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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

  1. Cover
  2. Title Page
  3. Copyright
  4. Dedication
  5. Preface
  6. Acknowledgments
  7. Chapter 1: AI in Investment Management
    1. WHAT ABOUT AI SUPPLIERS?
    2. LISTENING WITHOUT JUDGING
    3. THE FOUR STAGES OF AI IN INVESTMENTS
    4. THE CORE MODEL OF AIAI
    5. YOUR JOURNEY THROUGH THIS BOOK
    6. HOW TO READ AND APPLY THIS BOOK?
    7. REFERENCES
  8. Chapter 2: AI and Business Strategy
    1. WHY STRATEGY? THE RED BUTTON
    2. AI—A REVOLUTION OF ITS OWN
    3. INTELLIGENCE AS A COMPETITIVE ADVANTAGE
    4. INTELLIGENCE AS A COMPETITIVE ADVANTAGE AND VARIOUS STRATEGY SCHOOLS
    5. THE INTELLIGENCE SCHOOL
    6. INTELLIGENCE AND ACTIONS
    7. ACTIONS
    8. AUTOMATION
    9. INTELLIGENCE ACTION CHAIN AND SEQUENCE
    10. ENTERPRISE SOFTWARE
    11. DATA
    12. COMPETITIVE ADVANTAGE
    13. BUSINESS CAPABILITIES
  9. Chapter 3: Design
    1. WHO IS RESPONSIBLE FOR DESIGN?
    2. INTRODUCTION TO DESIGN
    3. AI AS A COMPETITIVE ADVANTAGE
    4. THE TEN ELEMENTS OF DESIGN
    5. 1. DESIGN YOUR BUSINESS MODEL
    6. 2. SET GOALS FOR THE ENTIRE FIRM
    7. 3. SPECIFY OBJECTIVES FOR AUTOMATION AND INTELLIGENCE
    8. 4. DESIGN WORK TASK FRAMES BASED ON HUMAN-COMPUTER INTERACTION
    9. 5. PERFORM A DTC (DO, THINK, CREATE) ANALYSIS
    10. 6. CREATE A SADAL FRAMEWORK
    11. 7. DEPLOY A FEEDBACK SYSTEM AND DEFINE PERFORMANCE MEASURES
    12. 8. DETERMINE THE BUSINESS CASE OR VALUE
    13. 9. ANALYZE RISKS
    14. 10. DEVELOP A GOVERNANCE PLAN
    15. SOME ADDITIONAL IDEAS ABOUT DESIGNING INTELLECTUALIZATION
    16. SUMMARY OF THE DESIGN PROCESS
    17. REFERENCES
    18. NOTE
  10. Chapter 4: Data
    1. WHO IS RESPONSIBLE FOR THE DATA CAPABILITY?
    2. DATA AND MACHINE LEARNING
    3. RAW DATA
    4. STRUCTURED VS. UNSTRUCTURED DATA
    5. DATA USED IN INVESTMENTS
    6. DATA MANAGEMENT FUNCTION FOR THE AI ERA
    7. STEP 1: DATA NEEDS ASSESSMENT (DNA)
    8. STEP 2: PERFORM STRATEGIC DATA PLANNING
    9. STEP 3: KNOW THE SENSORS AND SOURCES (IDENTIFY GAPS)
    10. STEP 4: PROCURE AND UNDERSTAND THE SUPPLY BASE
    11. STEP 5: UNDERSTAND THE DATA TYPE (SIGNALS)
    12. STEP 6: ORGANIZE DATA FOR USABILITY
    13. STEP 7: ARCHITECT DATA
    14. STEP 8: ENSURE DATA QUALITY
    15. STEP 9: DATA STORAGE AND WAREHOUSING
    16. STEP 10: EXCEL IN DATA SECURITY AND PRIVACY
    17. STEP 11: IMPLEMENT DATA FOR AI
    18. STEP 12: PROVIDE INVESTMENT SPECIALIZATION
    19. ABOUT LEGACY DATA MANAGEMENT
    20. REFERENCES
  11. Chapter 5: Model Development
    1. WHO IS RESPONSIBLE?
    2. HIGH-LEVEL PROCESS
