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Advances in Banking Technology and Management: Impacts of ICT and CRM

Book Description

"Banking across the world has undergone extensive changes thanks to the profound influence of developments and trends in information communication technologies, business intelligence, and risk management strategies. While banking has become easier and more convenient for the consumer, the advances and intricacies of emerging technologies have made banking operations all the more cumbersome.

Advances in Banking Technology and Management: Impacts of ICT and CRM examines the various myriads of technical and organizational elements that impact services management, business management, risk management, and customer relationship management, and offers research to aid the successful implementation of associated supportive technologies."

Table of Contents

  1. Copyright
  2. Foreword
  3. Preface
  4. Acknowledgment
  5. I. Introduction to Banking Technology and Management
    1. ABSTRACT
    2. INTRODUCTION
      1. Evolution of Banking
    3. ROLE OF ICT IN BANKING
        1. Negative Effects of ICT in Banking, and Solutions Offered by ICT
    4. CRM THROUGH DATA MINING
    5. ROLE OF COMPUTER SCIENCE IN RISK MANAGEMENT IN BANKING
    6. ROLE OF IT IN DATA STORAGE AND INFORMATION SECURITY IN BANKING
    7. ROLE OF IT IN BCP/DR IN BANKING
    8. CONCLUSION
    9. REFERENCES
  6. I. Services Management
    1. II. Service Quality in Banks: What are the Factors Behind Performance and Customer Satisfaction?
      1. ABSTRACT
      2. INTRODUCTION
      3. THE BRAZILIAN BANKING SECTOR
      4. SERVICES MANAGEMENT IN BANKING
      5. SCALES FOR SERVICE QUALITY
      6. THE RESEARCH
      7. RESULTS
      8. CONCLUSION
      9. REFERENCES
    2. III. Adoption and Diffusion of Internet Banking
      1. ABSTRACT
      2. INTRODUCTION
      3. ISSUES AND TRENDS IN INTERNET BANKING TODAY
        1. Internet Banking
        2. Cost benefits of Internet Banking to Banks
        3. The Development of Enabling Technologies for Internet Banking
        4. Consumer Trust in Internet Banking
      4. RESEARCH CONTRIBUTIONS IN ADOPTION OF INTERNET BANKING
        1. Diffusion of Innovations Theory
        2. Theory of Planned Behavior (TPB)
        3. Technology Acceptance Model (TAM)
        4. Discussion of Theories
      5. CASE STUDIES
        1. A Quantitative Approach: New Zealand
        2. A Qualitative Approach: Australia
        3. Comments on Case Studies
      6. FUTURE TRENDS
      7. CONCLUSION
      8. REFERENCES
    3. IV. Customer Acceptance of Internet Banking Services in Greece: The Case Study of Alpha Bank
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
        1. The Greek Economy
        2. Alpha Bank
        3. Customer Acceptance of Internet Banking
        4. Research Methodology
          1. Issues, Controversies, Problems
          2. Solutions and Recommendations
      4. RECOMMENDATIONS
      5. FUTURE TRENDS
      6. CONCLUSION
      7. REFERENCES
    4. V. The Adoption and Use of Smart Card Technology in Banking: An Empirical Evidence from the Moneo Electronic Purse in France
