Artificial Intelligence for Asset Management and Investment: A Strategic Perspective

Make AI technology the backbone of your organization to compete in the Fintech era The rise of artificial intelligence is nothing short of a technological revolution. AI is poised to completely transform asset management and investment banking, yet its current application within the financial sector is limited and fragmented. Existing AI implementations tend to solve very narrow business issues, rather than serving as a powerful tech framework for next-generation finance. Artificial Intelligence for Asset Management and Investment provides a strategic viewpoint on how AI can be comprehensively integrated within investment finance, leading to evolved performance in compliance, management, customer service, and beyond. No other book on the market takes such a wide-ranging approach to using AI in asset management. With this guide, you’ll be able to build an asset management firm from the ground up—or revolutionize your existing firm—using artificial intelligence as the cornerstone and foundation. This is a must, because AI is quickly growing to be the single competitive factor for financial firms. With better AI comes better results. If you aren’t integrating AI in the strategic DNA of your firm, you’re at risk of being left behind. See how artificial intelligence can form the cornerstone of an integrated, strategic asset management framework Learn how to build AI into your organization to remain competitive in the world of Fintech Go beyond siloed AI implementations to reap even greater benefits Understand and overcome the governance and leadership challenges inherent in AI strategy Until now, it has been prohibitively difficult to map the high-tech world of AI onto complex and ever-changing financial markets. Artificial Intelligence for Asset Management and Investment makes this difficulty a thing of the past, providing you with a professional and accessible framework for setting up and running artificial intelligence in your financial operations.

Table of Contents:
Preface xv Acknowledgments xxi Chapter 1: AI in Investment Management 1 What about AI Suppliers? 5 Listening without Judging 6 The Four Stages of AI in Investments 9 The Core Model of AIAI 14 Your Journey through This Book 16 How to Read and Apply this Book? 16 References 17 Chapter 2: AI and Business Strategy 19 Why Strategy? The Red Button 19 AI—a Revolution of its Own 21 Intelligence as a Competitive Advantage 22 Intelligence as a Competitive Advantage and Various Strategy Schools 23 The Intelligence School 25 Intelligence and Actions 26 Actions 27 Automation 28 Intelligence Action Chain and Sequence 28 Enterprise Software 29 Data 29 Competitive Advantage 30 Business Capabilities 31 Chapter 3: Design 35 Who Is Responsible for Design? 36 Introduction to Design 36 AI as a Competitive Advantage 38 The Ten Elements of Design 40 1. Design Your Business Model 41 2. Set Goals for the Entire Firm 44 3. Specify Objectives for Automation and Intelligence 45 4. Design Work Task Frames Based on Human-Computer Interaction 45 5. Perform a DTC (Do, Think, Create) Analysis 46 6. Create a SADAL Framework 47 7. Deploy a Feedback System and Define Performance Measures 49 8. Determine the Business Case or Value 49 9. Analyze Risks 50 10. Develop a Governance Plan 50 Some Additional Ideas about Designing Intellectualization 50 Summary of the Design Process 51 References 52 Chapter 4: Data 53 Who Is Responsible for the Data Capability? 53 Data and Machine Learning 55 Raw Data 55 Structured vs. Unstructured Data 56 Data Used in Investments 57 Data Management Function for the AI Era 58 Step 1: Data Needs Assessment (DNA) 59 Step 2: Perform Strategic Data Planning 59 Step 3: Know the Sensors and Sources (Identify Gaps) 61 Step 4: Procure and Understand the Supply Base 61 Step 5: Understand the Data Type (Signals) 62 Step 6: Organize Data for Usability 62 Step 7: Architect Data 63 Step 8: Ensure Data Quality 63 Step 9: Data Storage and Warehousing 63 Step 10: Excel in Data Security and Privacy 63 Step 11: Implement Data for AI 64 Step 12: Provide Investment Specialization 65 About Legacy Data Management 66 References 67 Chapter 5: Model Development 69 Who Is Responsible? 69 High-Level Process 70 Models 73 The Power of Patterns 74 Techniques of Learning 75 What Is Machine Learning? 