| Titre : | Detectingfinancial fraud using machine learning techniques |
| Auteurs : | Bouazza Bouchra Hmidia, Auteur ; SEG-P تاهي عبد الرحمان, Directeur de thèse |
| Type de document : | texte imprimé |
| Editeur : | SAIDA [ALGERIE] : 1- جامعة سعيدة الدكتور مولاي الطاهر / كلية العلوم الاقتصادية والتجارية وعلوم التسيير, 2025-2026 |
| Format : | 100p / 27cm |
| Accompagnement : | +CD-ROM |
| Langues: | Français |
| Catégories : | |
| Note de contenu : |
INTRODUCTION ______________________________________________________ 12
CHAPTER I ____________________________________________________________ 1 FUNDAMENTALS OF FINANCIAL FRAUD AND CREDIT CARD TRANSACTIONS _______________________________________________________ 1 1.1 INTRODUCTION __________________________________________________ 4 1.2 Financial Fraud :Definition and concepts ___________________________________________ 4 1.2.1 Definition of Financial Fraud ___________________________________________________ 4 1.2.2 Characteristics and Elements of Financial Fraud___________________________________ 6 1.2.3 Types of Financial Fraud in Financial Institutions__________________________________ 7 1.2.4 Causes and Motivations behind Financial Fraud ___________________________________ 8 1.2.5 Economic and Financial Impacts of Financial Fraud________________________________ 9 1.3 Classification of Financial Fraud in Financial Institutions ____________________________ 10 1.3.1 Internal vs. External Fraud____________________________________________________ 11 1.3.2 Online and Offline Fraud _____________________________________________________ 13 1.3.3 Credit Card Fraud vs. Other Financial Frauds ___________________________________ 14 1.4 Credit Card Systems and Transaction Lifecycle _____________________________________ 16 1.4.1 Architecture of Credit Card Systems____________________________________________ 16 1.4.2 Processing Workflow and Stages _______________________________________________ 17 1.4.3 Authorization, Clearing, and Settlement _________________________________________ 18 1.5 Credit Card Fraud: Concepts and Types___________________________________________ 19 1.5.1 Definition of Credit Card Fraud________________________________________________ 20 1.5.2 Common Techniques Used in Credit Card Fraud _________________________________ 21 1.6 Challenges in Detecting Financial and Credit Card Fraud ____________________________ 22 1.6.1 Data Imbalance and Concept Drift______________________________________________ 22 1.6.2 Real-Time Detection Constraints _______________________________________________ 23 1.6.3 Privacy, Security, and Ethical Issues ____________________________________________ 23 1.7 Economic and Financial Impacts of Fraud _________________________________________ 24 1.7.1 Financial and Institutional Losses ______________________________________________ 24 1.7.2 Consequences for Customers and Market Confidence______________________________ 25 1.7.3 Legal and Regulatory Implications _____________________________________________ 25 1.8 Conclusion____________________________________________________________________ 27 CHAPTER 2THEORETICAL FOUNDATIONS OF FINANCIAL FRAUD DETECTION: FROM CLASSICAL APPROACHES TO MACHINE LEARNING METHODS ____________________________________________________________ 28 1.1 INTRODUCTION _________________________________________________ 29 1.2 Classical Statistical Techniques___________________________________________________ 30 1.2.1 Rule-Based Systems (Expert Systems) ___________________________________________ 30 1.2.2 Statistical Hypothesis Testing __________________________________________________ 30 1.3 Challenges of Traditional Fraud Detection Methods _________________________________ 31 1.4 Machine Learning in Fraud Detection _____________________________________________ 32 1.4.1 Introduction to Machine Learning and Its Role in Fraud Detection___________________ 32 1.5 Machine Learning Algorithms in Fraud Detection ___________________________________ 36 1.5.1 Commonly Used Algorithms in Fraud Detection ________________________________ 36 1.5.2 Preprocessing Financial Data before Applying Algorithms__________________________ 41 1.6 Model Evaluation Metrics _______________________________________________________ 42 1.6.1 Methods for Evaluating Model Performance _____________________________________ 43 1.7 Ethical and Legal Considerations in Using Machine Learning _________________________ 44 1.8 Comparative Analysis: Classical vs. Machine Learning Approaches ____________________ 45 1.9 Conclusion____________________________________________________________________ 47 CHAPTER 3 PRACTICAL IMPLEMENTATION OF A CREDIT CARD FRAUD DETECTING SYSTEM USING MACHINE LEARNING _____________________ 48 1.1 INTRODUCTION: ________________________________________________ 49 Scope and Limitations:____________________________________________________________ 