      1. ABSTRACT
      2. INTRODUCTION
      3. THEORETICAL FOUNDATIONS
        1. Innovations Theory
          1. Network Externalities
      4. MODEL
        1. Factors of Individual Use (P1)
          1. Technological Factors
          2. Economic Factors
          3. Social Factors
          4. Socio-Economic Factors (P2)
          5. Network Externalities (P3)
      5. EMPIRICAL RESULTS
        1. Data and Methods
        2. Factors Determining the Decision to Adopt
        3. Reasons Given for Non-Adoption
        4. Determinants of the Frequency of Use by Cardholders
          1. Technological Factors
          2. Economic Factors
          3. Social Factors
        5. Network Externalities
        6. Network Externalities and Social Influence
      6. CONCLUSION
      7. REFERENCES
      8. ENDNOTES
    5. VI. Engineering Banking Applications: A Service-Oriented Agent-Based Approach
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
        1. Service-Oriented Architecture (SOA)
        2. Software Agent Technology
        3. Integration of SOA and Software Agents
      4. ENGINEERING BANKING APPLICATIONS USING SOAg
      5. SERVICE-ORIENTED METHODOLOGY
        1. Service Granularity
        2. Service Point
        3. Service Composition
        4. Service Collaboration
        5. Service Enactment
      6. A CASE STUDY ON ELECTRONIC CHECK PAYMENT SYSTEM
        1. Agent Environment and the Enactment of Roles
        2. Inter-Agent Communication Model
      7. SERVICE-ORIENTED AGENT ARCHITECTURE
      8. BENEFITS OF THE PROPOSED TECHNOLOGY
      9. CONCLUSION AND CHALLENGES
      10. REFERENCES
    6. VII. Smart Cards in the Banking Industry: Challenges, Competition, and Collaboration in the 2000's
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
        1. Innovations in the Financial Service Industry
        2. What is the Smart Card Innovation?
        3. System Innovation
      4. SMART CARDS ELECTRONIC PAYMENT SYSTEM
      5. CONCLUSION
      6. REFERENCES
      7. ENDNOTES
    7. VIII. Electronic Banking and Information Assurance Issues: Survey and Synthesis
      1. ABSTRACT
      2. INTRODUCTION
      3. INTERNET/WEB BANKING
        1. Web Site and Banking Service Hosting
        2. Internet Banking Platforms and Applications
        3. Standards Compliance
      4. INFORMATION ASSURANCE
        1. Security and Privacy Issues
        2. Authentication
          1. Access Control
          2. Non-Repudiation
          3. Integrity
          4. Confidentiality and Privacy
          5. Availability
          6. Perimeter Defense
          7. Intrusion Detection
          8. Security Event Detection
          9. Malicious Content
        3. Security Services, Mechanisms, and Security Protection
          1. Encryption
          2. Security Protocol Services
          3. Firewalls and Intrusion Detection Systems
          4. Passwords and Personal identification Numbers (PINs)
          5. Tokens
          6. Digital Certificates and Public Key Infrastructure (PKI)
          7. Biometrics
          8. Hardware Security Devices (HSDs)
          9. Industry Standards and Frameworks
        4. User and E-Banking Focus on Security Issues
      5. CONCLUSION
      6. ACKNOWLEDGMENT
      7. REFERENCES
      8. ENDNOTES
    8. A. APPENDIX A
      1. Common Security Protocol Services
    9. B. APPENDIX B
      1. Some Industry Standards and Frameworks in E-Banking
    10. IX. M-Payment Solutions and M-Commerce Fraud Management
      1. ABSTRACT
      2. INTRODUCTION
      3. M-PAYMENT VALUE CHAIN
      4. M-PAYMENT LIFECYCLE
      5. OPERATIONAL ISSUES IN M-COMMERCE PAYMENT
        1. Mobile Payment Systems or Solutions
      6. PAYMENT SOLUTIONS
        1. Paybox
        2. iPIN
          1. Vodafone m-PayBill
          2. m-Pay
        3. Jalda
          1. Other Solutions
      7. GENERAL ANALYSIS OF THE PAYMENT SOLUTIONS
      8. FRAUD MANAGEMENT SYSTEMS IN M-COMMERCE
      9. MOBILE PHONE FRAUD
      10. MOBILE NETWORK FRAUD
      11. M-COMMERCE PAYMENT SPECIFIC FRAUD
        1. Fraud Prevention During Payment Authentication
        2. Fraud During Payment Transaction and Settlement
      12. RESEARCH ISSUES AND CONCLUSION
        1. Research Issues
      13. CONCLUSION
      14. REFERENCES
  7. II. Business Management
