07441 a2200157 4500 20241018124852.0 241018b |||||||| |||| 00| 0 eng d 9789355424976 English Kaabar S. 208483 Deep Learning For Finance :Creating Machine A Deep Learning Models For Trading In Python SPD 2024 344 Table of Contents Foreword. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii 1. The Business Transformation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Why Businesses Are Transforming 1 Factor 1: The Speed of Change 2 Factor 2: The Convergence of Multiple Technologies 2 Factor 3: The Importance of Data 2 Factor 4: Changing Consumer Behavior and Customer Centricity 4 Industries Heavily Impacted by Digital Transformation 5 The Consequences for Your Business 7 There’s No Analytics Transformation Without Augmented Analytics 8 A Data-Driven Culture 9 The “People Problem” and the Limits of Upskilling 9 Conclusion 10 2. The Analytics Problem. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Finding Your Analytics Purpose 12 Competition and Customer Expectations 12 Operational Efficiency 12 Availability and User Friendliness 12 Innovation 13 Regulatory Compliance 13 How to Start Your Analytics Journey 13 Industry Examples 14 Ecommerce 14 Healthcare 14 v Manufacturing 14 Financial Services 14 Government 15 Commercial Insurance 15 The Concept of Analytical Maturity 16 Determine Your Current—and Future—Data Maturity 20 Stage 1: Data Reactive 20 Stage 2: Data Active 23 Stage 3: Data Progressive 31 Stage 4: Data Fluent 36 Conclusion 40 3. Understanding Augmented Analytics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 Definition 41 The Five I’s of Augmented Analytics 43 Overcoming the Limitations of Traditional Analytics Approaches 44 Augmented Workflows 45 The Benefits of Augmented Analytics 46 AA Gives Nonexpert Users a Better Experience 48 Automated Integration Provides More Complete Insights 49 AA Gives Faster, More Efficient Insights 49 Standardization Reduces Human Errors and Bias for Better Insights 50 AA Tools Are Easier to Scale Up 51 AA Reaches Further Afield to Generate Unexpected Insights 51 Overcoming Bias 51 Key Enablers of Augmented Analytics 55 Automation and AI 56 Artificial Intelligence: The Five Archetypes 57 The Limitations of Augmented Analytics 64 The Challenges of Augmented Analytics 66 Conclusion 67 4. Preparing People and the Organization for Augmented Analytics. . . . . . . . . . . . . . . . . . 69 Tailoring Augmented Analytics for Different Organizational Roles 70 Analytics Leader 71 Analytics Translator 73 Analytics User 74 Analytics Professional 78 Analytics Transformation Manager 82 Summary of Key Roles 84 vi | Table of Contents The Center of Excellence 86 Creating a Center of Excellence 86 Approaches to Organizing a CoE 89 Driving Transformational Change with the Influence Model 94 Fostering Understanding and Conviction 95 Reinforcing with Formal Mechanisms 95 Developing Talent and Skills 96 Role Modeling 96 Cultivating a Data-Literate Culture 97 Cultivating Analytics Awareness 98 Storytelling with Data 99 Embracing Data-Driven Management 100 Leading in the Age of AI 100 The Enablement Program 101 Training Formats for Analytics Leaders 101 Training Formats for Analytics Translators 105 Data Literacy Training 107 Technical Training 107 Conclusion 108 5. Augmented Workflows. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 Types of Workflow Augmentation 110 Fixed-Rule, High-Confidence Augmentation 110 Idea and Insight Enrichment 111 Conversational Augmentation 111 Contextual Augmentation 112 Collaborative Augmentation 112 The Analytics Use-Case Approach: Finding Workflows to Augment 112 Phase 1. Idea: The Initial Spark 114 Phase 2. Concept: Structuring the Idea 115 Phase 3. Proof of Concept: Testing the Waters 118 Phase 4. Prototyping: Shaping the Concept 121 Phase 5. Pilot: The Test Run 123 Phase 6. Product: Full Deployment 124 Making the Make-or-Buy Decision 124 Decision Scenarios 127 Overarching Success Factors 129 Balancing Automation and Integration 130 The Use-Case Library 133 Technical Requirements for Implementing AA 139 Table of Contents | vii Infrastructure Setup Challenges 141 IT System Integration Challenges 149 Governance Challenges 151 Conclusion 157 6. Augmented Frames. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 Business Objects and Frame Units 159 Understanding Frames 162 Key Features of Frames 164 Frame Types 165 Frame Engines 167 Frame Engine Types 169 Attribute Aggregation 170 Engine Interfaces 172 Result Objects 179 Implementation Challenges 186 Frame Agent 187 Dissolving Frames 188 Identifying Types 188 Translating Frame Units 188 Enriching Frames 188 Orchestrating Calls 189 Standardizing Results 189 Central Repository 189 Monitoring and Performance Analysis 189 User Access and Security 190 User Interface 190 Frame Dissolver 193 Frame Adapter 195 Dealing with Group Variables 196 Dealing with Bottom-up Business Object Structures 198 Dealing with Unconnected Business Objects 199 Frame Creator 200 Case Study: AP/TP Frame Engine 201 Infrastructure and Technology 207 An Iterative Approach to Introducing Augmented Frames 211 Iteration 1: Free Frames and Frame Engines 211 Iteration 2: A Frame Agent and Frame Adapter 212 Iteration 3: The Frame Dissolver, ID Frames, and Indexed Frames 213 Iteration 4: Static Frames 213 viii | Table of Contents Iteration 5: Dynamic Frames 214 Iteration 6: The Frame Creator 215 Iteration Wrap-up 215 Conclusion 216 7. Applied Examples. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219 The Underwriting Process 219 Types of Augmented Workflows in Underwriting 220 The Workflows in Detail 221 Example 1: Location Workflow 224 Situation and Problem Statement 224 Solution Overview 224 Solution Breakdown 226 Example Summary 228 Example 2: Benchmarking Workflow 229 Situation and Problem Statement 229 Solution Overview 229 Solution Breakdown 231 Example Summary 234 Example 3: Proposal Workflow 235 Situation and Problem Statement 235 Solution Overview 236 Solution Breakdown 236 Example Summary 238 Example 4: Improved Forecasting in Agile Projects 238 Situation and Problem Statement 238 Solution Overview 239 Solution Breakdown 240 Example Summary 245 Example 5: Quick Sales Intelligence 246 Situation and Problem Statement 247 Solution Overview 248 Solution Breakdown 249 Example Summary 256 Conclusion 257 Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259 Table of Contents | ix BK 359832 359832 0 0 ddc 0 0 CCEP CCEP 2024-10-11 115 1425.00 3 006.32 KAA CCEP-BK-67496 2025-08-11 00:00:00 2025-07-26 1900.00 2024-10-11 BK