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0 (hodnocen0 x )

EB

ONLINE

Cham : Springer International Publishing AG, 2022

1 online resource (371 pages)

ISBN 9783030945909 (electronic bk.)

ISBN 9783030945893

Print version: Soldatos, John Big Data and Artificial Intelligence in Digital Finance Cham : Springer International Publishing AG,c2022 ISBN 9783030945893

Intro -- Preface -- Acknowledgments -- Contents -- Editors and Contributors -- About the Editors -- Contributors -- Abbreviations -- Part I Big Data and AI Technologies for Digital Finance -- 1 A Reference Architecture Model for Big Data Systems in the Finance Sector -- 1 Introduction -- 1.1 Background -- 1.2 Big Data Challenges in Digital Finance -- 1.2.1 Siloed Data and Data Fragmentation -- 1.2.2 Real-Time Computing -- 1.2.3 Mobility -- 1.2.4 Omni-channel Banking: Multiple Channel Management -- 1.2.5 Orchestration and Automation: Toward MLOps and AIOps -- 1.2.6 Transparency and Trustworthiness -- 1.3 Merits of a Reference Architecture (RA) -- 1.4 Chapter Structure -- 2 Related Work: Architectures for Systems in Banking and Digital Finance -- 2.1 IT Vendors’ Reference Architectures -- 2.2 Reference Architecture for Standardization Organizations and Industrial Associations -- 2.3 Reference Architectures of EU Projects and Research Initiatives -- 2.4 Architectures for Data Pipelining -- 2.5 Discussion -- 3 The INFINITECH Reference Architecture (INFINITECH-RA) -- 3.1 Driving Principles: INFINITECH-RA Overview -- 3.2 The INFINITECH-RA -- 3.2.1 Logical View of the INFINITECH-RA -- 3.2.2 Development Considerations -- 3.2.3 Deployment Considerations -- 4 Sample Pipelines Based on the INFINITECH-RA -- 4.1 Simple Machine Learning Pipeline -- 4.2 Blockchain Data-Sharing and Analytics -- 4.3 Using the INFINITECH-RA for Pipeline Development and Specification -- 5 Conclusions -- References -- 2 Simplifying and Accelerating Data Pipelines in Digital Finance and Insurance Applications -- 1 Introduction -- 2 Challenges in Data Pipelines in Digital Finance and Insurance -- 2.1 IT Cost Savings -- 2.2 Productivity Improvements -- 2.3 Reduced Regulatory and Operational Risks -- 2.4 Delivery of New Capabilities and Services.

2.2.1 Instrument Features -- 2.2.2 System Features -- References -- 6 Historic Overview and Future Outlook of Blockchain Interoperability -- 1 Multidimensional Mutually Exclusive Choices as the Source of Blockchain Limitations -- 2 First Attempts at Interoperability -- 2.1 Anchoring -- 2.2 Pegged Sidechains -- 2.3 Cross-Chain Atomic Swaps -- 2.4 Solution Design -- 3 Later Attempts at Interoperability -- 3.1 Polkadot -- 3.2 Cosmos -- 3.3 Interledger -- 3.4 Idealistic Solution Design -- References -- 7 Efficient and Accelerated KYC Using Blockchain Technologies -- 1 Introduction -- 2 Architecture -- 3 Use Case Scenarios -- 4 Sequence Diagrams -- 5 Implementation Solution -- 6 Conclusions and Future Works -- References -- 8 Leveraging Management of Customers’ Consent Exploiting the Benefits of Blockchain Technology Towards SecureData Sharing -- 1 Introduction -- 2 Consent Management for Financial Services -- 3 Related Work -- 4 Methodology -- 4.1 User’s Registration -- 4.2 Customer Receives a Request to Provide New Consent for Sharing His/Her Customer Data -- 4.3 Definition of the Consent -- 4.4 Signing of the Consent by the Interested Parties -- 4.5 Consent Form Is Stored in the Consent Management System -- 4.6 Consent Update or Withdrawal -- 4.7 Expiration of the Validity Period -- 4.8 Access Control Based on the Consent Forms -- 4.9 Retrieve Complete History of Consents -- 5 The INFINITECH Consent Management System -- 5.1 Implemented Methods -- 5.1.1 Definition of Consent -- 5.1.2 Consent Update or Withdrawal -- 5.1.3 Consent Expiration -- 5.1.4 Access Control -- 5.1.5 Complete History of Consents -- 6 Conclusions -- References -- Part III Applications of Big Data and AI in Digital Finance -- 9 Addressing Risk Assessments in Real-Time for Forex Trading -- 1 Introduction -- 2 Portfolio Risk -- 3 Risk Models -- 3.1 Value at Risk.

