Detecting financial fraud using Rule-Based Techniques.

Abstract

Financial fraud is a growing problem that poses a significant threat to the banking industry,

the government sector, and the public. In response, financial institutions must continuously

improve their fraud detection systems. While preventive and security measures are put in

place to mitigate financial fraud, criminals persistently adjust and develop new methods to

circumvent fraud prevention systems. This dynamic adaptation poses challenges for quantitative

techniques and predictive models. To address the challenge of unbalanced financial

datasets, this study aims to develop rules to detect fraud transactions and improve accuracy

using Anomaly Reduction Boundary Based Oversampling (ARBBO) method. The performance

of the proposed model is evaluated using various metrics such as accuracy, precision,

recall, f1-score, confusion matrix, and ROC values. The proposed model is compared to

several existing machine learning models such as Random Forest (RF), Decision Tree (DT),

Multi-Layer Perceptron (MLP), K-Nearest Neighbor (KNN), Naive Bayes (NB), and Logistic

Regression (LR) using one benchmark dataset. The experimental results demonstrate that the

classifiers performed better with the resampled data, and the suggested Rule-Based model

with ARBBO in financial fraud detection outperformed then other algorithms by achieving

an accuracy and precision of 0.998 and 0.998, respectively.