Thesis Abstract

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Thesis Overview

The research project titled "Analyse comparative des modèles de machine learning pour la prédiction de fraudes bancaires" aims to explore and compare various machine learning models for predicting banking frauds. The project is motivated by the increasing prevalence of fraudulent activities in the banking sector and the need for more effective predictive tools to combat such activities. The introduction section of the study provides an overview of the research topic, highlighting the significance of the study in addressing the challenges of banking fraud. The background of the study delves into the existing literature on banking fraud detection and the role of machine learning in improving predictive accuracy. The problem statement identifies the gaps in current fraud detection methods and emphasizes the need for more robust predictive models. The objectives of the study are outlined to guide the research process towards achieving specific goals, such as evaluating the performance of different machine learning algorithms in fraud prediction. The limitations of the study are acknowledged to provide a realistic assessment of the research scope and potential constraints. The scope of the study defines the boundaries within which the research will be conducted, focusing on specific types of fraud and machine learning techniques. The significance of the study is emphasized in highlighting the potential impact of the research findings on improving banking security and reducing financial losses due to fraud. The structure of the thesis outlines the organization of the research work, including the chapters and their respective contents. Additionally, the definition of key terms clarifies the terminology used in the study to ensure a common understanding among readers. The literature review chapter synthesizes existing research on machine learning models for fraud detection, providing a comprehensive overview of the current state-of-the-art techniques. Various studies and methodologies are reviewed to identify trends, challenges, and opportunities in the field of banking fraud prediction. The research methodology chapter details the approach and methods employed in the study, including data collection, preprocessing, model training, and evaluation. The chapter outlines the experimental setup and validation procedures to ensure the reliability and validity of the research findings. The discussion of findings chapter presents the results of the comparative analysis of machine learning models for fraud prediction, highlighting the strengths and weaknesses of each approach. The chapter discusses the implications of the findings and provides insights into the effectiveness of different models in detecting banking fraud. Finally, the conclusion and summary chapter summarize the key findings of the study, reiterating the research objectives and discussing the implications for future research and practical applications. The chapter concludes with recommendations for improving fraud detection systems in the banking sector based on the research findings.