To better understand how asset managers compete with one another, it is necessary to look at how firms sell funds and the development of new products. There are more than 4,580 fund management companies across Europe, although many of these manage tiny pools of money (EFAMA 2021a). This number fits with the industry narrative that there is intense competition and that a large number of firms are constantly vying with one another to show that their funds are better than others.

However, taking Europe as a whole, many of these firms will not compete against one another. This is either because any given international firm is not necessarily active in every European market, or because a firm is a part of a larger group (such as a bank or insurer) and primarily provide funds to be used internally rather than competing with independent asset managers for a client's business. Having said this, even when individual countries are singled out, the number of competitors is still large, with approximately 1,100 asset managers in the UK, 680 in France and 386 in Germany (EFAMA 2021a). In addition, there are 417 companies in Ireland and 268 in Luxembourg. And even if these latter two partly overlap with the lists for the three largest European fund markets, they still swell the numbers of competitors.

These numbers also show that barriers to entry for establishing an asset management business in Europe are low. Getting a business off the ground has been made easier over recent years as service providers have made better use of technology, giving small investment management firms the ability to outsource more of their operations. The robustness of these small operations relying on large third-party providers was proven over the recent past as asset managers were forced to work from home through the pandemic. The regulatory burden in Europe has clearly increased over the past ten years, but this has not stopped the launch of fund management businesses, even if it has added to the cost burden, albeit for larger firms in particular.

But establishing a firm is different from making it a successful business. The biggest challenge for a fund management company is to attract sufficient assets early on to keep the business going. Gaining seed investors is crucial for this process, not just for making the business viable, but also for attracting more clients.

Fund managers play an important role in free market economies around the world, controlling more than $100 trillion of financial assets globally, with the ability to invest that capital into companies and to influence the way in which they are managed. Most research and literature on asset management focuses on investing – how fund managers do it, who does it well, the ways in which it has changed, as well as advice on how others can invest too. This leaves a largely unfilled gap looking at the rest of an asset manager's business, ranging across sales, operations, governance, marketing, risk management, finance and compliance. It is these functions that are needed to turn a solo stock picker into a successful business. They are integral to making investment management commercially viable. And it was these aspects that moved from the periphery to centre stage when Neil Woodford, the UK's best-known fund manager, was forced to close Woodford Investment Management in 2019 amid an acrimonious split with the firm overseeing its operations. Around 300,000 investors are still trapped in Woodford's former flagship fund (Martin 2019).

This is not just a British story. Fund manager scandals have broken in recent years at Swiss asset manager GAM, French firm H2O Asset Management, and German Union Investment. Unlike Woodford, these asset managers are backed by large financial institutions. H2O was owned by Natixis Investment Managers (part of French banking group BPCE) and got into difficulties because of its relationship with controversial German financier Lars Windhorst, while GAM was caught up with disgraced Australian financier Lex Greensill. The chief executives of Natixis IM and GAM have both stepped down since these revelations broke.

Understanding how such problems can arise is part of how these businesses make money, the nature of their business models, the role of their compliance and oversight functions and how they incorporate investors’ interests. Each of these will be examined throughout this book. It will also look at fund managers’ increasing focus on sustainable investing – investing with the long-term good of wider society and the planet in mind, not just focusing on shorter-term financial interests. As the large sums of money that fund managers earn are more prominent in the popular imagination than what fund managers do, it is no small task for firms to demonstrate that they are focused on social and environmental issues.

We describe fundamental challenges to estimating heterogeneous treatment effects in the context of the statistical causal inference literature, proposed algorithms for addressing those challenges, and methods to evaluate how well heterogeneous treatment effects have been estimated. We illustrate the proposed algorithms using data from two large randomized trials of blood pressure treatments. We describe directions for future research in medical statistics and machine learning in this domain. The focus will be on how flexible machine learning methods can improve causal estimators, especially in the RCT setting.