3 Regular Data Pipeline Steps in Digital Finance and Insurance -- 3.1 Data Intaking -- 3.2 Data Transformation -- 3.3 Generate the Required Output -- 4 How LeanXcale Simplifies and Accelerates Data Pipelines -- 4.1 High Insertion Rates -- 4.2 Bidimensional Partitioning -- 4.3 Online Aggregates -- 4.4 Scalability -- 5 Exploring New Use Cases: The INFINITECH Approach to Data Pipelines -- 6 Conclusion -- References -- 3 Architectural Patterns for Data Pipelines in Digital Finance and Insurance Applications -- 1 Introduction -- 1.1 Motivation -- 1.2 Data Pipelining Architectural Pattern Catalogue and How LeanXcale Simplifies All of Them -- 2 A Taxonomy of Databases for Data Pipelining -- 2.1 Database Taxonomy -- 2.1.1 Operational Databases -- 2.1.2 Data Warehouses -- 2.1.3 Data Lakes -- 2.2 Operational Database Taxonomy -- 2.2.1 Traditional SQL Databases -- 2.2.2 NoSQL Databases -- 2.2.3 NewSQL Databases -- 2.3 NoSQL Database Taxonomy -- 2.3.1 Key-Value Data Stores -- 2.3.2 Document-Oriented Databases -- 2.3.3 Graph Databases -- 2.3.4 Wide-Column Data Stores -- 3 Architectural Patterns Dealing with Current and Historical Data -- 3.1 Lambda Architecture -- 3.2 Beyond Lambda Architecture -- 3.3 Current Historical Data Splitting -- 3.4 From Current Historical Data Splitting to Real-Time Data Warehousing -- 4 Architectural Patterns for Off-Loading Critical Databases -- 4.1 Data Warehouse Off-Loading -- 4.2 Simplifying Data Warehouse Off-Loading -- 4.3 Operational Database Off-Loading -- 4.4 Operational Database Off-Loading at Any Scale -- 4.5 Database Snapshotting -- 4.6 Accelerating Database Snapshotting -- 5 Architectural Patterns Dealing with Aggregations -- 5.1 In-Memory Application Aggregation -- 5.2 From In-Memory Application Aggregation to Online Aggregation -- 5.3 Detail-Aggregate View Splitting -- 5.4 Avoiding Detail-Aggregate View Splitting.

