Enhancing Credit Card Fraud Detection: An Ensemble Machine Learning Approach

In the era of digital advancements, the escalation of credit card fraud necessitates the development of robust and efficient fraud detection systems. This paper delves into the application of machine learning models, specifically focusing on ensemble methods, to enhance credit card fraud detection. Through an extensive review of existing literature, we identified limitations in current fraud detection technologies, including issues like data imbalance, concept drift, false positives/negatives, limited generalisability, and challenges in real-time processing. To address some of these shortcomings, we propose a novel ensemble model that integrates a Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), Bagging, and Boosting classifiers. This ensemble model tackles the dataset imbalance problem associated with most credit card datasets by implementing under-sampling and the Synthetic Over-sampling Technique (SMOTE) on some machine learning algorithms. The evaluation of the model utilises a dataset comprising transaction records from European credit card holders, providing a realistic scenario for assessment. The methodology of the proposed model encompasses data pre-processing, feature engineering, model selection, and evaluation, with Google Colab computational capabilities facilitating efficient model training and testing. Comparative analysis between the proposed ensemble model, traditional machine learning methods, and individual classifiers reveals the superior performance of the ensemble in mitigating challenges associated with credit card fraud detection. Across accuracy, precision, recall, and F1-score metrics, the ensemble outperforms existing models. This paper underscores the efficacy of ensemble methods as a valuable tool in the battle against fraudulent transactions. The findings presented lay the groundwork for future advancements in the development of more resilient and adaptive fraud detection systems, which will become crucial as credit card fraud techniques continue to evolve

Enhancing credit card fraud detection: an ensemble machine learning approach

In the era of digital advancements, the escalation of credit card fraud necessitates the development of robust and efficient fraud detection systems. This paper delves into the application of machine learning models, specifically focusing on ensemble methods, to enhance credit card fraud detection. Through an extensive review of existing literature, we identified limitations in current fraud detection technologies, including issues like data imbalance, concept drift, false positives/negatives, limited generalisability, and challenges in real-time processing. To address some of these shortcomings, we propose a novel ensemble model that integrates a Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), Bagging, and Boosting classifiers. This ensemble model tackles the dataset imbalance problem associated with most credit card datasets by implementing under-sampling and the Synthetic Over-sampling Technique (SMOTE) on some machine learning algorithms. The evaluation of the model utilises a dataset comprising transaction records from European credit card holders, providing a realistic scenario for assessment. The methodology of the proposed model encompasses data pre-processing, feature engineering, model selection, and evaluation, with Google Colab computational capabilities facilitating efficient model training and testing. Comparative analysis between the proposed ensemble model, traditional machine learning methods, and individual classifiers reveals the superior performance of the ensemble in mitigating challenges associated with credit card fraud detection. Across accuracy, precision, recall, and F1-score metrics, the ensemble outperforms existing models. This paper underscores the efficacy of ensemble methods as a valuable tool in the battle against fraudulent transactions. The findings presented lay the groundwork for future advancements in the development of more resilient and adaptive fraud detection systems, which will become crucial as credit card fraud techniques continue to evolve

Crime risk evaluation within information sharing between the Police and community partners.

The aim of this paper is to provide profiles for crimes which can be used to model the context for information sharing between the police and community partner organisations. This context can then be integrated with information-sharing syntax used by Single Point of Contact (SPoC) agents to process information sharing requests [1]. The questionnaires attempt to clas-sify crimes into categories, with identify profiles of crime-types, according to the level of in-formation sharing they necessitate between community partner organisations. Crimes are sepa-rated into classifications, which are based on the perceived level of necessary information-exchange among police and community partners. The aim of the questionnaire is to gather academic responses to identify the level of risk in order that it can be defined as risk assess-ment level, which is key to enhancing the public?s reassurance in the police

A novel Information sharing model using binary decision diagrams for redundancy, shadowing, generalisation and correlation.

This paper outlines a novel information sharing method using Binary Decision Diagrams (BBDs). It is inspired by the work of Al-Shaer and Hamed, who applied BDDs into the modelling of network firewalls. This is applied into an information sharing policy system which optimizes the search of redundancy, shadowing, generalisation and correlation within information sharing rules

Interagency data exchange, privacy protection and governance architecture for Information sharing across domains

This paper discusses privacy enhancing technologies in the field of inter-agency data sharing, a key government objective for efficient public service delivery. We analyse the legal and conceptual framework that governs multi-agency cooperation in particular in the field of child protection work, and develop two approaches to represent relevant data protection ideas computationally in the infrastructure that agencies use to exchange sensitive personal data

Evaluation of binary decision diagrams for redundancy, shadowing, generalisation and correlation in an Information sharing model.

This paper defines a structured methodology which is based on the foundational work of Al-Shaer et al. in [1] and that of Hamed and Al-Shaer in [2]. It defines a methodology for the declaration of policy field elements, through to the syntax, ontology and functional verification stages. In their works of [1] and [2] the authors concentrated on developing formal definitions of possible anomalies between rules in a network firewall rule set. Their work is considered as the foundation for further works on anomaly detection, including those of Fitzgerald et al. [3], Chen et al. [4], Hu et al. [5], among others. This paper extends this work by applying the methods to information sharing policies, and outlines the evaluation related to these