Cover Image

Big Data and Artificial Intelligence in Digital Finance : Increasing Personalization and Trust in Digital Finance Using Big Data and AI.

Bibliographic Details
Main Author: Soldatos, John.
Other Authors: Kyriazis, Dimosthenis.
Format: eBook
Language:English
Published: Cham : Springer International Publishing AG, 2022.
Edition:1st ed.
Subjects:
Electronic books.
Online Access:Click to View
Table of Contents:
  • 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.
  • 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.
  • 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.
  • 3.2 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.
  • 3 Datasets Used and Data Enrichment.

Similar Items