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Artificial Intelligence for Sustainability : Innovations in Business and Financial Services
| Artificial Intelligence for Sustainability : Innovations in Business and Financial Services |
| Autore | Walker Thomas |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Cham : , : Palgrave Macmillan, , 2024 |
| Descrizione fisica | 1 online resource (250 pages) |
| Disciplina | 658.0563 |
| Altri autori (Persone) |
WendtStefan
GoubranSherif SchwartzTyler |
| ISBN | 3-031-49979-4 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Acknowledgements -- Contents -- About the Editors and Contributors -- About the Editors -- Notes on Contributors -- Copy Editing Team -- List of Figures -- List of Tables -- 1: Artificial Intelligence for Sustainability: An Overview -- 1.1 Introduction -- 1.2 Overview of Content -- References -- Part I: AI and Sustainable Industry Applications -- 2: Fast Fashion's Fate: Artificial Intelligence, Sustainability, and the Apparel Industry -- 2.1 Silhouette: Chapter's Contour -- 2.2 Pattern: AI and Algorithms -- 2.3 Ensemble: Apparel, Sustainability, and AI -- 2.4 Samples: Cases and Companies -- 2.5 Trends: Fashion's Future -- References -- 3: Artificial Intelligence and the Global Automotive Industry -- 3.1 Introduction -- 3.2 The Six Stages of Self-Driving and Their Implications -- 3.3 Artificial Intelligence and Technology Features -- 3.4 Autonomous Vehicles (AV) Industry Forces -- 3.5 Conclusion and Path Forward -- References -- 4: The Emergence of the Nighttime Artificial Intelligence-Robot-Driven Economy -- 4.1 Introduction -- 4.2 Transitions Toward an AI-Robot-Driven Economy -- 4.3 Decentralization, Localization, and Customization -- 4.4 AI-Robot-Driven Circular Economy (CE) and Value Chain Optimization -- 4.5 The Emergence of the AI-Robot-Driven Nighttime Economy -- 4.6 Conclusion -- References -- Part II: AI and Sustainable Business Operations -- 5: Strengthening the Sustainability of Artificial Intelligence: Fostering Green Intelligence for a More Ethical Future -- 5.1 Introduction -- 5.2 Bounded AI -- 5.3 The Greening of Bounded AI -- 5.4 Further Considerations for the Business Sector -- 5.5 Recommendations for Business Leaders, Policymakers, and Other Stakeholders -- 5.6 Conclusion -- References.
6: Predictive Machine Learning in Assessing Materiality: The Global Reporting Initiative Standard and Beyond -- 6.1 Introduction -- 6.2 Literature Review -- 6.3 Methodology and Results -- 6.3.1 Methodology -- 6.3.2 Results -- 6.4 Conclusions and Limitations -- References -- 7: Artificial Intelligence and the Food Value Chain -- 7.1 Introduction -- 7.2 Background -- 7.2.1 The Sustainability of Value Chains -- 7.2.2 Sustainable Food Value Chains -- 7.2.3 Artificial Intelligence (AI) -- 7.3 Sustainability Benefits from Artificial Intelligence in the Food Value Chain -- 7.3.1 Food Production -- 7.3.2 Food Aggregation -- 7.3.3 Food Processing -- 7.3.4 Food Distribution -- 7.4 Discussion and Conclusions -- References -- Part III: The Role of AI in Sustainable Development and the 2030 Agenda -- 8: Analysis of Smart Meter Data for Energy Waste Management -- 8.1 Introduction -- 8.2 Sustainability Benefits of Smart Grids and Smart Meters -- 8.3 Challenges of Using AI for Smart Meter Data Analysis -- 8.4 The Future of AI for Smart Meter Data Analytics -- References -- 9: Leveraging AI to Map SDG Coverage and Uncover Partnerships in Swiss Philanthropy -- 9.1 Overview -- 9.2 Contextualizing Sustainability - The SDG Framework -- 9.3 The Landscape of AI for SDGs -- 9.4 Philanthropy for the SDGs -- 9.5 Mapping SDG-Alignment of the Swiss Philanthropic Ecosystem -- 9.6 Risks of AI in Philanthropy and SDGs -- 9.7 Conclusion -- References -- 10: The Potential Role of Artificial Intelligence in the Commercialization of Traditional Medicines in Tropical Regions -- 10.1 Introduction -- 10.2 Sustainability Challenges with Developing Traditional and Complementary Medicines -- 10.3 Examples of Sources of Information on Traditional and Complementary Medicines. 10.4 Potential Role for AI in Developing a Comprehensive Process to Identify Efficacious Traditional Medicines -- 10.5 Discussion and Recommendations -- References -- Index. |
