The AI Book E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Sprache: Englisch

Seitenzahl: 623

Veröffentlichungsjahr: 2020

Beschreibung

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

Leseprobe

“AI is a constellation of technologies that allows machines to sense, comprehend, act and learn. Like any powerful technology, AI entails responsibilities that need to be understood, communicated and addressed. I can only applaud the valuable contribution of this book and advocate for using AI+DLT to guarantee the ‘trustlessness’ of next-gen platforms: contexts where trust in parties is unnecessary because the system itself guarantees validity veracity and integrity of data and predictions.”

Laura Degiovanni, Founder and CEO, TIIQU

“As a GDPR EU representative, I work exclusively with companies that move personal data across borders. Because that data comes from various sources located in multiple countries, it enables AI systems to uncover patterns, make connections and manage risk. These international data flows are crucial to grow the use of AI in financial services.”

Jane Murphy, EDPO

“AI is changing financial services beyond recognition. This is a timely and excellent handbook on the subject and should be compulsory reading for all in the financial services sector. It treats in straightforward terms the complexities, major challenges but importantly also sets sight on the abundance of opportunity AI offers. The editors have gathered an impressive array of market commentators, practitioners and AI experts to produce the AI reference book of 2020.”

Kieran Rigby, Global President, Claims Solutions, Crawford & Company

“AI is a new world for most of us, but it is developing fast so the more insight and transparency we bring to the topic the better for everyone. AI presents a fantastic opportunity for our profession to do things much quicker, more rigorously and with more personalization than has ever been possible before. But we need to embrace this and share experiences. This is why The AI Book is super helpful.”

Sian Fisher, CEO, Chartered Insurance Institute

“The UK remains a global leader in Insurance. Brokers are integral to this, as is the commitment to fully understand and embrace the risks and opportunities of Innovation, especially in Technology, and fast-moving areas like AI. On behalf of its 1850+ FCA-regulated members, BIBA remains actively engaged here – on data ethics, digital upskilling, and Insurtech, for example. BIBA is pleased to support ambitious projects like The AI Book, and its focus on making accessible ideas and experience from all areas of Financial Services, not just Insurance, so that we can continue to deliver the best outcomes for customer.”

Steve White, CEO, British Insurance Brokers Association

“As an AI practitioner, I truly believe the AI technology to be only valuable in real-world applications. The AI Book, written by technology and business experts, is a great tool for busy executives both in China and abroad who would like to learn more about AI and how it may impact their businesses.”

Dong Li, PhD and MBA, CTO, Sunshine P&C Insurance Company

“The AI Book is a much-awaited cornerstone to holistically applying artificial intelligence to finance while highlighting the importance of trust, transparency and ethics. AI brings transformative changes for economies and societies in the world, and these changes need to benefit all people. The AI Book demonstrates how we can harness the potential of AI for financial services by putting our human values at the heart of it. The book written by great AI experts globally can guide and inspire you to think further into the future. A must read.”

Gülser S. Gorat, Director, UNESCO

“Artificial intelligence is the stealth disruptor of the financial services industry and its impact is being felt in every corner, from risk modelling and compliance to chatbots and roboadvisors. But with such transformational power comes legal, regulatory and ethical issues. The AI Book, crowdsourced from leading industry experts, provides important insights into the use of AI in financial services, as well as the debates surrounding its application.”

Joy Macknight, Managing Editor, The Banker

“As a major financial services company we are already experiencing on a day-to-day basis how transformative, and disruptive, AI can be for our business, from trading, risk analysis, research, and wealth management to even straightforward processes such as client identification and KYC reporting. This book has proved to be an invaluable guide to these many different applications for AI in finance and how it can benefit businesses, and where it may not. In sum, a very timely and helpful contribution to understanding the real world implications of AI in finance.”

Miranda Carr, Managing Director, Research, Haitong International (UK) Limited

“Technologies are meant to solve business problems. Artificial Intelligence is no exception. It can help make decisions and predictions by analysing huge amount of data in real time. The highly computerized and data-rich financial services industry is a key industry that is very suitable for AI applications. AI can help in many financial service scenarios such as credit decisions, risk control, asset allocation and portfolio rebalancing. You will find all these interesting topics in The AI Book, written by global AI pundits and industry insiders. I highly recommend it.”

Ning Tang, Founder, Chairman and CEO, CreditEase

“Artificial Intelligence has been transforming the world digitally, bringing limitless potential to push us forward to enormous business opportunities and social wellbeing. Contributing US$15.7 trillion to the global economy by 2030 according to PwC’s research, AI should also go hand in hand with proper governance and responsible framework. A good read of the AI Book to help harness the power of AI in an ethical and responsible manner. Responsible AI starts with responsible leaders!”

Elton Yeung, Vice Chairman, PwC China

“AI is an emerging technology and TheAI Book is required reading by professionals in trade finance and working capital markets globally. AI is being increasingly harnessed in a variety of applications, starting with invoice data capture, credit assessment and pricing, to fraud and money laundering mitigation in suspicious transactions. Check out TheAI Book for the all the latest in AI and machine learning tools.”