    1. X. The CRM Process and the Banking Industry: Insights from the Marketing Literature
      1. ABSTRACT
      2. INTRODUCTION
        1. Factors Driving Business Interest in CRM
        2. CRM's Appeal to Banking
        3. Troubles with CRM
        4. Chapter Objectives and Organization
      3. BACK TO CRM'S ROOTS: MARKETING PERSPECTIVES ON EFFECTIVE CRM
        1. Hark Back to the Marketing Concept
        2. Focus on Needs-Based Customer Segmentation and Positioning
        3. Understand the Effective and Efficient Level of Customer Segmentation
        4. Take Advantage of New Analytical CRM Tools
      4. THE CRM PROCESS: CONCEPTUALIZATION, DEFINITION, AND MEASUREMENT
      5. AN ILLUSTRATION OF THE CRM PROCESS MODEL: LINDGREEN AND ANTIOCO'S (2005) FIRST EUROPEAN BANK CASE STUDY
        1. Background
        2. FEB's Relationship Maintenance Process Implementation
          1. Customer Evaluation
          2. Retention Management
          3. Up-Selling, Cross-Selling, and Referrals
      6. CUSTOMER CO-PRODUCTION AND SELF-SERVICE TECHNOLOGIES IN BANKING
        1. Three Levels of Customer Co-Production: Application to Banking
        2. Research Insights into Customer Use of Banking Self-Services Technologies (SSTs)
        3. Customer Participation in Service Recovery
      7. FUTURE RESEARCH OPPORTUNITIES IDENTIFIED IN THE MARKETING LITERATURE
      8. CONCLUSION
      9. ACKNOWLEDGMENT
      10. REFERENCES
    2. C. APPENDIX
    3. XI. Technology and Customer Value Dynamics in the Banking Industry: Measuring Symbiotic Influence in Growth and Performance
      1. ABSTRACT
      2. INTRODUCTION
      3. REVIEW OF LITERATURE
        1. Electronic Banking vs. Conventional Wisdom
        2. Customer Value Management
      4. FRAMEWORK OF ANALYTICAL CONSTRUCT
        1. Technology and Profit Optimization Equilibrium
        2. Measuring Customer Value
        3. Customer Value Enhancement Through Banking Technology
      5. GENERAL DISCUSSION AND FUTURE DIRECTIONS
      6. CONCLUSION
      7. REFERENCES
    4. XII. Data Warehousing and Analytics in Banking: Concepts
      1. ABSTRACT
      2. INTRODUCTION
        1. Competitive Advantage
          1. Sustainable Competitive Advantage
      3. CUSTOMER KNOWLEDGE AND INSIGHT
        1. Providing the Business Users with an Extract of Data
        2. Allowing Users to Access Operational Data for Analysis
          1. Challenges of These Approaches
        3. Definition of a Data Warehouse
          1. What Is a Data Warehouse?
      4. DATA WAREHOUSE ARCHITECTURE
        1. Source Data Layer
        2. Data Acquisition Layer
        3. Data Management Layer
        4. User Access Layer
      5. QUERY, REPORTING, AND ANALYSIS
      6. ADVANCED ANALYTICS
        1. Closing the Loop
          1. Other Components of Architecture
      7. WHAT IS THE DATA WAREHOUSE USED FOR?
        1. Customer Relationship Management (CRM)
        2. Statutory Compliance
        3. Other Applications
      8. CONCLUSION
      9. ACKNOWLEDGMENT
      10. REFERENCES
    5. XIII. Data Warehousing and Analytics in Banking: Implementation
      1. ABSTRACT
      2. INTRODUCTION
      3. CHALLENGES IN IMPLEMENTATION
      4. CRITICAL SUCCESS FACTORS
      5. IMPLEMENTATION METHODOLOGY
        1. Iterative Methodology
        2. Data Mining Methodology
        3. Agile Development Methodology
      6. DETAILED CASE STUDY
        1. Business Scenario
          1. IT Scenario
          2. Recommendations
          3. Implemented Architecture
          4. Challenges Faced
          5. Key Benefits
      7. OTHER IMPLEMENTATION EXAMPLES
        1. Scotiabank
          1. Leading Bank in South Asia
      8. DOES EVERYONE NEED A DATA WAREHOUSE?
      9. EMERGING TRENDS
        1. Right-Time Enterprise
        2. Business Activity Monitoring
        3. Other Trends
      10. CONCLUSION
      11. ACKNOWLEDGMENT
      12. REFERENCES
    6. XIV. A Reference Model for Savings Bank
      1. ABSTRACT
      2. THE CONCEPTUAL DESIGN OF LEVELS OF THE SKO-DATENMODELL
        1. The A-Level (Architecture View)
        2. The B-Level (Business View)
        3. The C-Level (Logical ERM Organization-Wide)
        4. The C'-Level (Logical ERM Subject Area View)
        5. The D-Level (Physical Database Scheme)
        6. Overall Concepts (Level Independent)
      3. METHODS AND PROCEDURES HANDBOOK AND TOOL SUPPORT
