The AI Book

The Artificial Intelligence Handbook for Investors, Entrepreneurs and FinTech Visionaries

Ivana Bartoletti, Anne Leslie, Shân M. Millie, Ivana Bartoletti, Anne Leslie, Shân M. Millie, Ivana Bartoletti, Anne Leslie, Shân M. Millie

• Publisher: Wiley
• Year: 2020
• Print ISBN: 9781119551904
• eBook ISBN: 9781119551928
• Edition: 1
• Topic: Business
• Subtopic: Finance
• Index: Business

Written by prominent thought leaders in the global fintech space, The AI Book aggregates diverse expertise into a single, informative volume and explains what artifical intelligence really means and how it can be used across financial services today. Key industry developments are explained in detail, and critical insights from cutting-edge practitioners offer first-hand information and lessons learned. Coverage includes:

· Understanding the AI Portfolio: from machine learning to chatbots, to natural language processing (NLP); a deep dive into the Machine Intelligence Landscape; essentials on core technologies, rethinking enterprise, rethinking industries, rethinking humans; quantum computing and next-generation AI

· AI experimentation and embedded usage, and the change in business model, value proposition, organisation, customer and co-worker experiences in today's Financial Services Industry

· The future state of financial services and capital markets – what's next for the real-world implementation of AITech?

· The innovating customer – users are not waiting for the financial services industry to work out how AI can re-shape their sector, profitability and competitiveness

· Boardroom issues created and magnified by AI trends, including conduct, regulation & oversight in an algo-driven world, cybersecurity, diversity & inclusion, data privacy, the 'unbundled corporation' & the future of work, social responsibility, sustainability, and the new leadership imperatives · Ethical considerations of deploying Al solutions and why explainable Al is so important

Part 1
AI: Need to Know

Artificial intelligence (AI) is poised to disrupt lives, businesses, whole economies and even the international geopolitical order. As such, it has never been more important to have a clear understanding of what AI is and the ramifications of its mass adoption, particularly in the financial services sector. However, the inherent complexity of the topic is often intimidating to non-specialists, and the absence of broad-based dialogue on the topic of AI is hindering business decision-making related to its application.

What exactly is AI; how is it being used in financial services; what is at stake; who are the major players; and what lies over the horizon?

In Part 1, we will explore all these questions and more. By delving into the detail behind the hype, readers will gain a firm understanding of the different type of technologies that fall under the more general, and somewhat opaque, “AI” heading. We will have the opportunity to look at how nation states are jostling for position and international competitive advantage relative to their peers through their national AI strategies and action plans. We will also have a chance to learn about tried-and-tested recommendations for successfully embedding AI into the daily operations of financial services firms, while avoiding the myriad pitfalls that still unfortunately get in the way of firms reaping the full advantage of their AI investments.

Finally, we will take a close look at the “human” aspects of AI and examine the reasons why, in the face of the growing sophistication of algorithmic systems, the exercise of sound human judgement, governance and control has never been more important. We will look at the role of boards and directors in the formulation and execution of AI strategy within firms, and we will see how artificial intelligence systems that complement human cognition have the potential to deliver maximized value.

CHAPTER 1
The Future of AI in Finance

By Chee-We Ng¹

¹Venture Capitalist, Oak Seed Ventures

How will artificial intelligence (AI) transform finance? What can AI do and how can we get it to work? What do we need to do to regulate AI in finance? These are questions at the forefront of many minds as we try to investigate the future of finance.

AI, a loosely defined set of technologies that try to mimic human judgement and interaction, has been in use in banking and finance since its inception in the 1950s. AI encompasses everything from rule-based technologies and probability-based methods that detect fraud, through to primitive neural networks for optical recognition and automatic stock and option trading. Collectively, these technologies automate processes that were previously undertaken by human beings, often improving accuracy and efficiency. One might argue that none of these traditional AI technologies is truly intelligent; AI merely automates what was previously performed manually.

The Promise of Deep Learning

The recent excitement around AI has tended to be linked to deep learning in its various forms. To understand why deep learning technologies simultaneously inspire excitement among researchers (who believe that deep learning is the breakthrough in AI everyone has been waiting for), and fear among tech leaders and politicians, it is important to place deep learning in the context of what its component technologies have achieved in the past 6 years.