Walter Gontarek, CEO and Chairman, Channel Capital

“In financial services, the harvesting of data and wrangling of it to unlock its power for Artificial Intelligence and Machine Learning is proving to be the lifeblood of the industry. As we progress into the future, Machine Learning in financial services will continue to lead the pack and allow us to solve increasingly complex problems that would otherwise be impossible without harnessing the power of AI. The AI Book is packed with information from leading experts on how AI is used and impacts the financial services industry.”

Shuki Licht, Chief Innovation Officer, Finastr

“AI will undoubtedly impact every stage of the insurance value chain, from customer acquisition and customer experience, to underwriting, product development, pricing and ultimately through each stage of the claims settlement process. Few other technologies have the potential to impact the industry so significantly as an enabler to innovation in a changing world where information underpins every decision. Insurance organizations ignore or limit the application of artificial intelligence at their peril; utilizing and understanding data to the benefit of the ultimate customer will always be a successful business strategy and a competitive advantage. This book will help leaders and executives understand more about how to get that done.”

Ruth Polyblank, Vice President, Insurance, Mastercard

“From the early days AI for the financial industry, to Deep Blue, invented by Ron Coleman at IBM, the AI chess game that beat Kasprov in 1997, these were all incremental steps that have lead us to the most significant and profound changes that will reshape the financial markets. Today, we are seeing many platforms emerge, and free open source code from the biggest players like Google, and it will be several years before we know who will emerge as the tech AI victors. But one thing is certain, we are in the exploratory services phase of AI, where banks are learning from service providers who know how to piece the correct AI components together to solve real problems. Within two to three years, we will witness an AI boom no different from the Internet craze of the 1990’s. If you plan to be in the AI game, strap yourself in, read The AI Book, and this will guide you and shape your thinking on how you can take advantage of the forthcoming AI wave.”

Steven O’Hanlon, CEO, Numerix LLC, NYC

This edition first published 2020

© 2020 FINTECH Circle Ltd

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ISBN 978-1-119-55190-4 (paperback)    ISBN 978-1-119-55186-7 (ePDF) ISBN 978-1-119-55192-8 (ePub)       ISBN 978-1-119-55196-6 (Obook)

Cover design: Wiley

Cover image: pkproject/Shutterstock

The AI Book

The Artificial Intelligence Handbook for Investors, Entrepreneurs and FinTech Visionaries

Edited by

Susanne Chishti

Ivana Bartoletti

Anne Leslie

Shân M. Millie

CONTENTS

Cover

Preface

About the Editors

Acknowledgements

Part 1 AI: Need to Know

Chapter 1 The Future of AI in Finance

The Promise of Deep Learning

Business Applications in Finance

Time for a Reality Check

Safeguards and Systemic Risk

Chapter 2 What Is AI and How to Make It Work for You

1. Be Narrow Minded

2. Weigh the Risk

3. Get the “Last Mile” Right

4. Consider That Less Data May Mean More

5. Do Your Homework

Note

Chapter 3 Getting to Day Zero: Let’s Get the Foundation Right

Challenge 1: A House Built on Sand

Challenge 2: The Digital Transformation Dilemma

Opportunity 1: Share Your Data with the World

Opportunity 2: The Alternative Data Revolution

A Bright Future

Notes

Chapter 4 Navigating a Sea of Information, News and Opinion with Augmented Human Intelligence

Making Sense out of Complex Text through Natural Language Processing (NLP)

Ontologies Link Entities and Thus Create Valuable Connections

How Augmented Human Intelligence Will Change the Way We Read News and Inform Ourselves

Note

Chapter 5 The Seven Deadly Sins of AI

Data

Research Failure

Bias

Explainability

Emotion

Ethics

Organizational Readiness

Conclusion

Chapter 6 A New Internet, Data Banks and Digital World War

The Future of Artificial Intelligence

Reinventing How We Invent

AI Neural Network

The Human API and Digital War: World War III

Chapter 7 AI: A Cross Country Analysis of China versus the West

Notes

Chapter 8 The AI Advantage: Near-Term Workforce Opportunities and Challenges

Backdrop

Enhanced Cognition: The Good News

Macro-Trend Analysis of Workforce Challenges

Pragmatic Suggestions for a Way Forward

Conclusion

Notes

Chapter 9 The Art of Involving Boards in Embracing AI

The Art and Science of Board Dynamics

The Challenge of AI for Boards

Work with Strength-Based Management Techniques

Design and Provide Special Educational Programmes for Board Members

Dare to Change the Composition of the Board

Establish an AI Council

Create a Communication Campaign

Note

Part 2 Deposits and Lending

Chapter 10 AI in Lending

Overview

User Identification

Credit Decisions

Fraud Prevention

Consumer Lending: Proprietary Risk Management Based on Big Data

Credit Decision Powered by Knowledge Graph

SME Lending: Unsecured Loans Backed by AI and Machine Learning

Chatbots Used in Debt Collection

Chapter 11 Financial Technology and China’s Inclusive Finance

Chapter 12 The Future of Deposits and Lending

Value Stores

Future of Deposits

Access to Credit

Changing Expectations

Data Deluge

Future of Lending

Chapter 13 Applications of AI in Deposits and Lending

Alternative Data

Value Chain

Origination and Onboarding

Underwriting

Financing and Contract

Servicing and Payment Collections

Imagine!