        1. The Methods and Procedures Handbook of the SKO-Datenmodell
        2. Model Management
      4. DEVELOPMENT OF THE DATA MODEL
      5. REFERENCES
  8. III. Risk Management
    1. XV. A Semi-Online Training Algorithm for the Radial Basis Function Neural Networks: Applications to Bankruptcy Prediction in Banks
      1. ABSTRACT
      2. INTRODUCTION
        1. Review of Work Done in Improving RBF Network
      3. SEMI-ONLINE RBFN
        1. Evolving Clustering Method (ECM)
        2. The ECM Algorithm
      4. BANKRUPTCY PREDICTION IN BANKS
      5. OVERVIEW OF TECHNIQUES APPLIED IN CURRENT WORK
        1. Adaptive Neuro Fuzzy Inference System (ANFIS)
          1. TreeNet
          2. SVM
        2. Rough Set-Based classifier (RSES)
        3. Orthogonal RBFN
      6. EXPERIMENTAL METHODOLOGY
        1. Type I and Type II Errors
        2. ROC Curves
        3. Area Under ROC Curves
      7. RESULTS AND DISCUSSION
      8. CONCLUSION AND FUTURE DIRECTIONS
      9. REFERENCES
    2. XVI. Forecasting Foreign Exchange Rates Using an SVR-Based Neural Network Ensemble
      1. ABSTRACT
      2. INTRODUCTION
      3. NEURAL NETWORK ENSEMBLE AS A FOREIGN EXCHANGE RATES FORECASTING TOOL
      4. BUILDING PROCESS OF THE SVR-BASED NEURAL NETWORK ENSEMBLE FORECASTING MODEL
        1. Generating Single Neural Network Predictor
        2. Selecting Appropriate Ensemble Members
        3. Combining the Selected Members
      5. EXPERTMENTAL ANALYSIS
        1. Data Description and Evaluation Criteria
        2. Experimental Results
      6. CONCLUSION AND FUTURE DIRECTIONS
      7. ACKNOWLEDGMENT
      8. REFERENCES
    3. XVII. On the New Transformation-Based Approach to Value-at-Risk: An Application to the Indian Stock Market
      1. ABSTRACT
      2. INTRODUCTION
      3. VALUE-AT-RISK: THE CONCEPT, MEASUREMENT, AND EVALUATION
        1. The Concept
        2. Important Issues While Estimating VaR
        3. Measurement of VaR: Select Available Techniques
        4. Normal (Covariance) Method
        5. Method Using Tail-Index
        6. Hill's Estimator
        7. Estimating VaR Using Tail Index
      4. STRAGIES TO EVALUATE VaR MODELS
        1. Backtesting
        2. Statistical Tests of VaR Accuracy
        3. Evaluation of VaR Models Using Loss-Function
      5. THE NEW TRANSFORMATION-BASED APPROACH TO MEASURING VaR
        1. Basic Premises
        2. Transformations of a Random Variable to Normality
        3. Selection of Transformation to Normality
        4. On Implementing the Transformation-Based Approach
      6. AN APPLICATION TO STOCK MARKET DATA IN INDIA
        1. Data
        2. Competing VaR Models
        3. Empirical Results
        4. Empirical Return Distributions and Transformations to Normality
        5. Estimation of VaR
        6. Empirical Evaluation of VaR Estimates/Models
      7. CONCLUSION
      8. ACKNOWLEDGMENT
      9. REFERENCES
      10. ENDNOTES
    4. XVIII. Data Mining and the Banking Sector: Managing Risk in Lending and Credit Card Activities
      1. ABSTRACT
      2. INTRODUCTION TO RISK ASSESMENT
      3. RISK ASSESSMENT TODAY
        1. Data Warehousing and Credit Risk Modeling
      4. VARIOUS MODELS OF RISK ASSESSMENT
        1. Basic Models to Measure Credit Risk
        2. Early Warning Models
        3. Measuring Loss
        4. Use of Information
      5. FUTURE OF RISK ASSESSMENT
      6. CONSUMER LENDING
      7. CREDIT CARD
        1. Notes:
      8. EDITOR'S NOTES
    5. XIX. Data Mining for Credit Scoring
      1. ABSTRACT
      2. INTRODUCTION
        1. Credit Scoring
      3. BACKGROUND
        1. Fair Isaac Corporation (FICO) Score
        2. Credit Scoring and Data Mining
      4. TYPES OF CREDIT SCORING AND ASSOCIATED MODELS
        1. Credit Scoring for Credit Cards
        2. Credit Scoring for Mortgages
        3. Credit Scoring for Small Business Lending
        4. Data Mining Techniques for Credit Scoring
        5. Model Development
        6. A Study in Default Probability Estimation for Business Lending
        7. Economy-Based Models
        8. Accounting-Based Models
        9. Evaluation
      5. BENEFITS AND LIMITATIONS OF CREDIT SCORING
        1. Limitations of Credit Scoring
      6. FUTURE TRENDS
      7. CONCLUSION
      8. REFERENCES
  9. Compilation of References
  10. About the Contributors