6 Architectural Patterns Dealing with Scalability -- 6.1 Database Sharding -- 6.2 Removing Database Sharding -- 7 Data Pipelining in INFINITECH -- 8 Conclusions -- 4 Semantic Interoperability Framework for Digital Finance Applications -- 1 Introduction -- 2 Background: Relevant Concepts and Definitions for the INFINITECH Semantic Interoperability Framework -- 2.1 Interoperability -- 2.1.1 Semantic Interoperability -- 2.1.2 Semantic Models -- 2.1.3 Ontologies -- 2.1.4 Semantic Annotations -- 2.2 Methodologies for Ontology Engineering -- 2.2.1 METHONTOLOGY -- 2.2.2 SAMOD -- 2.2.3 DILIGENT -- 2.2.4 UPON Lite -- 3 INFINITECH Semantic Interoperability Framework -- 3.1 Methodology for Semantic Models, Ontology Engineering, and Prototyping -- 3.1.1 Modeling Method -- 3.1.2 Envisioned Roles and Functions in Semantic Models, Ontology Engineering, and Prototyping -- 4 Applying the Methodology: Connecting the Dots -- 4.1 Workflow and Technological Tools for Validation of the Methodology -- 4.2 Collecting -- 4.3 Building and Merging -- 4.4 Refactoring and Linking -- 4.4.1 Data Ingestion -- 4.4.2 Semantic Alignment: Building and Merging -- 4.4.3 Semantic Transformation: Generating a Queryable Knowledge Graphs -- 4.4.4 Data-Sharing/Provisioning -- 5 Conclusions -- References -- Part II Blockchain Technologies and Digital Currencies for Digital Finance -- 5 Towards Optimal Technological Solutions for Central Bank Digital Currencies -- 1 Understanding CBDCs -- 1.1 A Brief History of Definitions -- 1.2 How CBDCs Differ from Other Forms of Money -- 1.3 Wholesale and Retail CBDCs -- 1.4 Motivations of CBDCs -- 1.4.1 Financial Stability and Monetary Policy -- 1.4.2 Increased Competition in Payments and Threats to Financial Sovereignty -- 2 From Motivations to Design Options -- 2.1 The Design Space of CBDCs -- 2.2 Assessing Design Space Against Desirable Characteristics.

3 Datasets Used and Data Enrichment.

3.2 Expected Shortfall -- 4 Real-Time Management -- 5 Pre-trade Analysis -- 6 Architecture -- 7 Summary -- References -- 10 Next-Generation Personalized Investment Recommendations -- 1 Introduction to Investment Recommendation -- 2 Understanding the Regulatory Environment -- 3 Formalizing Financial Asset Recommendation -- 4 Data Preparation and Curation -- 4.1 Why Is Data Quality Important? -- 4.2 Data Preparation Principles -- 4.3 The INFINITECH Way Towards Data Preparation -- 5 Approaches to Investment Recommendation -- 5.1 Collaborative Filtering Recommenders -- 5.2 User Similarity Models -- 5.3 Key Performance Indicator Predictors -- 5.4 Hybrid Recommenders -- 5.5 Knowledge-Based Recommenders -- 5.6 Association Rule Mining -- 6 Investment Recommendation within INFINITECH -- 6.1 Experimental Setup -- 6.2 Investment Recommendation Suitability -- 7 Summary and Recommendations -- References -- 11 Personalized Portfolio Optimization Using Genetic(AI) Algorithms -- 1 Introduction to Robo-Advisory and Algorithm-Based Asset Management for the General Public -- 2 Traditional Portfolio Optimization Methods -- 2.1 The Modern Portfolio Theory -- 2.2 Value at Risk (VaR) -- 3 Portfolio Optimization Based on Genetic Algorithms -- 3.1 The Concept of Evolutionary Theory -- 3.2 Artificial Replication Using Genetic Algorithms -- 3.3 Genetic Algorithms for Portfolio Optimization -- 3.3.1 Multiple Input Parameters -- 3.3.2 Data Requirements -- 3.3.3 A Novel and Flexible Optimization Approach Based on Genetic Algorithms -- 3.3.4 Fitness Factors and Fitness Score -- 3.3.5 Phases of the Optimization Process Utilizing Genetic Algorithms -- 3.3.6 Algorithm Verification -- 3.3.7 Sample Use Case "Sustainability" -- 4 Summary and Conclusions -- References -- 12 Personalized Finance Management for SMEs -- 1 Introduction -- 2 Conceptual Architecture of the Proposed Approach.

001896946

express

(Au-PeEL)EBL6966394

(MiAaPQ)EBC6966394

(OCoLC)1313606242