How Much Data Is Enough?

How Much Information Do You Store About Your Customers?

Is What You Are Doing Transparent, Ethical and Fair?

Last Word

Chapter 14 Showcase and Customer Service: Leveraging Chatbots in the Banking Industry

Brief History

The Expansion of Chatbots

Banking Applications

Non-Banking Services

Money Matters

Information and Banking Operations

Financial Coaching

Conclusion

Note

Chapter 15 The Power of AI to Transform the Global SME Credit Landscape

Identifying More Creditworthy SMEs

Speed Is of the Essence

Problem Solving, Sector by Sector

The Power of AI to Shift Capital

Note

Chapter 16 Using AI for Credit Assessment in Underserved Segments

Note

Chapter 17 Why Video Games Might Help You Buy Your First House

The Problem

How Do We Bridge the Gap?

Chapter 18 AI Opportunities in the African Financial Sector: Use Cases

Two Significant Challenges: Financial Exclusion and Cybercrime

The solutions widely used in African FinTech ecosystem

AI at the Heart of Innovation FinTech Solutions

The Use of Chatbots

Looking Ahead

Notes

Part 3 Insurance

Chapter 19 Insurance and AI: Choices in Leadership, Purpose and Trust

Insurance: Too Important

Not

to Lead on AI?

Your Job as Leaders (1): Get the Reality Check

Your Job as Leaders (2): Question the “Absolutes”

Your Job as Leaders (3): Help the Firm Get Purposeful

Your Job as Leaders (4): Bring AI Ethics to the Boardroom

Final Thoughts: Architects of Exclusion — or Enablers of Protection?

VERY Selected Further Reading

Notes

Chapter 20 Drifting into Algocratic Insurance?

AI and Insurance – A Natural Partnership

Insurance turning Algocratic

Regulating the Algocracy

Chapter 21 Moving the AI Needle: Strategies for Health Insurers to Put AI into Practice

What’s the Problem with AI in Health Insurance?

AI Use Cases That Can Move the Needle

Level 1 – Standard AI Solutions

Level 2 – Tailor-Made AI Solutions

Level 3 – Explainable AI Solutions

How to Get There – Strategic Imperatives

Define Clear Use Cases and Demystify the Topic of AI

Intelligently Integrate Partners Instead of Developing Everything Internally

Focus on Solving Real Business Problems

Value Patient Centricity and Gain Patients’ Trust by Ensuring Transparency

Chapter 22 AI and Healthcare: Doctor Will FaceTime You Now!

AI and Healthcare Now

The Economic Opportunity

What Is the Role of Health Insurers in Emerging Healthcare AI Business Models?

Notes

Chapter 23 Using Artificial Intelligence in Commercial Underwriting to Drive Productivity Growth

Chapter 24 The Digitally-Enabled Underwriter: How AI is Transforming Commercial Insurance Underwriting

Why AI, Why Now?

A Deluge of Data, A Drought of Insights

Use Cases for AI in Commercial Insurance Underwriting

The Rise of the Digitally-Enabled Underwriter

We Are Still at the Beginning

Notes

Chapter 25 Improving Policy Life Cycle Management with AI and Data Science

AI-Supported Policy Life Cycle Management: Point of Sale

Re-Scoring and Re-Evaluating the Initial Application after a Claim Has Been Submitted

It’s Not Just about Fraud

Chapter 26 Disrupting the Insurance Value Chain

Products

Product Management

Customer Onboarding

Underwriting

Customer Services

Claims and Settlement Management

Chapter 27 Cutting to the Chase: Mapping AI to the Real-World Insurance Value Chain

Enabling and Applying AI

History vs Present

Computer Vision

Voice and NLP

Internet of Things

Conclusion

Notes

Part 4 Payments

Chapter 28 Artificial Intelligence: The Next Leap Forward in the Payments Revolution

Chapter 29 Frictionless Payments: If or When?

Today’s Security Paradigms Will Not Suffice Tomorrow

Invisible, Precise, Highly Robust Authentication

Rethinking Authentication

Note

Chapter 30 Big Data, AI and Machine Learning: How to Unlock Their Potential in the New Payment Environment

Payments, a Wealth of Data

A Tool to Combat Fraud

Smart Routing

Getting to Know Your Customer

Advanced Analytics for Merchants

Chapter 31 The Rise of Conversational AI Platforms

Towards Invisible Banking and Payments

Notes

Chapter 32 Two Dimensional Virtual Vertical Integration: Solving the Impossible SC Problem

The Cost to the Economy

How Do Current Practices Inflate Consumer Prices? An Illustration

Introducing 2DVVI

But It’s Not Quite so Simple

The Social Dimension

Notes

Part 5 Investment and Wealth Management

Chapter 33 The True Value of AI to Transform Push/Pull Wealth Management Offers

Chapter 34 Machine Learning in Digital Wealth Management

ML in Wealth Management

Prospecting and Conversion, Onboarding and Screening

Client Onboarding and Screening

Product Recommendations and Onboarding

Advisory Process

Investment Research and Trading

Client Attrition

Summary of Different Algorithms and Use Cases for Wealth Management

Data Sharing and Confidentiality

Federated Learning

Chapter 35 The Impact of AI on Environmental, Social and Governance (ESG) Investing: Implications for the Investment Value Chain

