skandia Case Study: Pioneering Seamless Digital Wealth

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 Case Study: How to build an innovative financial advisor in under seven months?

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.

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 III - Portfolio Construction - The Real World Analysis

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.

Part II - Portfolio Construction - Sampling & Optimisation

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.

Part I - Portfolio Construction - Parameter & Model Uncertainty

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.

Blog Articles

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.

Bridging Gaps in Consumer Duty

The purpose of the UK’s latest regulatory update goes beyond additional reporting and addresses the value created by wealth management and insurance businesses. It aims to set a higher standard of care and deliver better consumer outcomes throughout the customer journey. The overarching principles set out to change how products and services are evaluated, priced, explained and supported, and it should transform the relationships within wealth management value chains.

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.