The most recent wave of deep learning began in 2012 when Geoffrey Hinton and his students used deep convolutional neural networks (CNN) to tackle image recognition, a problem that has baffled scientists and engineers for many years. By achieving significantly higher detection rates and smaller false positives without having to write complicated code, Geoffrey Hinton was able to teach computers how to classify images just by showing many labelled samples, hence the term “machine learning”. AI was taken to new heights in 2017, when Google’s AlphaGo, and subsequently AlphaGo Zero, beat the world’s best Go player, Hanjin Lee. Using reinforcement learning, AlphaGo Zero learnt how to play by playing against itself without having been provided any instruction on how to play. Not only did it teach itself Go strategies humans had developed over hundreds, and possibly thousands of years, it developed strategies that no human had ever conceived of previously.

Meanwhile, recurrent neural networks (RNN), and variations like long short-term memory (LSTM), improved machine translation significantly, while generative adversarial networks (GANs) succeeded in restoring colour photographs from old black and white ones, creating cartoons and oil paintings from photographs and even making fake videos and photographs. In a matter of years, deep learning has demonstrated, at least under certain conditions, that it can learn better than humans (without being taught) and be capable of mimicking humans themselves.

Business Applications in Finance

Today, AI and deep learning have broad ranging applications in deposits and lending, insurance, payments to investment management and capital markets. Deep learning methods are now better than probability-based methods in fraud detection. Like image recognition, fraud detection is a classification problem. Instead of creating static rules which struggle with keeping up and are not sufficiently discerning at times, deep learning solves the classification problem by letting the machine learn by itself. Similar technologies are used in assessing the right premiums for insurance markets and making predictions about stock market prices based on a large number of variables, which can then be used for automated trading.

Just like how AlphaGo Zero taught itself strategies of Go that humans haven’t discovered, deep learning is now used in finance to make connections between large numbers of seemingly unconnected events and variables to make predictions for fraud detection, insurance pricing and trading stock. With strides in natural language processing (NLP) achieved by deep learning, chatbots are also used in banking and finance to do preliminary sales and improve customer service, replacing human customer service agents.

Time for a Reality Check

Despite having made significant breakthroughs, deep learning nonetheless has limitations. These limitations can present themselves in the form of implementation challenges, unintended consequences and ethical issues. In order to implement deep learning technologies well, large quantities of labelled and clean data are often required. Picking the right neural network architecture and the number of layers is largely an art today and performance and robustness varies with architecture. To obtain large volumes of clean labelled data often requires significant effort on the part of firms in consolidating, fusing and cleaning large volumes of source data.

Data needs to be unbiased, or otherwise the machine will learn the bias that is inherently embedded in the data. It is a known fact that many facial recognition algorithms work well with certain races but much less reliably in other races and gender. It is also known that language models today are sexist or discriminatory because of biases engrained in the training data. When such biases exist in finance, it means that certain races or gender may be subject to lower approval rates for loans, or higher interest for mortgages or higher premiums for insurance.

Furthermore, because deep learning is essentially still a “black box”, it can fail catastrophically in unexpected ways. Studies have shown how when noise imperceptible to the eye is added to images, deep learning can recognize a panda as a cat with high confidence. It has also been demonstrated that deep learning algorithms used in autonomous cars to recognize road signs can be easily tricked.

As deep learning learns patterns and correlations without understanding causality, its classification result may be based on the wrong features, or features that are only temporal, or even features that coincide but actually do not mean anything. When deep learning is applied to finance, it can mean that loans could be rejected unfairly for a reason that is hard to decipher and explain to customers. Meanwhile, it is also plausible that a smart attacker could fool a deep learning model used to detect fraudulent activity.

Safeguards and Systemic Risk

When AI is used in isolation, the impact of major failures could be large but contained. However, as AI is being used more and more in connected systems such as in the stock market for automated trading, unexpected catastrophic failures could lead to the widespread failure of entire systems. We don’t need to go very far back ...

Table of contents

• Cover
• Copyright
• Title Page
• Preface
• About the Editors
• Acknowledgements
• Part 1 AI: Need to Know
• Part 2 Deposits and Lending
• Part 3 Insurance
• Part 4 Payments
• Part 5 Investment and Wealth Management
• Part 6 Capital Markets
• Part 7 Trust, Transparency and Ethics
• Part 8 Legal Risk and Regulation
• Part 9 The Future of AI in Finance
• Appendix
• List of Contributors
• Index
• End User License Agreement