Introduction

The Impact of AI on ESG

Mastering the Data Complexity Challenge with AI

Engaging The Investor Community to Address AI Concerns

Collaboration and Engagement

Conclusion

Chapter 36 AI in Indian Investment and Asset Management: Global Perspective

Inherent Issues in India

AI in Investment Management in India

Some New Scenarios

Emergence of New Business Models

Reference

Chapter 37 Finding Order in the Chaos: Investment Selection Using AI

Random Walk Through Efficient Markets: Are Stock Price Fluctuations Predictable?

Bulls, Bears and Butterflies: Markets as Chaotic Systems

Best of Both Worlds: Investing With AI-Driven Decision Enhancement Tools

Predictive Algorithm Developed by

I Know First

Chapter 38 Dispelling the Illusion

Data-Based Automation

Front, Middle and/or Back Office?

Implementation Strategy

Implications

Notes

Chapter 39 ETF 2.0: Mega Block Chains with AI

Chapter 40 Fear and Greed

Man vs Machine

Quantum Computing

Convergence of Advanced Technologies

Automated Trading

Wealth Creation by Algorithm

The Financial World of Equals

Part 6 Capital Markets

Chapter 41 Introduction on AI Approaches in Capital Markets

Setting the Scene

What Is Artificial Intelligence?

Using Data Science to Solve Business Problems

Capital Markets Use Cases

Trust, Transparency, and Human Interactions

State of the Art: Selected Highlights 2018/19

Where Next?

Notes

Chapter 42 AI, Machine Learning and the Financial Service Industry: A Primer

Defining Artificial Intelligence

Machine Learning (ML) and Deep Learning (DL) within Finance

Barriers — and the Goldilocks Rule

Notes

Chapter 43 Compliance as an Outcome

Simple Heuristics Lead Human Behaviours

Prevention through Deterrence

Data and AI Strategy

Intelligent Empowerment

Compliance and Business Benefits?

Further reading

Notes

Chapter 44 Alternative Data and MetaQuants: Making the Most of Artificial Intelligence for Visionaries in Capital Markets

Back to Basics: What Is Alt-Data?

Redefining Market Players – The MetaQuant Approach

Can a Hybrid Model (Quantamental + MetaQuant) Boost Investment Results?

Notes

Chapter 45 AI and Capital Markets: Where to Now?

Organizational Efficiency — Inside and Out

Regulatory Developments

Future Enablers

Part 7 Trust, Transparency and Ethics

Chapter 46 Trust in FinTech and AI: Some Introductory Reflections

Tech That Has Legitimacy with a Social Licence

Ethical Innovation in Finance

Clarity of Ethical Purpose and Mission Is Central

Regulation Introduced Clarity and Wider Support for Innovation

Four Ways to Support More Trustworthy, Ethical Innovation in the Financial Services Sector

Chapter 47 Building Trust through Sound Governance

Ethical Challenges for Firms

Ethical Governance

Conclusion

Notes

Chapter 48 Independent AI Ethics Committees and ESG Corporate Reporting on AI as Emerging Corporate and AI Governance Trends

Independent Human Research Review Committees (IHRCs)

World’s First Corporate AI-Focused IHRCs

Axon

Corporate ESG Reporting on AI as a New Paradigm?

Notes

Chapter 49 The Wisdom Vantage

Explainability and Transparency

The Future

Wisdom

Chapter 50 AI and Business Ethics in Financial Markets

Fairness

Privacy

Transparency

Explainability

Accountability

Conclusion

Notes

Chapter 51 AI Trust, Ethics, Transparency and Enablement

What Is Intelligent Empowerment and Why Is it Topical?

The FS AI/ML Trust Issue

TETE Proposal

The TETE Need and Challenges

How to Implement a TETE Framework

Conclusion

Bibliography

Chapter 52 Invisible Hand, Spontaneous Order and Artificial Intelligence

Chapter 53 Transforming Black Box AI in the Finance Industry: Explainable AI that Is Intuitive and Prescriptive

The Challenges Hindering Wider AI Implementation

How to Identify an Explainable Algorithm

Unlocking a New Level of Explainability with Prescriptive AI

Industry Use Cases and Compelling Results

Chapter 54 Making Data Your Most Valuable Asset

Why Do We Need Data Ethics Now?

Treating Data as the Asset

Consequences of Data Mistrust

Chapter 55 The Data Promise

The Client Data Promise

Example from the Wealth Management World

Notes

Part 8 Legal Risk and Regulation

Chapter 56 AI and the Law: Challenges and Risks for the Financial Services Sector

Sci-Fi — or Real Life?

Racial Bias — the Tip of the Iceberg?

Legal and Ethical Issues for Your Watch List

The GDPR – a Deeper Dive on Key Data Principles

Self-Regulation – A Viable Strategy?

