The AI Book

Book description

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




Table of contents

  1. Cover
  2. Preface
  3. About the Editors
  4. Acknowledgements
  5. Part 1 AI: Need to Know
    1. Chapter 1 The Future of AI in Finance
      1. The Promise of Deep Learning
      2. Business Applications in Finance
      3. Time for a Reality Check
      4. Safeguards and Systemic Risk
    2. Chapter 2 What Is AI and How to Make It Work for You
      1. 1. Be Narrow Minded
      2. 2. Weigh the Risk
      3. 3. Get the “Last Mile” Right
      4. 4. Consider That Less Data May Mean More
      5. 5. Do Your Homework
      6. Note
    3. Chapter 3 Getting to Day Zero: Let’s Get the Foundation Right
      1. Challenge 1: A House Built on Sand
      2. Challenge 2: The Digital Transformation Dilemma
      3. Opportunity 1: Share Your Data with the World
      4. Opportunity 2: The Alternative Data Revolution
      5. A Bright Future
      6. Notes
    4. Chapter 4 Navigating a Sea of Information, News and Opinion with Augmented Human Intelligence
      1. Making Sense out of Complex Text through Natural Language Processing (NLP)
      2. Ontologies Link Entities and Thus Create Valuable Connections
      3. How Augmented Human Intelligence Will Change the Way We Read News and Inform Ourselves
      4. Note
    5. Chapter 5 The Seven Deadly Sins of AI
      1. Data
      2. Research Failure
      3. Bias
      4. Explainability
      5. Emotion
      6. Ethics
      7. Organizational Readiness
      8. Conclusion
    6. Chapter 6 A New Internet, Data Banks and Digital World War
      1. The Future of Artificial Intelligence
      2. Reinventing How We Invent
      3. AI Neural Network
      4. The Human API and Digital War: World War III
    7. Chapter 7 AI: A Cross Country Analysis of China versus the West
      1. Notes
    8. Chapter 8 The AI Advantage: Near-Term Workforce Opportunities and Challenges
      1. Backdrop
      2. Enhanced Cognition: The Good News
      3. Macro-Trend Analysis of Workforce Challenges
      4. Pragmatic Suggestions for a Way Forward
      5. Conclusion
      6. Notes
    9. Chapter 9 The Art of Involving Boards in Embracing AI
      1. The Art and Science of Board Dynamics
      2. The Challenge of AI for Boards
      3. Work with Strength-Based Management Techniques
      4. Design and Provide Special Educational Programmes for Board Members
      5. Dare to Change the Composition of the Board
      6. Establish an AI Council
      7. Create a Communication Campaign
      8. Note
  6. Part 2 Deposits and Lending
    1. Chapter 10 AI in Lending
      1. Overview
      2. User Identification
      3. Credit Decisions
      4. Fraud Prevention
      5. Consumer Lending: Proprietary Risk Management Based on Big Data
      6. Credit Decision Powered by Knowledge Graph
      7. SME Lending: Unsecured Loans Backed by AI and Machine Learning
      8. Chatbots Used in Debt Collection
    2. Chapter 11 Financial Technology and China’s Inclusive Finance
    3. Chapter 12 The Future of Deposits and Lending
      1. Value Stores
      2. Future of Deposits
      3. Access to Credit
      4. Changing Expectations
      5. Data Deluge
      6. Future of Lending
    4. Chapter 13 Applications of AI in Deposits and Lending
      1. Alternative Data
      2. Value Chain
      3. Origination and Onboarding
      4. Underwriting
      5. Financing and Contract
      6. Servicing and Payment Collections
      7. Imagine!
      8. How Much Data Is Enough?
      9. How Much Information Do You Store About Your Customers?
      10. Is What You Are Doing Transparent, Ethical and Fair?
      11. Last Word
    5. Chapter 14 Showcase and Customer Service: Leveraging Chatbots in the Banking Industry
      1. Brief History
      2. The Expansion of Chatbots
      3. Banking Applications
      4. Non-Banking Services
      5. Money Matters
      6. Information and Banking Operations