    3. MODELS
    4. THE POWER OF PATTERNS
    5. TECHNIQUES OF LEARNING
    6. WHAT IS MACHINE LEARNING?
    7. SCIENTIFIC PROCESS ON STEROIDS
    8. THE LEARNING MACHINES
    9. ALGORITHMS
    10. SUPERVISED LEARNING
    11. SUPERVISED: CLASSIFICATION
    12. CLASSIFICATION: RANDOM FOREST
    13. CLASSIFICATION: USING MATHEMATICAL FUNCTIONS
    14. CLASSIFICATION: SIMPLE LINEAR CLASSIFIER
    15. SUPERVISED: SUPPORT VECTOR MACHINE
    16. CLASSIFICATION: NAIVE BAYES
    17. CLASSIFICATION: BAYESIAN BELIEF NETWORKS
    18. CLASSIFICATION: K-NEAREST NEIGHBOR
    19. SUPERVISED: REGRESSION
    20. SUPERVISED: MULTIDIMENSIONAL REGRESSION
    21. UNSUPERVISED LEARNING
    22. NEURAL NETWORKS
    23. REINFORCEMENT LEARNING
    24. REFERENCES
  12. Chapter 6: Evaluation
    1. WHO PERFORMS THE EVALUATION?
    2. PROBLEMS
    3. MAKING THE MODEL WORK
    4. OVERFITTING AND UNDERFITTING
    5. SCALE AND MACHINE LEARNING
    6. NEW METHODS
    7. BIAS AND VARIANCE
    8. BACKTESTING
    9. BACKTESTING PROTOCOL
    10. REFERENCES
  13. Chapter 7: Deployment
    1. REFERENCE ARCHITECTURE
    2. THE REFERENCE ARCHITECTURE AND HARDWARE
    3. REFERENCES
  14. Chapter 8: Performance
    1. WHO IS RESPONSIBLE FOR PERFORMANCE?
    2. WHAT ARE THE WORK PROCESSES OF PERFORMANCE?
    3. BUSINESS PERFORMANCE
    4. TECHNOLOGICAL PERFORMANCE
    5. REFERENCES
  15. Chapter 9: A New Beginning
    1. BUILDING AN INVESTMENT MANAGEMENT FIRM AROUND ARTIFICIAL INTELLIGENCE?
    2. THE FALLACY OF GOING DIGITAL
    3. WHY BUILD YOUR FIRM AROUND AI?
    4. YOU MUST RELY ON YOUR OWN CAPABILITIES
    5. WHAT IS ASSET SCIENCE?
    6. A HEALTHY CYCLE
    7. THE TOOL SET
    8. THIS IS NOT JUST AUTOMATION
    9. REFERENCES
  16. Chapter 10: Customer Experience Science
    1. CUSTOMER EXPERIENCE
    2. VALUE, STRENGTH, AND DURATION OF RELATIONSHIP
    3. UNDERSTANDING CUSTOMERS: EMPATHY FOR CX
    4. STEPS TO BECOME AN EMPATHETIC ASSET MANAGEMENT FIRM
    5. KNOW YOUR EMPMETER
    6. EXPAND EMPATHY AWARENESS AND UNDERSTANDING
    7. INCORPORATE INTO PRODUCTS AND SERVICES
    8. WHAT IS AUTOMATED EMPATHY AND COMPASSION (AEC)?
    9. INCORPORATING AEC MARKETING
    10. REFERENCES
  17. Chapter 11: Marketing Science
    1. WHO UNDERTAKES THIS RESPONSIBILITY?
    2. HOW TO APPLY AI FOR MARKETING
    3. BEGIN WITH ASSESSMENT
    4. KNOW YOUR DATA
    5. THE AI PLAN FOR ASSET MANAGEMENT MARKETING
    6. PERFORM STRATEGIC PLANNING
    7. MANAGE PRODUCT PORTFOLIO WITH AI
    8. TRANSFORM YOUR COMMUNICATIONS
    9. BUILD RELATIONSHIPS
    10. EXECUTE WITH EXCELLENCE
    11. REFERENCES
  18. Chapter 12: Land that Institutional Investor with AI