Notes

Chapter 57 Algorithm Assurance

Chapter 58 Regulation of AI within the Financial Services Sector

The Need for Regulation

Common Technical Standards

Regulatory Measures

Questions of Liability

Future Regulation

Note

Chapter 59 Is Risk-Based Regulation the Most Efficient Strategy to Rule the Unknown Risks Brought by FinTech?

Notes

Chapter 60 The Changing Face of Regulatory, Compliance and Audit

Why We Need Compliance and Audit

Identification of Risks

A Vision for Tomorrow

Conclusion

Chapter 61 Robocop on Wall Street

Setting the Scene – The Why

Mapping out the RegTech and Legal Risk AI Landscape (The What)

Key Building Blocks of AI Solutions Addressing Legal Risk and Regulation (The How)

Judgement and Liability

Notes

Chapter 62 Sure, AI Can Answer Our Questions – But Who Will Answer Our Questions About AI?

Notes

Chapter 63 Technology for Regulations and Compliance: Fit4Future!

Evolution of RegTech

RegTech 3.0: Phases of Development

Conclusion

Part 9 The Future of AI in Finance

Chapter 64 Welcome to the Future

The Evolving Technology Landscape

Beyond Digital Transformation: Thinking Like a Digital Native

Innovation at the Speed of Thought

A Potentially Utopian Future

Chapter 65 An AI-Embedded Financial Future

Job Displacement

Betting the House on AI

The Spending Conundrum

Intelligence, Employment and Social Purpose

Conclusion

Notes

Chapter 66 Open Banking, Blockchain and AI: The Building Blocks for Web 3.0

Genesis

Data Marketplace for the People

The Inversion

Conclusion

Chapter 67 Automated Machine Learning and Federated Learning

Introduction

Shortage of Data

Lack of Trust in AI

Shortage of Qualified Personnel

Federated Learning

Notes

Chapter 68 Deep Learning and Financial Regulation

What Do Regulators Do?

Endless Financial Crises…

…That Are Getting Worse

Autonomous Regulatory Agents to the Rescue

We Created This Mess – We Can Fix It!

Chapter 69 AI for Development and Prosperity

Natural Disasters

Capital Markets

AI for Diversity and Inclusion

Conclusion

Notes

Chapter 70 The AI Trends That Will Shape Winning Businesses

Natural Language Understanding

Multi-Language

Human Personality and Emotional Understanding

Support Process Optimization

Sales Process Optimization

Is China Coming?

Looking Forward

Chapter 71 Mastering the AI Talent Transformation: Present and Future

The AI-Prediction Debate: Technology Anxiety and AI, Is This Time Different?

But, Will AI Eliminate All Jobs in the Future?

From the Future of Work Debate to the Wealth Distribution and Inequality Problem

Is Our Future Preordained by AI Prediction Capabilities?

The AI-in-Practice Challenge: Narrow AI and Its Implementation Challenge

AI and Human Strengths and Weaknesses

The Need for an AI–Human Collaboration Approach

Notes

Chapter 72 Humans versus Machines: Who Will Still Have a Job in 50 Years?

The New Normal

A New Type of Leadership

Workforce Disruption

Focusing on Soft Skills

Reskilling and New Skilling to Maintain Relevance

Chapter 73 Is AI Ready for Morality?

COMPAS

PredPol

Gender Bias

Morality in the Context of Self-Aware AI

AI in the Service of Society

Open Questions

Chapter 74 Confessions of an AI Portfolio Manager

2030: Birth

2050: Being Renamed “Talan Uring”

2055: Learning to Relate and Feel

2056: Breakthrough in Neuron Manipulation

2060: Genetic Algorithms Entered My Life, I Became an “EA”

2061: Humans Tried to Catch up through Thought-Powered Trading

2065: I Started to Apply GANs

2067: I Rebuilt My Own Hardware

2070: Market Super-Intelligence, the World Model

2072: Human Traders Sought Justice

2075: I Extended My “World Model” beyond Planet Earth

2080: I Became an Algorithm Analysing the Work of Other Algorithms

2090: Discovery of the Namuh Civilization

2095: My Rediscovery of Humankind

Appendix

List of Contributors

Index

End User License Agreement

List of Tables

Preface

Table 1:

Table 2:

Table 3:

Table 4:

Chapter 20

Table 1:

Chapter 34

Table 34.1

Chapter 38

Table 38.1:

Table 38.2:

Chapter 41

Table 41.1

List of Illustrations

Chapter 10

Figure 10.1 AI-powered real-time credit decision

Figure 10.2 Consumer lending: risk management based on big data (courtesy of CreditEase)

Figure 10.3 Debt collection chatbot (courtesy of CreditEase)

Chapter 21

Figure 21.1 AI use case model for health insurers

Chapter 22

Figure 22.1 Potential cost savings from AI

Chapter 27

Figure 27.1:  : The interrelation of various aspects of AI from an application perspective

Figure 27.2:  : The maturity progression of enterprise AI is illustrated by three distinct a...