76 Scientific Process on Steroids 79 The Learning Machines 79 Algorithms 80 Supervised Learning 82 Supervised: Classification 85 Classification: Random Forest 86 Classification: Using Mathematical Functions 87 Classification: Simple Linear Classifier 88 Supervised: Support Vector Machine 91 Classification: Naive Bayes 94 Classification: Bayesian Belief Networks 95 Classification: k-Nearest Neighbor 95 Supervised: Regression 96 Supervised: Multidimensional Regression 99 Unsupervised Learning 100 Neural Networks 103 Reinforcement Learning 106 References 107 Chapter 6: Evaluation 109 Who Performs the Evaluation? 109 Problems 111 Making the Model Work 111 Overfitting and Underfitting 113 Scale and Machine Learning 113 New Methods 114 Bias and Variance 115 Backtesting 116 Backtesting Protocol 119 References 121 Chapter 7: Deployment 123 Reference Architecture 127 The Reference Architecture and Hardware 130 References 131 Chapter 8: Performance 133 Who Is Responsible for Performance? 134 What Are the Work Processes of Performance? 134 Business Performance 136 Technological Performance 138 References 141 Chapter 9: A New Beginning 143 Building an Investment Management Firm Around Artificial Intelligence? 144 The Fallacy of Going Digital 145 Why Build Your Firm Around AI? 148 You Must Rely on Your Own Capabilities 149 What Is Asset Science? 150 A Healthy Cycle 154 The Tool Set 155 This Is Not Just Automation 156 References 157 Chapter 10: Customer Experience Science 159 Customer Experience 159 Value, Strength, and Duration of Relationship 160 Understanding Customers: Empathy for CX 161 Steps to Become an Empathetic Asset Management Firm 162 Know Your Empmeter 162 Expand Empathy Awareness and Understanding 163 Incorporate into Products and Services 163 What Is Automated Empathy and Compassion (AEC)? 163 Incorporating AEC Marketing 165 References 168 Chapter 11: Marketing Science 171 Who Undertakes This Responsibility? 171 How to Apply AI for Marketing 172 Begin with Assessment 172 Know Your Data 174 The AI Plan for Asset Management Marketing 176 Perform Strategic Planning 176 Manage Product Portfolio with AI 179 Transform Your Communications 180 Build Relationships 181 Execute with Excellence 181 References 182 Chapter 12: Land that Institutional Investor with AI 183 Who Is Responsible for IRMS Automation? 183 Is IRMS Your CRM System? 184 Know Thyself: Automated Self-Discovery 184 Automated Asset Class Analysis 185 Automated Institutional Analysis 185 Automated Structure and Terms Analysis 186 Automated Fee Analysis 186 Automated Communications 186 Unleash the Power of Knowing 188 Chapter 13: Sales Science 189 What Is Sales Science? 189 Who Is Responsible for Implementing Sales Science? 190 Are You Driving This in Sales? 190 How to Build Your AI-Based Sales System 193 References 195 Chapter 14: Investment: Managing the Returns Loop 197 Who Is Responsible for Investment Management? 197 How to Approach Building the New-Era Investment Function? 198 The Core Tool Set 204 What Will Be the Function of Your Investment Lab? 206 Make the Decisions 206 A New World 207 The (Unnecessary) Debate 208 More Behaviors 208 Research and Investment Strategy 209 Portfolio 210 Performance 210 References 210 Chapter 15: Regulatory Compliance and Operations 213 Who Is Responsible? 213 Regulatory Compliance 213 Why Intelligent Automation? 214 Have You Scoped Out What to Do? 215 How to Do It? 215 How to Use Technology for GIPS Implementation? 217 Back and Middle Office 219 Chapter 16: Supply Chain Science 221 Who Is Responsible for Supply Chain Science? 221 How to Think about Supply Chains 222 References 225 Chapter 17: Corporate Social Responsibility 227 CSR Woes: Can Processes Explain Them? 227 What Are the Criticisms of CSR? 228 Measurement Issues 228 Behavioral and Role Issues 230 Strategic and Organizational Issues 230 How to Apply AI in CSR? 231 CSR Must Not Be Forgotten 232 ESG Investment 232 How Can AI Help? 234 You Must Avoid These Mistakes 236 Summary Steps 236 References 237 Chapter 18: AI Organization and Project Management 241 The New Asset Management Organization 241 Why a CAIO/COO Role? 243 What Is Changing? 244 How to Get There? 244 Issues of the New Organization 246 Change Management 248 Managing AI Projects 249 References 250 Chapter 19: Governance and Ethics 251 Corporate Governance with AI 251 Governance of AI 257 Framing the Ethical Problems from a Pragmatic Viewpoint 261 Some Obvious Ethical Issues 262 Humans and AI 262 Ethics Charter 263 References 264 Chapter 20: Adaptation and Emergence 267 The Revolution Is Real 268 Complex Adaptive Systems 270 Our Coronavirus Meltdown Prediction 271 Index 273