Artificial Intelligence for Asset Management and Investment

AL NAQVI is the CEO of the American Institute of Artificial Intelligence, where he designs and develops machine learning based finance products, teaches classes on applied AI, deep learning, and cognitive transformation, and leads the company strategy. He studies the application of deep learning to financial engineering, investment, and asset management. He is also the author of Artificial Intelligence for Audit, Forensic Accounting, and Valuation (Wiley).

• Preface xvAcknowledgments xxiChapter 1: AI in Investment Management 1What about AI Suppliers? 5Listening without Judging 6The Four Stages of AI in Investments 9The Core Model of AIAI 14Your Journey through This Book 16How to Read and Apply this Book? 16References 17Chapter 2: AI and Business Strategy 19Why Strategy? The Red Button 19AI—a Revolution of its Own 21Intelligence as a Competitive Advantage 22Intelligence as a Competitive Advantage and Various Strategy Schools 23The Intelligence School 25Intelligence and Actions 26Actions 27Automation 28Intelligence Action Chain and Sequence 28Enterprise Software 29Data 29Competitive Advantage 30Business Capabilities 31Chapter 3: Design 35Who Is Responsible for Design? 36Introduction to Design 36AI as a Competitive Advantage 38The Ten Elements of Design 401. Design Your Business Model 412. Set Goals for the Entire Firm 443. Specify Objectives for Automation and Intelligence 454. Design Work Task Frames Based on Human-Computer Interaction 455. Perform a DTC (Do, Think, Create) Analysis 466. Create a SADAL Framework 477. Deploy a Feedback System and Define Performance Measures 498. Determine the Business Case or Value 499. Analyze Risks 5010. Develop a Governance Plan 50Some Additional Ideas about Designing Intellectualization 50Summary of the Design Process 51References 52Chapter 4: Data 53Who Is Responsible for the Data Capability? 53Data and Machine Learning 55Raw Data 55Structured vs. Unstructured Data 56Data Used in Investments 57Data Management Function for the AI Era 58Step 1: Data Needs Assessment (DNA) 59Step 2: Perform Strategic Data Planning 59Step 3: Know the Sensors and Sources (Identify Gaps) 61Step 4: Procure and Understand the Supply Base 61Step 5: Understand the Data Type (Signals) 62Step 6: Organize Data for Usability 62Step 7: Architect Data 63Step 8: Ensure Data Quality 63Step 9: Data Storage and Warehousing 63Step 10: Excel in Data Security and Privacy 63Step 11: Implement Data for AI 64Step 12: Provide Investment Specialization 65About Legacy Data Management 66References 67Chapter 5: Model Development 69Who Is Responsible? 69High-Level Process 70Models 73The Power of Patterns 74Techniques of Learning 75What Is Machine Learning? 76Scientific Process on Steroids 79The Learning Machines 79Algorithms 80Supervised Learning 82Supervised: Classification 85Classification: Random Forest 86Classification: Using Mathematical Functions 87Classification: Simple Linear Classifier 88Supervised: Support Vector Machine 91Classification: Naive Bayes 94Classification: Bayesian Belief Networks 95Classification: k-Nearest Neighbor 95Supervised: Regression 96Supervised: Multidimensional Regression 99Unsupervised Learning 100Neural Networks 103Reinforcement Learning 106References 107Chapter 6: Evaluation 109Who Performs the Evaluation? 109Problems 111Making the Model Work 111Overfitting and Underfitting 113Scale and Machine Learning 113New Methods 114Bias and Variance 115Backtesting 116Backtesting Protocol 119References 121Chapter 7: Deployment 123Reference Architecture 127The Reference Architecture and Hardware 130References 131Chapter 8: Performance 133Who Is Responsible for Performance? 134What Are the Work Processes of Performance? 134Business Performance 136Technological Performance 138References 141Chapter 9: A New Beginning 143Building an Investment Management Firm Around Artificial Intelligence? 144The Fallacy of Going Digital 145Why Build Your Firm Around AI? 148You Must Rely on Your Own Capabilities 149What Is Asset Science? 150A Healthy Cycle 154The Tool Set 155This Is Not Just Automation 156References 157Chapter 10: Customer Experience Science 159Customer Experience 159Value, Strength, and Duration of Relationship 160Understanding Customers: Empathy for CX 161Steps to Become an Empathetic Asset Management Firm 162Know Your Empmeter 162Expand Empathy Awareness and Understanding 163Incorporate into Products and Services 163What Is Automated Empathy and Compassion (AEC)? 163Incorporating AEC Marketing 165References 168Chapter 11: Marketing Science 171Who Undertakes This Responsibility? 171How to Apply AI for Marketing 172Begin with Assessment 172Know Your Data 174The AI Plan for Asset Management Marketing 176Perform Strategic Planning 176Manage Product Portfolio with AI 179Transform Your Communications 180Build Relationships 181Execute with Excellence 181References 182Chapter 12: Land that Institutional Investor with AI 183Who Is Responsible for IRMS Automation? 183Is IRMS Your CRM System? 184Know Thyself: Automated Self-Discovery 184Automated Asset Class Analysis 185Automated Institutional Analysis 185Automated Structure and Terms Analysis 186Automated Fee Analysis 186Automated Communications 186Unleash the Power of Knowing 188Chapter 13: Sales Science 189What Is Sales Science? 189Who Is Responsible for Implementing Sales Science? 190Are You Driving This in Sales? 190How to Build Your AI-Based Sales System 193References 195Chapter 14: Investment: Managing the Returns Loop 197Who Is Responsible for Investment Management? 197How to Approach Building the New-Era Investment Function? 198The Core Tool Set 204What Will Be the Function of Your Investment Lab? 206Make the Decisions 206A New World 207The (Unnecessary) Debate 208More Behaviors 208Research and Investment Strategy 209Portfolio 210Performance 210References 210Chapter 15: Regulatory Compliance and Operations 213Who Is Responsible? 213Regulatory Compliance 213Why Intelligent Automation? 214Have You Scoped Out What to Do? 215How to Do It? 215How to Use Technology for GIPS Implementation? 217Back and Middle Office 219Chapter 16: Supply Chain Science 221Who Is Responsible for Supply Chain Science? 221How to Think about Supply Chains 222References 225Chapter 17: Corporate Social Responsibility 227CSR Woes: Can Processes Explain Them? 227What Are the Criticisms of CSR? 228Measurement Issues 228Behavioral and Role Issues 230Strategic and Organizational Issues 230How to Apply AI in CSR? 231CSR Must Not Be Forgotten 232ESG Investment 232How Can AI Help? 234You Must Avoid These Mistakes 236Summary Steps 236References 237Chapter 18: AI Organization and Project Management 241The New Asset Management Organization 241Why a CAIO/COO Role? 243What Is Changing? 244How to Get There? 244Issues of the New Organization 246Change Management 248Managing AI Projects 249References 250Chapter 19: Governance and Ethics 251Corporate Governance with AI 251Governance of AI 257Framing the Ethical Problems from a Pragmatic Viewpoint 261Some Obvious Ethical Issues 262Humans and AI 262Ethics Charter 263References 264Chapter 20: Adaptation and Emergence 267The Revolution Is Real 268Complex Adaptive Systems 270Our Coronavirus Meltdown Prediction 271Index 273