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Journal : Indatu Journal of Management and Accounting

Credit Card Fraud Detection for Contemporary Financial Management Using XGBoost-Driven Machine Learning and Data Augmentation Techniques Noviandy, Teuku Rizky; Idroes, Ghalieb Mutig; Maulana, Aga; Hardi, Irsan; Ringga, Edi Saputra; Idroes, Rinaldi
Indatu Journal of Management and Accounting Vol. 1 No. 1 (2023): September 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijma.v1i1.78

Abstract

The rise of digital transactions and electronic payment systems in modern financial management has brought convenience but also the challenge of credit card fraud. Traditional fraud detection methods are struggling to cope with the complexities of contemporary fraud strategies. This study explores the potential of machine learning, specifically the XGBoost (eXtreme Gradient Boosting) algorithm, combined with data augmentation techniques, to enhance credit card fraud detection. The research demonstrates the effectiveness of these techniques in addressing imbalanced datasets and improving fraud detection accuracy. The study showcases a balanced approach to precision and recall in fraud detection by leveraging historical transaction data and employing techniques like Synthetic Minority Over-sampling Technique-Edited Nearest Neighbors (SMOTE-ENN). The implications of these findings for contemporary financial management are profound, offering the potential to bolster financial integrity, allocate resources effectively, and strengthen customer trust in the face of evolving fraud tactics.
Artificial Intelligence in Islamic Finance: Forecasting Stock Indices with Neural Prophet Muksalmina, Muksalmina; Idroes, Ghadamfar Muflih; Maulana, Aga
Indatu Journal of Management and Accounting Vol. 2 No. 2 (2024): December 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijma.v2i2.232

Abstract

Ensuring financial system stability is paramount, especially in markets guided by Sharia principles, where investor confidence and adherence to ethical standards play critical roles. The ability to accurately forecast stock movements within this framework not only supports informed investment decisions but also strengthens the overall stability of financial markets. This research employs the innovative Neural Prophet model to predict Islamic stock indices in Indonesia with remarkable accuracy and depth. The model demonstrates its capability not only in accurately forecasting trends but also in detecting subtle fluctuations within three Islamic stock indices: the Jakarta Islamic Index (JII), the Jakarta Islamic Index 70 (JII70), and the Indonesia Sharia Stock Index (ISSI). Visual representations highlight the model's adaptability and advanced foresight, surpassing traditional models. The significance of this research lies in its potential to enhance the precision of stock index predictions, particularly for Islamic stocks, offering stakeholders deeper insights. The model's effectiveness spans both stable and volatile market conditions, making it a valuable tool for informed financial decision-making. Accurate forecasts aid in risk management and support well-informed investment decisions in fluctuating markets, thereby contributing to financial system stability.