The study investigates whether a machine learning algorithm can be used to detect fraud attempts and how a fraud management system based on machine learning might work. For fraud detection, most institutions rely on rule-based systems with manual evaluation. Until recently, these systems had been performing admirably. However, as fraudsters become more sophisticated, traditional systems' outcomes are becoming inconsistent.
Fraud usually comprises many methods that are used repeatedly that's why looking for patterns is a common emphasis for fraud detection. Data analysts can, for example, avoid insurance fraud by developing algorithms that recognize trends and abnormalities. AI techniques used to detect fraud include Data mining classifies, groups, and segments data to search through millions of transactions to find patterns and detect fraud.
The scientific paper discusses machine learning methods to detect fraud detection with a case study and analysis of Kaggle datasets.
Table of Contents
1 Introduction
2 Objective
3 Literature Review
3.1 Related Work
3.2 Machine learning approaches
3.2.1 Logistic Regression
3.2.2 Decision Tree
3.2.3 Working of Decision Tree
3.2.4 Random Forest
3.2.5 Support Vector Machines (SVM)
3.2.6 K-Nearest Neighbours (KNN)
3.2.7 Gradient Boosted Trees
3.2.8 Research Method Data Challenges
3.3 Recent Fraud Cases
3.4 CRISP-DM Model
3.4.1 Business Understanding
3.4.2 Data Understanding
3.4.3 Data Preparation
3.4.4 Data Modelling
3.4.5 Model evaluation
3.4.6 Model Deployment
4 Methodology and Case Study
4.1 Banking Theory
4.2 Data Description
4.3 Data Preparation
4.3.1 Scaling the data
4.3.2 Missing values handling
4.3.3 Dropping NA
4.3.4 Data encoding
4.4 Data Visualisation
4.4.1 Univariate Analysis
4.4.2 Histograms
4.4.3 Boxplot
4.4.4 Bivariate analysis
4.4.5 Correlation
4.4.6 Summary from EDA
4.5 Feature Selection
4.5.1 ANOVA
4.6 Model Comparison and Results
4.6.1 Logistic Regression
4.6.2 Decision Tree
4.6.3 Random Forest
4.6.4 XGBBoost
4.6.5 GradientBoosting
4.6.6 LGBMclassifier
4.7 Classification Evaluation Metrics
4.7.1 Confusion Matrix
4.7.2 Precision
4.7.3 Recall
4.7.4 F1 score
4.7.5 AUC-ROC
4.7.6 Receiver operating characteristic (ROC) Curve
4.7.7 Accuracy
4.7.8 Imbalanced Data
4.8 Possible next steps
5 Summary and Conclusion
Research Objective and Scope
This thesis aims to develop a robust machine learning-based system to detect fraudulent credit card transactions by analyzing historical payment data. The study addresses challenges such as dataset imbalance and provides a systematic comparison of various classification models to optimize fraud detection performance.
- Comparison of machine learning techniques for fraud identification.
- Analysis of credit card payment behavior to assess default risk.
- Implementation of oversampling/undersampling to handle unbalanced datasets.
- Evaluation of classification models using metrics like Accuracy, AUC-ROC, and F1 scores.
Excerpt from the Book
Benefits of machine learning in fraud detection
Modern analytics technologies and systems rely heavily on humans to examine data and discover suspicious transactions and fraud. This reliance is vulnerable to difficulties such as slowness and human mistake. Some of these problems can be solved with the help of machine learning. The following are some of the advantages of machine learning for banks to avoid loss by detecting fraud:
Speed As the speed and volume of eCommerce grow, speed becomes increasingly critical.Machine learning algorithms are capable of analysing large amounts of data in a short duration of time. The model has the capability of collecting and analysing new data in real time if deployed.
Efficiency Algorithms can evaluate huge amounts of payments each second, which is far more work than a team of human analysts could complete in the same amount of time. This decreases down on both the expenses and the time complexity it takes to evaluate transactions, making the process more efficient.Machine learning algorithms are capable of automating repetitive operations and detecting small pattern changes in massive volumes of data. This is crucial for detecting fraud faster than humans.
Summary of Chapters
1 Introduction: Provides an overview of the rising problem of fraud in the banking sector and highlights the necessity for advanced predictive models.
2 Objective: Outlines the research goals, including the identification of the best-performing fraud detection models and the handling of unbalanced data.
3 Literature Review: Discusses existing machine learning approaches and the CRISP-DM methodology used for fraud detection.
4 Methodology and Case Study: Describes the dataset, data preparation, feature selection, and the comparative results of classification algorithms.
5 Summary and Conclusion: Summarizes the key findings and provides recommendations for integrating these models into bank risk management systems.
Keywords
Machine Learning, Fraud Detection, Credit Card Default, CRISP-DM, Logistic Regression, Decision Tree, Random Forest, XGBoost, LGBMclassifier, Data Mining, Fraud Prevention, Risk Management, Classification, AUC-ROC, Imbalanced Data
Frequently Asked Questions
What is the primary focus of this research?
The research focuses on utilizing machine learning algorithms to detect fraudulent banking and credit card transactions to minimize financial losses.
What are the core thematic areas covered?
The core areas include fraud detection theory, the application of various supervised machine learning models, exploratory data analysis of payment behavior, and strategies for managing imbalanced datasets.
What is the main objective of this study?
The objective is to find a high-performing classification model that efficiently identifies potential fraudulent transactions and default payments in complex, real-world datasets.
Which scientific methods are employed?
The study employs the CRISP-DM (Cross-Industry Standard Process for Data Mining) model to structure the research, alongside algorithms like Logistic Regression, Random Forest, SVM, XGBoost, and LGBMclassifier.
What topics are discussed in the main body?
The main body detail the literature review, data description, data cleaning and encoding, Exploratory Data Analysis (EDA), feature selection techniques like ANOVA, and a comparative performance analysis of various ML models.
Which keywords characterize this work?
The work is characterized by terms such as Machine Learning, Fraud Detection, Credit Card Default, Data Mining, Classification, and Risk Management.
How does the study handle the imbalance in credit card data?
The research addresses imbalanced data using techniques like SMOTE (Synthetic Minority Oversampling Technique), undersampling, and oversampling to enhance the detection of the minority fraudulent class.
What makes the LGBMclassifier stand out in this study?
The LGBMclassifier achieved the best results in terms of both accuracy and AUC scores when compared to other supervised learning models like Decision Trees and Random Forests.
- Arbeit zitieren
- Riwaj Kharel (Autor:in), 2022, Machine Learning Approach to Detect Fraudulent Banking Transactions, München, GRIN Verlag, https://www.grin.com/document/1275894
