A Sequence Mining-Based Novel Architecture for Detecting Fraudulent Transactions in Healthcare Systems

Matloob, Irum; Khan, Shoab Ahmed; Rukaiya, Rukaiya; Khan, Muazzam A.; Munir, Arslan

doi:10.1109/access.2022.3170888

Cited by 15 publications

(11 citation statements)

References 67 publications

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“…SPN excels in intrusion detection with dual systems, margin loss, and nnPU. Healthcare systems face increasing fraudulent billing cases, as discussed in [13]. This work presents a process-based fraud detection methodology using sequence mining concepts to detect insurance claim-related frauds.…”

Section: Detailed Study Of Fraud Detection Methodsmentioning

confidence: 99%

Machine Learning Models for Fraud Detection: A Comprehensive Review and Empirical Analysis

L. K. Vishwamitra

2024

jes

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An in-depth familiarity with ML and DL models for fraud detection is essential due to the growing frequency and complexity of fraudulent activity across many domains. Despite the abundance of research on the subject, empirical analyses of these models, especially in their real-time implementations, are typically lacking. This study fills that need by meticulously reviewing and analysing ML and DL models developed for fraud detection. We draw attention to the shortcomings of existing approaches, which are crucial in the ever-changing field of fraud detection and include problems with recall, scalability, complexity, precision, and accuracy. By evaluating various ML and DL models using these measures, our evaluation method is based on a rigorous empirical approach. Provided that insights into the practical consequences as well as flexibility of each model in real-time circumstances, they thoroughly assess their performance. There are many ways in which this work will be useful; for example, it will help professionals choose the best models for their fraud detection needs, improve academic knowledge of these models in practice, and open the door to more studies that will concentrate on developing better fraud detection tools. This comprehensive research gives academics and companies a foundation for better, more effective and more scalable fraud detection systems in this period of essential digital security.

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Section: Detailed Study Of Fraud Detection Methodsmentioning

confidence: 99%

Machine Learning Models for Fraud Detection: A Comprehensive Review and Empirical Analysis

L. K. Vishwamitra

2024

jes

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“…I N recent years, there has been a significant increase in the volume of financial transactions due to the expansion of financial institutions and the popularity of web-based ecommerce. Fraudulent transactions have become a growing problem in online banking, and fraud detection has always been challenging [1], [2].…”

Section: Introductionmentioning

confidence: 99%

Fraud Detection in Banking Data by Machine Learning Techniques

2023

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As technology advanced and e-commerce services expanded, credit cards became one of the most popular payment methods, resulting in an increase in the volume of banking transactions. Furthermore, the significant increase in fraud requires high banking transaction costs. As a result, detecting fraudulent activities has become a fascinating topic. In this study, we consider the use of class weighttuning hyperparameters to control the weight of fraudulent and legitimate transactions. We use Bayesian optimization in particular to optimize the hyperparameters while preserving practical issues such as unbalanced data. We propose weight-tuning as a pre-process for unbalanced data, as well as CatBoost and XGBoost to improve the performance of the LightGBM method by accounting for the voting mechanism. Finally, in order to improve performance even further, we use deep learning to fine-tune the hyperparameters, particularly our proposed weight-tuning one. We perform some experiments on real-world data to test the proposed methods. To better cover unbalanced datasets, we use recall-precision metrics in addition to the standard ROC-AUC. CatBoost, LightGBM, and XGBoost are evaluated separately using a 5-fold cross-validation method. Furthermore, the majority voting ensemble learning method is used to assess the performance of the combined algorithms. LightGBM and XGBoost achieve the best level criteria of ROC-AUC = 0.95, precision 0.79, recall 0.80, F1 score 0.79, and MCC 0.79, according to the results. By using deep learning and the Bayesian optimization method to tune the hyperparameters, we also meet the ROC-AUC = 0.94, precision = 0.80, recall = 0.82, F1 score = 0.81, and MCC = 0.81. This is a significant improvement over the cutting-edge methods we compared it to.

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“…Applying data mining [1] techniques in order to extract meaningful knowledge from various data types is of great interest, being used for improving decision-making processes in various domains. Machine learning (ML) [2] offers a wide range of models and techniques for uncovering hidden patterns in data from numerous practical domains, such as bioinformatics (for protein dynamics analysis [3], [4]), mete-orology (for precipitation nowcasting and radar data analysis [5], [6]), software engineering (for software structure analysis [7] and restructuring [8], aspect mining [9]), medicine (for clinical decision support [10] and medical data analysis [11]), computer vision (for image analysis [12]), educational data mining (for academic data analysis [13], [14]), etc.…”

Section: Introductionmentioning

confidence: 99%

“…To evaluate the performance of IntelliDaM , we use real data collected at Babeş-Bolyai University, Romania, over three academic years, for a Computer Science (CS) discipline. Besides the proposed framework, the additional contributions envisaged by our study are: (1) to emphasise the effectiveness of IntelliDaM in analysing students' performance-related data; (2) to analyse and interpret, for the considered case study, the relevance of the patterns unsupervisedly mined from academic data and to show how these patterns are correlated with students' academic performance; and (3) to test whether or not the prediction of the students' final performance in a certain academic discipline is enhanced by their results achieved in previous CS courses from the curriculum. Even if it is empirically evaluated on academic data, the proposed IntelliDaM framework is a general one, and it may be applied to any data analysis task.…”

Section: Introductionmentioning

confidence: 99%

IntelliDaM: A Machine Learning-Based Framework for Enhancing the Performance of Decision-Making Processes. A Case Study for Educational Data Mining

et al. 2022

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Nowadays, both predictive and descriptive modelling play a key role in decision-making processes in almost every branch of activity. In this article we are introducing IntelliDaM , a generic machine learning-based framework useful for improving the performance of data mining tasks and subsequently enhancing decision-making processes. Through its components designed for feature analysis, unsupervised and supervised learning-based data mining, IntelliDaM facilitates hidden knowledge discovery from data. Intensive research has been conducted in the field of educational data mining, as education institutions are interested in constantly adapting their educational programs to the needs of society by improving the quality of managerial decisions, course instructors' decision-making, or information gathering for course design. The present work conducts a longitudinal educational data mining study by applying IntelliDaM to real data collected at Babeş-Bolyai University, Romania, for a Computer Science course. The problem of mining educational data has been thoroughly examined using the proposed framework, with the goal of analysing students' performance. A very good performance has been achieved for the classification task (an F 1 score of around 92%), and the results also highlighted a statistically significant performance improvement by using a technique for selecting discriminative data features. The performed study confirmed that IntelliDaM could be a useful instrument in educational environments, particularly for improving decision-making processes, like designing courses, the setup of efficient examinations, avoiding plagiarism, or offering support regarding stress management.INDEX TERMS Data mining, Educational data mining, Machine learning, Students' performance analysis and prediction.

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A Sequence Mining-Based Novel Architecture for Detecting Fraudulent Transactions in Healthcare Systems

Cited by 15 publications

References 67 publications

Machine Learning Models for Fraud Detection: A Comprehensive Review and Empirical Analysis

Machine Learning Models for Fraud Detection: A Comprehensive Review and Empirical Analysis

Fraud Detection in Banking Data by Machine Learning Techniques

IntelliDaM: A Machine Learning-Based Framework for Enhancing the Performance of Decision-Making Processes. A Case Study for Educational Data Mining

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