      7. Financial Coaching
      8. Conclusion
      9. Note
    6. Chapter 15 The Power of AI to Transform the Global SME Credit Landscape
      1. Identifying More Creditworthy SMEs
      2. Speed Is of the Essence
      3. Problem Solving, Sector by Sector
      4. The Power of AI to Shift Capital
      5. Note
    7. Chapter 16 Using AI for Credit Assessment in Underserved Segments
      1. Note
    8. Chapter 17 Why Video Games Might Help You Buy Your First House
      1. The Problem
      2. How Do We Bridge the Gap?
    9. Chapter 18 AI Opportunities in the African Financial Sector: Use Cases
      1. Two Significant Challenges: Financial Exclusion and Cybercrime
      2. The solutions widely used in African FinTech ecosystem
      3. AI at the Heart of Innovation FinTech Solutions
      4. The Use of Chatbots
      5. Looking Ahead
      6. Notes
  7. Part 3 Insurance
    1. Chapter 19 Insurance and AI: Choices in Leadership, Purpose and Trust
      1. Insurance: Too Important Not to Lead on AI?
      2. Your Job as Leaders (1): Get the Reality Check
      3. Your Job as Leaders (2): Question the “Absolutes”
      4. Your Job as Leaders (3): Help the Firm Get Purposeful
      5. Your Job as Leaders (4): Bring AI Ethics to the Boardroom
      6. Final Thoughts: Architects of Exclusion — or Enablers of Protection?
      7. VERY Selected Further Reading
      8. Notes
    2. Chapter 20 Drifting into Algocratic Insurance?
      1. AI and Insurance – A Natural Partnership
      2. Insurance turning Algocratic
      3. Regulating the Algocracy
    3. Chapter 21 Moving the AI Needle: Strategies for Health Insurers to Put AI into Practice
      1. What’s the Problem with AI in Health Insurance?
      2. AI Use Cases That Can Move the Needle
      3. Level 1 – Standard AI Solutions
      4. Level 2 – Tailor-Made AI Solutions
      5. Level 3 – Explainable AI Solutions
      6. How to Get There – Strategic Imperatives
      7. Define Clear Use Cases and Demystify the Topic of AI
      8. Intelligently Integrate Partners Instead of Developing Everything Internally
      9. Focus on Solving Real Business Problems
      10. Value Patient Centricity and Gain Patients’ Trust by Ensuring Transparency
    4. Chapter 22 AI and Healthcare: Doctor Will FaceTime You Now!
      1. AI and Healthcare Now
      2. The Economic Opportunity
      3. What Is the Role of Health Insurers in Emerging Healthcare AI Business Models?
      4. Notes
    5. Chapter 23 Using Artificial Intelligence in Commercial Underwriting to Drive Productivity Growth
    6. Chapter 24 The Digitally-Enabled Underwriter: How AI is Transforming Commercial Insurance Underwriting
      1. Why AI, Why Now?
      2. A Deluge of Data, A Drought of Insights
      3. Use Cases for AI in Commercial Insurance Underwriting
      4. The Rise of the Digitally-Enabled Underwriter
      5. We Are Still at the Beginning
      6. Notes
    7. Chapter 25 Improving Policy Life Cycle Management with AI and Data Science
      1. AI-Supported Policy Life Cycle Management: Point of Sale
      2. Re-Scoring and Re-Evaluating the Initial Application after a Claim Has Been Submitted
      3. It’s Not Just about Fraud
    8. Chapter 26 Disrupting the Insurance Value Chain
      1. Products
      2. Product Management
      3. Customer Onboarding
      4. Underwriting
      5. Customer Services
      6. Claims and Settlement Management
    9. Chapter 27 Cutting to the Chase: Mapping AI to the Real-World Insurance Value Chain
      1. Enabling and Applying AI
      2. History vs Present
      3. Computer Vision
      4. Voice and NLP
      5. Internet of Things
      6. Conclusion
      7. Notes
  8. Part 4 Payments
    1. Chapter 28 Artificial Intelligence: The Next Leap Forward in the Payments Revolution
    2. Chapter 29 Frictionless Payments: If or When?
      1. Today’s Security Paradigms Will Not Suffice Tomorrow