To address the challenge of limited training data for machine learning models in the healthcare domain, we advocate for human-in-the-loop machine learning, which involves domain experts in an inter- active process of developing predictive models. Interpretability offers a promising way to facilitate this interaction. We describe an approach that offers a simple decision tree interpretation for any complex blackbox machine learning model. In a case study with physicians, we find that they were able to use the interpretation to discover an unexpected causal issue in a personalized patient risk score trained on electronic medical record data. To account for dynamics in disease progression, we advocate for building decision models that integrate predictions of the disease progression at the individual patient level with system models capturing the dynamic operational environments. We describe a case study on hospital inpatient management, showing how to build a Markov decision framework that leverages predictive analytics on patient readmission risk and prescribes the optimal set of patients to be discharged each day.

In this chapter, we explore how data-driven modeling can improve the understanding of OHCA risk, help identify the limitations of current AED placement strategies, and guide the development of optimal AED networks to increase the chance of AED use and OHCA survival. More specifically, we frame AED network design and related response efforts as a facility location problem, focusing on the maximum coverage location and p-median problems. We also highlight how novel tools that combine techniques from areas including information theory and machine learning with optimization models can shape the future of OHCA response efforts and AED placement strategies.

This chapter provides an overview of how researchers form interdisciplinary partnerships that leverage analytics-driven methods in AI, IE, and OR to tackle the most difficult societal and operational problems in healthcare.

This chapter provides an introduction to analytics-driven hospital capacity management through three projects that employed mathematical programming and discrete event simulation to address common challenges. The first project used mathematical programming to identify the mix of patients at Stanford Hospital that would maximize revenue given the capacity of hospital resources after a planned hospital expansion. The second project used discrete event simulation to plan the physical capacity and operational profile of a new procedural space at a hospital in New England. The third project combined mathematical programming and discrete event simulation to create a tool to schedule surgical procedures at Lucile Packard Children's Hospital Stanford.

This chapter offers lessons from engineering and other industries that promise developments in healthcare, and practical guidance for clinician-engineer partnerships. Section 1 provides guidance on how to establish a shared vocabulary and common understanding between engineers and clinicians of what terms such as AI and ML do and don’t mean. Section 2 identifies challenges clinician-engineering partnerships must overcome to deliver sustained value and ways to avoid common causes of failure. Section 3 provides specific advice on how to design projects to produce value at a series of stages rather than rely on the success of one, ambitious final model. Section 4 concludes by drawing on cautionary lessons from healthcare and other industries.

Designing AI-assisted technology to better understand major depressive disorder and further develop appropriate strategies for monitoring and treatment of major depression under resource constraints is an important and challenging task. In this chapter, we present seven studies that developed methods for AI-assisted, data-driven decision support systems to aid healthcare professionals. These methods focus on modeling chronic depression’s complex disease trajectories, identifying patients at high risk of progression, and recommending adaptive and cost-effective follow-up care. Long-term goals of this research include improving patient health outcomes and facilitating efficient allocation of healthcare providers’ limited resource through the use of novel technology.

Mathematical models may be used to optimize the decision of when to screen for cancer and how invasive a test to use, for example a biopsy or a biomarker. Partially observable Markov decision process (POMDP) models may be used to optimize screening decisions based on a patient's belief state, which is calculated using Bayesian updating and comprises a patient's complete history of biomarker test results. POMDPs can be used to determine how, if at all, biomarkers should be used for cancer screening in order to maximize quality-adjusted life years, a population health measure of disease burden that incorporates both the quality and quantity of life.

In this chapter, we: (1) detail the problem scope and gatekeeper training as a solution to it, (2) delineate key issues that can impact GT as well as the consequences of ignoring them, (3) explore the value of social network information for GT recruitment, (4) explain the mathematical model of GT and an algorithmic solution approach that can account for gatekeeper, population, and institutional issues, and (5) describe experimental results that compare a novel algorithmic solution for GT recruitment with recruitment approaches that approximate common GT practice.