Implementation of Data Mining in Detecting Financial Transaction Anomalies in the Company

Fitria; Emy Iryanie; Heldalina; Aneta Rakhmawati

doi:10.59890/ijels.v3i5.47

Authors

Fitria Politeknik Negeri Banjarmasin
Emy Iryanie Politeknik Negeri Banjarmasin
Heldalina Politeknik Negeri Banjarmasin
Aneta Rakhmawati Politeknik Negeri Banjarmasin

DOI:

https://doi.org/10.59890/ijels.v3i5.47

Keywords:

Data Mining, Decision Tree, Anomaly Detection, Financial Transactions, Fraud

Abstract

This study aims to evaluate the application of the decision tree algorithm in detecting anomalies in corporate financial transactions, focusing on the identification of unbalanced fraudulent transactions. The data imbalance between normal transactions and fraud is often a major challenge in fraud detection systems. This study uses a quantitative approach with an experimental design, where the company's financial transaction data is processed and analyzed using a decision tree algorithm. The analysis techniques applied include data preprocessing, normalization, and model evaluation based on accuracy, precision, and recall. The results showed that although the decision tree was effective in identifying fraudulent transactions, the model was more likely to classify transactions as normal (false negatives), which shows the importance of addressing data imbalances to improve model performance

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