      2. Invisible, Precise, Highly Robust Authentication
      3. Rethinking Authentication
      4. Note
    3. Chapter 30 Big Data, AI and Machine Learning: How to Unlock Their Potential in the New Payment Environment
      1. Payments, a Wealth of Data
      2. A Tool to Combat Fraud
      3. Smart Routing
      4. Getting to Know Your Customer
      5. Advanced Analytics for Merchants
    4. Chapter 31 The Rise of Conversational AI Platforms
      1. Towards Invisible Banking and Payments
      2. Notes
    5. Chapter 32 Two Dimensional Virtual Vertical Integration: Solving the Impossible SC Problem
      1. The Cost to the Economy
      2. How Do Current Practices Inflate Consumer Prices? An Illustration
      3. Introducing 2DVVI
      4. But It’s Not Quite so Simple
      5. The Social Dimension
      6. Notes
  9. Part 5 Investment and Wealth Management
    1. Chapter 33 The True Value of AI to Transform Push/Pull Wealth Management Offers
    2. Chapter 34 Machine Learning in Digital Wealth Management
      1. ML in Wealth Management
      2. Prospecting and Conversion, Onboarding and Screening
      3. Client Onboarding and Screening
      4. Product Recommendations and Onboarding
      5. Advisory Process
      6. Investment Research and Trading
      7. Client Attrition
      8. Summary of Different Algorithms and Use Cases for Wealth Management
      9. Data Sharing and Confidentiality
      10. Federated Learning
    3. Chapter 35 The Impact of AI on Environmental, Social and Governance (ESG) Investing: Implications for the Investment Value Chain
      1. Introduction
      2. The Impact of AI on ESG
      3. Mastering the Data Complexity Challenge with AI
      4. Engaging The Investor Community to Address AI Concerns
      5. Collaboration and Engagement
      6. Conclusion
    4. Chapter 36 AI in Indian Investment and Asset Management: Global Perspective
      1. Inherent Issues in India
      2. AI in Investment Management in India
      3. Some New Scenarios
      4. Emergence of New Business Models
      5. Reference
    5. Chapter 37 Finding Order in the Chaos: Investment Selection Using AI
      1. Random Walk Through Efficient Markets: Are Stock Price Fluctuations Predictable?
      2. Bulls, Bears and Butterflies: Markets as Chaotic Systems
      3. Best of Both Worlds: Investing With AI-Driven Decision Enhancement Tools
      4. Predictive Algorithm Developed by I Know First
    6. Chapter 38 Dispelling the Illusion
      1. Data-Based Automation
      2. Front, Middle and/or Back Office?
      3. Implementation Strategy
      4. Implications
      5. Notes
    7. Chapter 39 ETF 2.0: Mega Block Chains with AI
    8. Chapter 40 Fear and Greed
      1. Man vs Machine
      2. Quantum Computing
      3. Convergence of Advanced Technologies
      4. Automated Trading
      5. Wealth Creation by Algorithm
      6. The Financial World of Equals
  10. Part 6 Capital Markets
    1. Chapter 41 Introduction on AI Approaches in Capital Markets
      1. Setting the Scene
      2. What Is Artificial Intelligence?
      3. Using Data Science to Solve Business Problems
      4. Capital Markets Use Cases
      5. Trust, Transparency, and Human Interactions
      6. State of the Art: Selected Highlights 2018/19
      7. Where Next?
      8. Notes
    2. Chapter 42 AI, Machine Learning and the Financial Service Industry: A Primer
      1. Defining Artificial Intelligence
      2. Machine Learning (ML) and Deep Learning (DL) within Finance
      3. Barriers — and the Goldilocks Rule
      4. Notes
    3. Chapter 43 Compliance as an Outcome
      1. Simple Heuristics Lead Human Behaviours
      2. Prevention through Deterrence
      3. Data and AI Strategy
      4. Intelligent Empowerment
      5. Compliance and Business Benefits?
      6. Further reading
      7. Notes
    4. Chapter 44 Alternative Data and MetaQuants: Making the Most of Artificial Intelligence for Visionaries in Capital Markets