50 .2 LITERATURE REVIEW_____________________________________________ 51 2.1 Credit Card Fraud: A Global Challenge ___________________________________________ 51 2.2 Traditional Fraud Detection Methods: ____________________________________________ 51 2.2.1 Rule-Based System (Threshold-Based Detection): _________________________________ 52 2.2.2 Z-Score Method (Standard Deviation Approach): _________________________________ 52 2.2.3 Benford’s Law:______________________________________________________________ 54 2.3 Machine Learning Approaches in Fraud Detection __________________________________ 56 2.4 Class Imbalance Problem in Fraud Detection _______________________________________ 57 2.5 PCA in Financial Data Privacy ___________________________________________________ 57 2.6 Synthesis of Contemporary Research______________________________________________ 58 2.6.1. Performance of Standard Classifiers __________________________________________ 58 2.6.2. Smote-Based Approaches ___________________________________________________ 59 2.6.3. Boosting and Hybrid Approaches ____________________________________________ 59 2.7 Comparative Analysis of Previous Studies__________________________________________ 60 2.7.1 Summary Insight ____________________________________________________________ 61 2.8 Research Gap and Problem Statement_____________________________________________ 61 .3 METHOD__________________________________________________________ 62 3.1 Research Approach: ___________________________________________________________ 62 3.2 System Design Overview ________________________________________________________ 62 3.3 System Architecture :___________________________________________________________ 63 3.2.1 The Simulation Bridge Architecture ____________________________________________ 63 3.3.2 High-Level Architecture ______________________________________________________ 64 3.3.3 Backend Architecture (FastAPI) _______________________________________________ 64 3.3.4 Frontend Overview ________________________________________________________ 65 3.3.5 Data Flow Process _________________________________________________________ 65 3.4 Data Collection ________________________________________________________________ 66 3.5 Data Preprocessing_____________________________________________________________ 67 3.6 Model Training Pipeline ________________________________________________________ 68 3.7 Model Descriptions_____________________________________________________________ 68 3.8 Backend API Implementation____________________________________________________ 69 3.9 PDF Processing________________________________________________________________ 69 3.10 Frontend Implementation _______________________________________________________ 69 4. SYSTEM IMPLEMENTATION. ______________________________________ 70 4.1 Technology Stack.______________________________________________________________ 70 4.2 System Evaluation _____________________________________________________________ 70 4.2.1 Functional Evaluation of the System __________________________________________ 70 4.2.2 Model Performance Overview _______________________________________________ 71 4.2.3 Ensemble Analysis ___________________________________________________________ 71 4.2.4 System Performance (Descriptive Analysis):______________________________________ 71 .5 RESULT AND ANALYSIS ___________________________________________ 71 5.1 Model Training Results _________________________________________________________ 71 5.2 Cross-Validation Performance ___________________________________________________ 72 5.3 Ensemble Voting Analysis _______________________________________________________ 73 5.4 System Performance____________________________________________________________ 74 .6 DISCUSSION ______________________________________________________ 74 6.1 Strengths of the Approach_______________________________________________________ 74 6.2 Limitations ___________________________________________________________________ 75 6.3 Ethical Considerations__________________________________________________________ 75 7.3 DISCUSSION_____________________________________________________ 76 7.1 Strengths of the Approach_______________________________________________________ 76 7.2 Limitations ___________________________________________________________________ 76 7.3 Ethical Considerations__________________________________________________________ 77 8. CONCLUSION AND FUTURE WORK_________________________________ 77 8.1 Conclusion____________________________________________________________________ 77 8.2 Future Work__________________________________________________________________ 78 GENERAL CONCLUSION ______________________________________________ 80 BIBLIOGRAPHY_______________________________________________________ 81 |
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