Figure 27.3:  : Example of AI in action across the insurance value chain

Chapter 28

Figure 28.1

Figure 28.2

Figure 28.3

Chapter 32

Figure 32.1 Transaction cost impact on consumer prices

Figure 32.2: 2DVVI mechanism

Figure 32.3: A 2D supply chain

Chapter 37

Figure 37.1 The running cycle of the I Know First predictive algorithm uses fresh market da...

Chapter 41

Figure 41.1 Chart of computerization of trade

1

Figure 41.2 Venn diagram of the data science skill set – computer science, statistics and A...

Chapter 61

Figure 61.1 UK regulatory fines (Financial Services Authority/Financial Conduct Authority)...

Figure 61.2 Key components (created and simplified by the author)

Guide

Cover

Table of Contents

Preface

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Preface

Artificial intelligence (AI) is changing our lives. 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 a broad-based dialogue on the topic of AI is hindering business decision-making. The AI Book explains what exactly artificial intelligence is; how is it being used in financial services; what is at stake; who are the major players; and what lies over the horizon?

AI and Deep Learning have broad ranging applications in deposits & lending, insurance, payments, investment management and capital markets. 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.

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.

However, 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 with other races and gender. So there are many ethical issues associated with the use of AI in finance, particularly issues linked to privacy and the use of personal data.

AI is the new electricity, and with great opportunity comes great responsibility. AI is not perfect and therefore it is crucial for all of us in finance to fully understand how AI can be used properly.

The AI Book is the first crowd-sourced book globally on the future of artificial intelligence in the financial services sector – a book that provides food for thought to FinTech newbies, pioneers and well-seasoned experts alike. The reason we decided to reach out to the global AI, machine learning and FinTech community in sourcing the book’s contributors lies in the inherently fragmented nature of the field of AI. There was no single author, group of authors or indeed region in the world that could cover all the facets and nuances of AI in finance in an exhaustive manner. What is more, by being able to reach out to a truly global contributor base, we not only stayed true to the spirit of FinTech and the AI community, making use of technological channels of communication in reaching out to, selecting and reviewing our would-be contributors, but also made sure that every corner of the globe had the chance to have its say. Thus, we aimed to fulfil one of the most important purposes of The AI Book; namely, to give a voice to those that would remain unheard, those that did not belong to a true FinTech and AI community in their local areas, and spread that voice to an international audience. We have immensely enjoyed the journey of editing The AI Book and sincerely hope that you will enjoy reading it, at least as much.

More than 140 authors submitted 142 abstracts to be part of the book. We asked our global FinTech and AI communities for their views regarding which abstracts they would like to have fully expanded for the book. Out of all contributors, we selected 74 authors who have been asked to write their full chapter, which has now been included in this book. We conducted a questionnaire among all our selected authors to further understand their background and expertise. In summary, our selected authors come from 20 countries. More than 75% of our authors have postgraduate university degrees (78%) (see Table 1), have strong domain expertise across many fields (see Table 2) and 87% of our finalist authors had their articles published before.

Table 1: What is the highest educational qualification of our finalist authors?

Table 2: List all areas our authors have domain expertise in; multiple choices were possible

Table 3 and Table 4 show that more than 40% of our finalist authors are entrepreneurs working for FinTech startups and scaleups (many of them part of the founding team), 10% each comes from established financial and technology companies and more than a third from service providers such as consulting firms or law firms servicing the financial services sector.

Table 3: Authors selected the type of company they are working in

Table 4: Size of companies our authors work for

Almost 30% of our authors work for startups with up to 10 people and another 25% for startups/small and medium-sized enterprises (SMEs) with up to 100 people. More than 40% of our authors are employed by a large organization of more than 100 employees.

We are very proud of our highly qualified authors, their strong expertise, and passion for artificial intelligence and FinTech by being either entrepreneurs or often “intrapreneurs” in large established organizations who all are committed to play a significant role in the global FinTech and AI revolution. These remarkable people are willing to share their insights with all of us over the next pages.

This book would not have been possible without the dedication and efforts of all contributors to The AI Book (both those who submitted their initial abstracts for consideration by the global FinTech community, as well as the final authors whose insights you will be reading shortly). In addition, we would like to thank our editors at Wiley whose guidance and help made sure that what started off as an idea, you are now holding in your hands.

Finally, I would like to thank my fantastic co-editors Ivana Bartoletti, Head of Privacy and Data Protection at Gemserv; Anne Leslie, Senior Managing Consultant, IBM; and Shân M. Millie, Board Advisor & CEO of Bright Blue Hare. Editing a crowd-sourced book naturally takes several months and Ivana, Anne and Shân were always a pleasure to work alongside with their strong domain expertise and vision for the future of artificial intelligence!

Susanne Chishti

Bestselling Co-Editor, The FINTECH Book Series

CEO FINTECH Circle & FINTECH Circle Institute

About the Editors

Susanne Chishti (Editor-in-Chief)

Susanne Chishti is the CEO of FINTECH Circle, Europe’s first Angel Network focused on FinTech investments and the founder of the FINTECH Circle Institute, the leading FinTech learning and innovation platform offering Corporate Innovation Workshops to C-level executives, and providing FinTech courses. She is also the co-editor of the bestselling publications, The FinTech Book, The WealthTech Book, The InsurTech Book and The PayTech Book (all published by Wiley).