      1. Back to Basics: What Is Alt-Data?
      2. Redefining Market Players – The MetaQuant Approach
      3. Can a Hybrid Model (Quantamental + MetaQuant) Boost Investment Results?
      4. Notes
    5. Chapter 45 AI and Capital Markets: Where to Now?
      1. Organizational Efficiency — Inside and Out
      2. Regulatory Developments
      3. Future Enablers
  11. Part 7 Trust, Transparency and Ethics
    1. Chapter 46 Trust in FinTech and AI: Some Introductory Reflections
      1. Tech That Has Legitimacy with a Social Licence
      2. Ethical Innovation in Finance
      3. Clarity of Ethical Purpose and Mission Is Central
      4. Regulation Introduced Clarity and Wider Support for Innovation
      5. Four Ways to Support More Trustworthy, Ethical Innovation in the Financial Services Sector
    2. Chapter 47 Building Trust through Sound Governance
      1. Ethical Challenges for Firms
      2. Ethical Governance
      3. Conclusion
      4. Notes
    3. Chapter 48 Independent AI Ethics Committees and ESG Corporate Reporting on AI as Emerging Corporate and AI Governance Trends
      1. Independent Human Research Review Committees (IHRCs)
      2. World’s First Corporate AI-Focused IHRCs
      3. Axon
      4. Corporate ESG Reporting on AI as a New Paradigm?
      5. Notes
    4. Chapter 49 The Wisdom Vantage
      1. Explainability and Transparency
      2. The Future
      3. Wisdom
    5. Chapter 50 AI and Business Ethics in Financial Markets
      1. Fairness
      2. Privacy
      3. Transparency
      4. Explainability
      5. Accountability
      6. Conclusion
      7. Notes
    6. Chapter 51 AI Trust, Ethics, Transparency and Enablement
      1. What Is Intelligent Empowerment and Why Is it Topical?
      2. The FS AI/ML Trust Issue
      3. TETE Proposal
      4. The TETE Need and Challenges
      5. How to Implement a TETE Framework
      6. Conclusion
      7. Bibliography
    7. Chapter 52 Invisible Hand, Spontaneous Order and Artificial Intelligence
    8. Chapter 53 Transforming Black Box AI in the Finance Industry: Explainable AI that Is Intuitive and Prescriptive
      1. The Challenges Hindering Wider AI Implementation
      2. How to Identify an Explainable Algorithm
      3. Unlocking a New Level of Explainability with Prescriptive AI
      4. Industry Use Cases and Compelling Results
    9. Chapter 54 Making Data Your Most Valuable Asset
      1. Why Do We Need Data Ethics Now?
      2. Treating Data as the Asset
      3. Consequences of Data Mistrust
    10. Chapter 55 The Data Promise
      1. The Client Data Promise
      2. Example from the Wealth Management World
      3. Notes
  12. Part 8 Legal Risk and Regulation
    1. Chapter 56 AI and the Law: Challenges and Risks for the Financial Services Sector
      1. Sci-Fi — or Real Life?
      2. Racial Bias — the Tip of the Iceberg?
      3. Legal and Ethical Issues for Your Watch List
      4. The GDPR – a Deeper Dive on Key Data Principles
      5. Self-Regulation – A Viable Strategy?
      6. Notes
    2. Chapter 57 Algorithm Assurance
    3. Chapter 58 Regulation of AI within the Financial Services Sector
      1. The Need for Regulation
      2. Common Technical Standards
      3. Regulatory Measures
      4. Questions of Liability
      5. Future Regulation
      6. Note
    4. Chapter 59 Is Risk-Based Regulation the Most Efficient Strategy to Rule the Unknown Risks Brought by FinTech?
      1. Notes
    5. Chapter 60 The Changing Face of Regulatory, Compliance and Audit
      1. Why We Need Compliance and Audit
      2. Identification of Risks
      3. A Vision for Tomorrow
      4. Conclusion
    6. Chapter 61 Robocop on Wall Street
      1. Setting the Scene – The Why
      2. Mapping out the RegTech and Legal Risk AI Landscape (The What)
      3. Key Building Blocks of AI Solutions Addressing Legal Risk and Regulation (The How)
      4. Judgement and Liability
      5. Notes
    7. Chapter 62 Sure, AI Can Answer Our Questions – But Who Will Answer Our Questions About AI?
