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A probabilistic approach driven credit card anomaly detection with CBLOF and isolation forest models

Alexandria Engineering Journal, ISSN: 1110-0168, Vol: 114, Page: 231-242
2025
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Article Description

Businesses throughout the world are starting to accept more and more electronic payment alternatives for in-person and online purchases. Unusual behaviour such as internet fraud and payment defaults, which may lead to significant monetary losses, have become increasingly widespread as credit card use in online buying has expanded. To find a solution to this problem, researchers looked into a number of different machine learning classifiers that may identify irregularities in the data pertaining to credit card transactions. Overlapping class samples and an uneven distribution of classes make it hard to find outliers in this data. General learning algorithms may favour samples from the majority class, and the detection rate of anomalies in samples from minority classes is thus low. Our proposed Credit Card Outlier Detection (CCOD) model incorporates many machine learning algorithms to enhance the detection rates of credit card anomalies. Utilising the stratified sampling technique and the k-fold cross-validation process, we address data imbalance and overfitting. Working with optimized selected features rather than all features reduces the danger of overfitting and improves model performance. It has been noted that the findings of Cluster-Based Local Outlier Factor (CBLOF) classifier on European Credit Card Dataset and Isolation Forest classifier on German credit card dataset were superior to those of the other classifiers with Mathew correlation coefficient value of 0.95 and 0.97 respectively. The CBLOF, an advanced outlier detection method that evaluates the local density of data points within clusters. By comparing the density of a point to the densities of its surrounding clusters, CBLOF identifies outliers based on their deviation from normal cluster behaviour. In general, this CCOD model shows that it is better at dealing with issues like uneven class distribution, overfitting, and classes that overlap.

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