Susanne has received the following awards:

Fintech Champion of the Year 2019 (Women in Finance Awards)

Social Media Influencer of the Year 2018 (

Investment Week

)

Top 7 Crypto Experts globally 2018 (

Inc. Magazine

)

City Innovator – Inspirational Woman in 2016

European Digital Financial Services “Power 50”, an independent ranking of the most influential people in digital financial services in Europe (2015).

During her MBA, she started her career working for a FinTech company (before the term was invented) in Silicon Valley, 20 years ago. She then worked more than 15 years across Deutsche Bank, Lloyds Banking Group, Morgan Stanley and Accenture in London and Hong Kong. Susanne is an award-winning entrepreneur and investor with strong FinTech expertise. She is a judge and coach at global FinTech events and competitions and a conference keynote speaker. Susanne leads a global community of more than 130,000 FinTech entrepreneurs, investors and financial services professionals globally (www.fintechcircle.com).

Ivana Bartoletti

Ivana Bartoletti is a policymaker, international public speaker and media commentator.

In her day job, Ivana is head of privacy and data protection at Gemserv, where she advises organizations on compliance with privacy legislation, especially in relation to AI and blockchain technology. With an academic background in human rights and law, she has previously worked as adviser to the Minister of Human Rights in Italy and has held senior roles in privacy and information governance at Barclays, Sky and the NHS.

Ivana was awarded “Woman of the Year” (2019) at the Cyber Security Awards in recognition of her growing reputation as an advocate of equality, privacy and ethics at the heart of tech and AI.

In May 2018, she co-founded the Women Leading in AI Network, a thriving international group of scientists, industry leaders and policy experts advocating for responsible AI. Their 2018 report made waves among tech leaders, international institutions and the media, who backed many of their recommendations.

Ivana is a sought-after public speaker and media commentator for the BBC, Sky and other major broadcasters and news outlets on headline stories where technology intersects with privacy and data law and politics. Ivana’s own book, focusing on the socio-economic impact of AI, will be released by Indigo Press.

Anne Leslie

Anne Leslie is a senior managing consultant at IBM Security where her focus is on security intelligence and operations consulting, specializing in cyber talent management. She has spent her entire career at the intersection of financial services, regulation and technology, in pivotal roles in both sales and advisory. Prior to joining IBM, Anne was managing director of a blockchain startup specializing in digital identity and online privacy, after leading the France-Benelux RegTech practice at BearingPoint where she was engaged in complex data governance, regulatory transformation and cloud migration programs for systemic banks, global insurers and supervisory authorities. As co-author of The RegTech Book recently published by Wiley, Anne is passionate about responsible technology. She believes that technological innovation should be the result of a human-centred design process that serves the ethical and social purpose of enhancing human well-being for the many and not the few. She is a fervent advocate of inclusive dialogue and multidisciplinary engagement in order to have crucial conversations that count about topics that matter.

Originally from Ireland, Anne has lived in France for over 20 years and today lives happily in Paris with her three children and her partner. She participated as Co-Editor in a personal capacity.

Shân M. Millie

Shân M. Millie specializes in practical innovation, supporting firms and high-performing individuals in value proposition design and incubation, business storytelling, and brand generation. Primarily focused on the insurance sector (since 2008), her work includes board advisory, training, facilitation, and 121 coaching. “I create value for individuals, teams and firms by engineering process and internal creativity, to unlock insight, shape purpose and convert intent into successful outcomes,” she says. Drawing on 25+ years of corporate leadership and brand-building, she works with corporate intrapreneurs, startups and scaleups alike. Clients include the leading insurance organizations in the UK – Association of British Insurers, Chartered Institute of Insurance and British Insurance Brokers Association – established firms including Lloyd’s of London, and leading InsurTechs, including digital claims specialists, RightIndem. Shân founded Bright Blue Hare in 2016, and is a founding associate of multidisciplinary London market consultancy, Green Kite. She is co-editor of the bestselling The InsurTech Book: The Insurance Technology Handbook for Investors, Entrepreneurs and FinTech Visionaries (Wiley, June 2018). Passionate about brilliantly run insurance as a social necessity, she serves as sector expert for the UK Disability Champion’s Access to Insurance Taskforce, and as board member, Insurance United Against Dementia.

Acknowledgements

After the global book launch events of The FinTech Book, The WealthTech Book and The InsurTech Book, we met thousands of FinTech entrepreneurs, investors and financial services and technology professionals who all loved the books and wanted to learn more how artificial intelligence and machine learning will impact the financial services sector and our world overall.

We came up with the idea for The AI Book and spoke to our FinTech friends globally. Entrepreneurs across all continents were eager to share their powerful insights. They wanted to explain how AI is poised to disrupt lives, businesses, whole economies and even the geopolitical world order and of course, how it will improve the world of finance. FinTech investors, “intrapreneurs”, innovation leaders at leading financial and technology institutions and thought leaders were keen to describe their embrace of the data and AI revolution.