      1. Notes
    8. Chapter 63 Technology for Regulations and Compliance: Fit4Future!
      1. Evolution of RegTech
      2. RegTech 3.0: Phases of Development
      3. Conclusion
  13. Part 9 The Future of AI in Finance
    1. Chapter 64 Welcome to the Future
      1. The Evolving Technology Landscape
      2. Beyond Digital Transformation: Thinking Like a Digital Native
      3. Innovation at the Speed of Thought
      4. A Potentially Utopian Future
    2. Chapter 65 An AI-Embedded Financial Future
      1. Job Displacement
      2. Betting the House on AI
      3. The Spending Conundrum
      4. Intelligence, Employment and Social Purpose
      5. Conclusion
      6. Notes
    3. Chapter 66 Open Banking, Blockchain and AI: The Building Blocks for Web 3.0
      1. Genesis
      2. Data Marketplace for the People
      3. The Inversion
      4. Conclusion
    4. Chapter 67 Automated Machine Learning and Federated Learning
      1. Introduction
      2. Shortage of Data
      3. Lack of Trust in AI
      4. Shortage of Qualified Personnel
      5. Federated Learning
      6. Notes
    5. Chapter 68 Deep Learning and Financial Regulation
      1. What Do Regulators Do?
      2. Endless Financial Crises…
      3. …That Are Getting Worse
      4. Autonomous Regulatory Agents to the Rescue
      5. We Created This Mess – We Can Fix It!
    6. Chapter 69 AI for Development and Prosperity
      1. Natural Disasters
      2. Capital Markets
      3. AI for Diversity and Inclusion
      4. Conclusion
      5. Notes
    7. Chapter 70 The AI Trends That Will Shape Winning Businesses
      1. Natural Language Understanding
      2. Multi-Language
      3. Human Personality and Emotional Understanding
      4. Support Process Optimization
      5. Sales Process Optimization
      6. Is China Coming?
      7. Looking Forward
    8. Chapter 71 Mastering the AI Talent Transformation: Present and Future
      1. The AI-Prediction Debate: Technology Anxiety and AI, Is This Time Different?
      2. But, Will AI Eliminate All Jobs in the Future?
      3. From the Future of Work Debate to the Wealth Distribution and Inequality Problem
      4. Is Our Future Preordained by AI Prediction Capabilities?
      5. The AI-in-Practice Challenge: Narrow AI and Its Implementation Challenge
      6. AI and Human Strengths and Weaknesses
      7. The Need for an AI–Human Collaboration Approach
      8. Notes
    9. Chapter 72 Humans versus Machines: Who Will Still Have a Job in 50 Years?
      1. The New Normal
      2. A New Type of Leadership
      3. Workforce Disruption
      4. Focusing on Soft Skills
      5. Reskilling and New Skilling to Maintain Relevance
    10. Chapter 73 Is AI Ready for Morality?
      1. COMPAS
      2. PredPol
      3. Gender Bias
      4. Morality in the Context of Self-Aware AI
      5. AI in the Service of Society
      6. Open Questions
    11. Chapter 74 Confessions of an AI Portfolio Manager
      1. 2030: Birth
      2. 2050: Being Renamed “Talan Uring”
      3. 2055: Learning to Relate and Feel
      4. 2056: Breakthrough in Neuron Manipulation
      5. 2060: Genetic Algorithms Entered My Life, I Became an “EA”
      6. 2061: Humans Tried to Catch up through Thought-Powered Trading
      7. 2065: I Started to Apply GANs
      8. 2067: I Rebuilt My Own Hardware
      9. 2070: Market Super-Intelligence, the World Model
      10. 2072: Human Traders Sought Justice
      11. 2075: I Extended My “World Model” beyond Planet Earth
      12. 2080: I Became an Algorithm Analysing the Work of Other Algorithms
      13. 2090: Discovery of the Namuh Civilization
      14. 2095: My Rediscovery of Humankind
  14. Appendix
  15. List of Contributors
  16. Index
  17. End User License Agreement

Product information

  • Title: The AI Book
  • Author(s): Susanne Chishti, Ivana Bartoletti, Anne Leslie, Shân M. Millie
  • Release date: June 2020
  • Publisher(s): Wiley
  • ISBN: 9781119551904