Executive Summary

The wealth management industry is experiencing enormous pressure from increasing regulatory requirements, low portfolio returns and fierce industry competition. There is an increasing need to reduce costs and deliver industrial-scale wealth management propositions. To maintain and grow market share, wealth managers need to boost portfolio returns, adapt to regulatory requirements, and generate cost and scale efficiencies in their investment process. This part will look at some of the ongoing trends and changes in the financial services industry and how asset managers can (or should) be transforming themselves to stay relevant and “ahead of the curve”. This will include an overview of different technologies, including the buzzwords “du jour” such as artificial intelligence (AI), machine learning and cloud computing.

PSD2 will fundamentally transform the banking landscape as we know today, and we see it as the first step towards openness in the financial industry. Platform strategy is a solution that has demonstrated its value by creating high-value marketplaces. That strategy can be different for each firm, but will require leadership and self-awareness from senior management to embrace the unknown. The wealth management sector has been one of the last bastions resisting innovation, and investment managers and private banks should consider PSD2 as their wake-up call. We will also discuss changing client behaviours and the demands these will have on traditional wealth management.

One chapter is dedicated to alternative lending. Many FinTech companies born in the last decade have new innovative solutions in alternative lending, with the aim of gaining market share by replacing bank lending. They have an attractive value proposition. However, scaling up the initial success remains a challenge and only a selected few managed to expand their businesses. This chapter explains how to gain market share in alternative lending and describes the most important factors for success.

AI is changing every aspect of human life – creating ways to develop precision solutions that were not possible before. The application to personal finance is one that is democratizing access to high-quality advice, and transforming how people save, invest and think about their financial future. The capital markets constitute a complex system with increasing amounts of data, that evolves beyond established theories and thus cannot be explained and predicted by traditional models. Adaptable, self-learning systems are needed to model and forecast the changing markets. AI and deep learning allow us to model the markets without human-derived assumptions and to identify patterns and relationships in stock market data to generate ranked forecasts. The forecasts provide investors with important insights when managing their wealth and are also utilized in the development of AI-driven trading and allocation strategies and structuring of corresponding investment vehicles.

Computers can be clever, but surely they lack any inherent sense of self, humanity or ability to act fiduciary? Hot on the heels of robo-advice, robo-selection – as it relates to the selection and research of mutual funds – is the next frontier for FinTech. As more assets move to index-based and systematic strategies, so the dynamics of fund research are changing. One chapter will focus on how robo-selectors can put the client above all others.

Finally, we will address the lack of diversity in finance. We see measurable action with the launch of Bloomberg's Gender Equality Index (GEI). The final chapter in this part will outline the development and creation of the GEI and insights derived from the data. It will also discuss how encouraging disclosure across industries, as the GEI has done in financial services, can get us closer to gender parity by catalysing a virtuous cycle – where disclosure and transparency can lead to changes in practice and behaviour; and when behaviour changes, culture follows; and when culture changes, it can change reality.