    1. WHO IS RESPONSIBLE FOR IRMS AUTOMATION?
    2. IS IRMS YOUR CRM SYSTEM?
    3. KNOW THYSELF: AUTOMATED SELF-DISCOVERY
    4. AUTOMATED ASSET CLASS ANALYSIS
    5. AUTOMATED INSTITUTIONAL ANALYSIS
    6. AUTOMATED STRUCTURE AND TERMS ANALYSIS
    7. AUTOMATED FEE ANALYSIS
    8. AUTOMATED COMMUNICATIONS
    9. UNLEASH THE POWER OF KNOWING
  19. Chapter 13: Sales Science
    1. WHAT IS SALES SCIENCE?
    2. WHO IS RESPONSIBLE FOR IMPLEMENTING SALES SCIENCE?
    3. ARE YOU DRIVING THIS IN SALES?
    4. HOW TO BUILD YOUR AI-BASED SALES SYSTEM
    5. REFERENCES
  20. Chapter 14: Investment: Managing the Returns Loop
    1. WHO IS RESPONSIBLE FOR INVESTMENT MANAGEMENT?
    2. HOW TO APPROACH BUILDING THE NEW-ERA INVESTMENT FUNCTION?
    3. THE CORE TOOL SET
    4. WHAT WILL BE THE FUNCTION OF YOUR INVESTMENT LAB?
    5. MAKE THE DECISIONS
    6. A NEW WORLD
    7. THE (UNNECESSARY) DEBATE
    8. MORE BEHAVIORS
    9. RESEARCH AND INVESTMENT STRATEGY
    10. PORTFOLIO
    11. PERFORMANCE
    12. REFERENCES
  21. Chapter 15: Regulatory Compliance and Operations
    1. WHO IS RESPONSIBLE?
    2. REGULATORY COMPLIANCE
    3. WHY INTELLIGENT AUTOMATION?
    4. HAVE YOU SCOPED OUT WHAT TO DO?
    5. HOW TO DO IT?
    6. HOW TO USE TECHNOLOGY FOR GIPS IMPLEMENTATION?
    7. BACK AND MIDDLE OFFICE
  22. Chapter 16: Supply Chain Science
    1. WHO IS RESPONSIBLE FOR SUPPLY CHAIN SCIENCE?
    2. HOW TO THINK ABOUT SUPPLY CHAINS
    3. REFERENCES
  23. Chapter 17: Corporate Social Responsibility
    1. CSR WOES: CAN PROCESSES EXPLAIN THEM?
    2. WHAT ARE THE CRITICISMS OF CSR?
    3. MEASUREMENT ISSUES
    4. BEHAVIORAL AND ROLE ISSUES
    5. STRATEGIC AND ORGANIZATIONAL ISSUES
    6. HOW TO APPLY AI IN CSR?
    7. CSR MUST NOT BE FORGOTTEN
    8. ESG INVESTMENT
    9. HOW CAN AI HELP?
    10. YOU MUST AVOID THESE MISTAKES
    11. SUMMARY STEPS
    12. REFERENCES
  24. Chapter 18: AI Organization and Project Management
    1. THE NEW ASSET MANAGEMENT ORGANIZATION
    2. WHY A CAIO/COO ROLE?
    3. WHAT IS CHANGING?
    4. HOW TO GET THERE?
    5. ISSUES OF THE NEW ORGANIZATION
    6. CHANGE MANAGEMENT
    7. MANAGING AI PROJECTS
    8. REFERENCES
  25. Chapter 19: Governance and Ethics
    1. CORPORATE GOVERNANCE WITH AI
    2. GOVERNANCE OF AI
    3. FRAMING THE ETHICAL PROBLEMS FROM A PRAGMATIC VIEWPOINT
    4. SOME OBVIOUS ETHICAL ISSUES
    5. HUMANS AND AI
    6. ETHICS CHARTER
    7. REFERENCES
  26. Chapter 20: Adaptation and Emergence
    1. THE REVOLUTION IS REAL
    2. COMPLEX ADAPTIVE SYSTEMS
    3. OUR CORONAVIRUS MELTDOWN PREDICTION
  27. Index
  28. End User License Agreement
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