| Record Nr. | UNINA-9910805569503321 |
Walker Thomas
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| Cham : , : Palgrave Macmillan, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Big data in finance : opportunities and challenges of financial digitalization / / Thomas Walker, Frederick Davis, Tyler Schwartz, editors
| Big data in finance : opportunities and challenges of financial digitalization / / Thomas Walker, Frederick Davis, Tyler Schwartz, editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Palgrave Macmillan, , [2022] |
| Descrizione fisica | 1 online resource (283 pages) : illustrations |
| Disciplina | 332 |
| Soggetto topico |
Finance - Data processing
Big data Financial services industry - Technological innovations |
| ISBN | 3-031-12240-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Acknowledgments -- Contents -- Notes on Contributors -- List of Figures -- List of Tables -- Introduction -- Big Data in Finance: An Overview -- 1 Introduction -- 2 Overview of Content -- 2.1 Part I: Big Data in the Financial Markets -- 2.2 Part II: Big Data in Financial Services -- 2.3 Part III: Case Studies and Applications -- References -- Big Data in the Financial Markets -- Alternative Data -- 1 Introduction -- 2 Characteristics of Alternative Data -- 2.1 Less Commonly Used by Market Participants -- 2.2 Tend to Be More Costly to Collect and Purchase -- 2.3 Typically Outside of Financial Markets -- 2.4 Tend to Lack Historical Data -- 2.5 More Challenging to Use -- 3 Catalysts of the Growth in Alternative Data -- 4 Sources of Alternative Data -- 5 Types of Alternative Data -- 5.1 Text Data -- 5.2 Job Postings and Other Economic Activity Indicators -- 5.3 Mandatory Disclosures -- 5.4 Social Media Data: Use Cases, Methods, Applications -- 5.5 Transaction Data -- 5.6 Satellite Imagery and Weather Data -- 6 Processing Alternative Data -- 7 Evaluating Alternative Data -- 8 Conclusion -- References -- An Algorithmic Trading Strategy to Balance Profitability and Risk -- 1 Introduction -- 2 Algorithmic Trading: Concept, Methods, and Influence -- 3 Proposed AT Strategy -- 4 Empirical Evidence of Proposed AT Strategies -- 4.1 Empirical Analysis -- 5 Comparison of Proposed AT Strategy with Other Benchmarks -- 5.1 Empirical Evidence of AT Strategy Using IBEX-35 Exchange -- 5.2 Evidence of AT Strategy Using Fictional Data and Other AT Strategies -- 6 Discussion and Applicability of Big Data to Proposed AT Strategy -- 7 Conclusion -- References -- High-Frequency Trading and Market Efficiency in the Moroccan Stock Market -- 1 Introduction -- 2 Literature Review -- 3 Methodology and Data -- 3.1 Methodology -- 3.2 Data -- 4 Results.
5 Conclusion -- References -- Ensemble Models Using Symbolic Regression and Genetic Programming for Uncertainty Estimation in ESG and Alternative Investments -- 1 Introduction -- 2 Background -- 2.1 Stocks and ETFs -- 2.1.1 Levi Strauss -- 2.1.2 British American Tobacco (BATS) -- 2.1.3 How ETFs Integrate ESG Factors into Stock Selections -- 3 Modeling and Data Collection -- 3.1 Modeling -- 3.1.1 Symbolic Regression (SR) -- 3.1.2 Symbolic Regression Versus Regression Models -- 3.1.3 Trustable Model Ensembles -- 3.1.4 Estimating (and Reducing) Uncertainty -- 3.2 Data Collection -- 3.2.1 Publicly Traded Private Equity Stocks -- 3.2.2 Sustainable ETFs -- 4 Results -- 4.1 Publicly Traded Private Equity Stocks -- 4.2 Sustainable ETFs -- 5 Discussion -- 5.1 Publicly Traded Private Equity Stocks -- 5.2 Sustainable ETFs -- 5.3 Ensemble Models Using Big Data -- 6 Conclusion -- 6.1 Publicly Traded Private Equity Stocks -- 6.2 Sustainable ETFs -- Appendix -- References -- Big Data in Financial Services -- Consumer Credit Assessments in the Age of Big Data -- 1 Introduction -- 2 Overview of Traditional Credit Assessment Data and Techniques -- 3 FinTech Lenders and Data Evolution -- 3.1 P2P Lending and Resulting Data Creation -- 3.2 The Expanding Scope of Alternative Data -- 4 Advancements in Methodologies and Technologies -- 4.1 Common Classification Methodologies in ML -- 4.2 Model Performance and Evaluation -- 5 Challenges, Biases, and Ethics -- 6 Conclusions and Areas for Future Research -- References -- Robo-Advisors: A Big Data Challenge -- 1 Introduction -- 2 Robo-Advisor Features, Benefits, and Drawbacks -- 2.1 Generalities and Recent Trends in the Financial Industry -- 2.2 Robo-Advisor Benefits -- 2.3 Robo-Advisor Drawbacks -- 3 Big Data and Artificial Intelligence in Robo-Advisory -- 3.1 Humanization Inspired by Artificial Intelligence. 3.2 Big Data for Robo-Advisor Customization -- 3.3 Opening the Black Box -- 4 Conclusion -- References -- Bitcoin: Future or Fad? -- 1 Introduction -- 2 Is Bitcoin the Future of Payment Systems? -- 2.1 Bitcoin as a Cash Proxy -- 2.1.1 Stablecoins -- 2.2 Bitcoin vs Gold: A Store of Value? -- 2.3 Bitcoin: Investment and Diversification Role -- 2.3.1 Bitcoin: Political Uncertainty and Dictatorial Regimes -- 2.4 Is Bitcoin a Collectible Asset? -- 3 Discussion -- 3.1 What is Bitcoin's Real Contribution: Cryptocurrencies, Big Data, and Blockchain Technology -- 3.2 Government Regulations -- 4 Concluding Thoughts -- References -- Culture, Digital Assets, and the Economy: A Trans-National Perspective -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Hypothesis Development -- 3.2 Data, Variables, and Modeling -- 4 Results -- 4.1 Financial Institutions and the Use of Digital Assets -- 4.2 The Role of Culture in the Use of Digital Assets -- 4.2.1 Evidence from Hofstede Culture Dimensions -- 4.2.2 Evidence from Alternative Measures of Culture -- 5 Conclusion -- Appendix -- References -- Case Studies and Applications -- Islamic Finance in Canada Powered by Big Data: A Case Study -- 1 Introduction -- 2 Methods -- 3 Deep Learning Models -- 3.1 The Building Blocks of Deep Learning -- 3.2 Deep Learning Models for Credit Scoring and Risk Prediction -- 3.3 Deep Learning Models for Processing Sequential Data -- 4 How Deep Learning Is (and Can Be) Used in Credit Unions -- 4.1 Deep Learning Models for Consumer Risk Prediction -- 4.2 Deep Learning Models for Financial Forecasting -- 5 Conclusions -- References -- Assessing the Carbon Footprint of Cryptoassets: Evidence from a Bivariate VAR Model -- 1 Introduction -- 2 Literature Review -- 3 Data Description -- 4 Empirical Methodology -- 4.1 Causality Tests -- 4.2 Impulse-Response Analysis. 5 Environmental Impact of Cryptoassets -- 6 Concluding Remarks -- References -- A Data-Informed Approach to Financial Literacy Enhancement Using Cognitive and Behavioral Analytics -- 1 Introduction -- 2 Related Work -- 2.1 Defining Financial Literacy -- 2.2 Associated Factors -- 3 Learning About Learners: Analyzing Participant Behavior in a Large-Scale Financial Literacy Program -- 3.1 Financial Literacy Training Boosts Financial Knowledge, Confidence, and Intention -- 3.2 Heterogeneities in Impact of Financial Literacy Trainings -- 3.3 Factors Associated with Financial Intention -- 3.4 Generating Profiles of IFL's Learners Using Cluster Analysis -- 4 Recommendations for a Data-Informed Financial Literacy Program -- 4.1 Expanding Financial Literacy Touchpoints on Mobile and Web -- 4.2 Development of a Psychologically Enhanced Learner Profile for Improved Personalization -- 4.3 Continuous Evaluation of Financial Literacy Programs and Policies -- 4.4 Increased Focus on Specific Population Groups -- 5 Conclusion -- References -- Index. |
| Record Nr. | UNINA-9910735388603321 |
| Cham, Switzerland : , : Palgrave Macmillan, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||