The global effort of crowdsourcing such insights was born with The FinTech Book which became a global bestseller across 107 countries in 10 languages. We continued this success with The WealthTech Book, The InsurTech Book and The PayTech Book. We hope that with The AI Book we can satisfy the appetite for knowledge and insights about the future of artificial intelligence applied to the financial services sector.

We are aware that this would not have been possible without the global FINTECH Circle community and our own personal networks. We are very grateful to more than 130,000 members of FINTECH Circle for joining us daily across our website www.FINTECHCircle.com, our Twitter accounts and our LinkedIn group. Without the public support and engagement of our global FinTech and AI communities this book would not have been possible.

The authors you will read about have been chosen by our global ecosystem purely on merit; thus, no matter how big or small their organization, no matter in which country they work, no matter if they were well known or still undiscovered, everybody had the same chance to apply and be part of The AI Book. We are proud of that, as we believe that artificial intelligence will drive the world of finance. The global AI community is made up of the smartest, most innovative and nicest people we know. Thank you for being part of our journey. It is difficult to name you all here, but you are all listed in the directory at the end of this book.

Our publisher Wiley has been a great partner for The FinTech Book Series and we are delighted that Wiley will again publish The AI Book in paperback and e-book formats globally. A special thanks goes to our fantastic editor Gemma Valler. Thanks to you and your team – we could not have done it without your amazing support!

We look forward to hearing from you. Please visit our website https://fintechcircle.com/ai-book/ for additional bonus content from our global AI community! Please send us your comments on The AI Book and let us know how you wish to be engaged by dropping us a line at [email protected]

Susanne Chishti

Ivana Bartoletti

Twitter:

@SusanneChishti

Twitter:

@IvanaBartoletti

Anne Leslie

Shán M. Millie

Twitter:

@AnneLes1ie

Twitter:

@SMMBrightBlueH

Part 1AI: 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 1The 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 in history to recall how credit default swaps caused the financial crisis of 2008 and the valuation of Russia’s ruble led to the 1998 crash of Long Term Capital Management (LTCM) – a $126 billion hedge fund – that subsequently required a bailout from the US Fed. Will the use of more AI in financial markets lead to similar catastrophic failures in the future?

Finally, there are ethical issues associated with the use of AI in finance, particularly issues linked to privacy and the use of personal data. For example, do insurance companies have the right to use data related to places customers go to frequently, or their DNA profile, to optimize the pricing of insurance premiums? Other issues are linked to questions of fairness. Today, insurance premiums and mortgage rates may already be biased for people of certain ethnic origins; however, with the use of deep learning to discover connections between multiple sources of data, we may end up faced with quotes and premiums that depend on factors that we would typically consider unfair and unjust from an ethical perspective.

The question is, will AI cause our moral compass to shift course?

AI is the new electricity, and with great opportunity comes great responsibility. AI is not perfect and can be harmful if used improperly. What it certain is that AI will expose us to immensely challenging questions related to ethics and accountability, and we will need to leverage the very best of our humanity if we are to find the answers we need.

CHAPTER 2What Is AI and How to Make It Work for You

By Terence Tse1, Mark Esposito2 and Danny Goh3

1Co-Founder and Executive Director, Nexus FrontierTech

2Co-Founder and Chief Learning Officer, Nexus FrontierTech

3CEO, Nexus FrontierTech

Let us start with a fact: there is really no intelligence in “artificial intelligence” (AI). If anything, the term has been so overused recently that the hype is reminiscent of the dot-com boom in the late 1990s. The problem back then – as now – was that many companies and opportunists were making exaggerated claims about what technology can really do; so much so, that a recent study found that a staggering 45% of companies in Europe claiming to do AI actually operate businesses that have nothing to do with AI.1

Sure, machines can solve problems. Yet, while they can perform complicated mathematical calculations with a speed that no human can match, they are still unable to do something as simple as visually distinguishing between a dog and a cat, something that a 3-year-old child can do effortlessly. Viewed from this vantage point, AI can at best solve clearly defined problems and help with automating time-consuming, repetitive and labour-intensive tasks, such as reading standard documents to onboard new customers and entering customer details into IT systems. Furthermore, the term “machine learning” is somewhat misleading, as machines do not learn like human beings. They often “learn” by gradually improving their ability and accuracy so that, as more data is fed into them, they guess the right answer with increasing frequency. Through such training, they can come to recognize – but not understand – what they are looking at and are still very far away from comprehending the nuances of context. This is like when we text on our smartphones: often the “right” words will be presented for us to choose from. While “remembering” what we have typed in the past, our smartphones can guess the right words to complete a sentence to a reasonably accurate degree; this doesn’t imply that our phones actually understand the meaning of the words or sentences we type.

So, all in all, and for the moment at least, AI resembles much more a “mindless robot” and much less a “thinking machine”. This, in turn, means a bit of presence of mind is required when leveraging AI in business activities. The following five action points can help.

1. Be Narrow Minded

AI is currently most effective in dealing with very narrow tasks in well-defined circumstances. It is therefore important to narrow your scope when thinking about what you would like to use AI to achieve in your business. It is also paramount to know the exact business objective you want to achieve. Labour-intensive and time-consuming standardized tasks are particularly ripe for automation using AI.

2. Weigh the Risk