This study addresses the escalating problem of financial fraud, with a particular focus on credit card fraud, a phenomenon that has skyrocketed due to the increasing prevalence of online transactions. The research aims to strengthen anti-money laundering (AML) systems, thereby improving the detection and prevention of fraudulent transactions. For this study, a Dense Neural Network (DNN) has been developed to predict fraudulent transactions with efficiency and accuracy. The model is based on deep learning, and given the highly unbalanced nature of the dataset, balancing techniques were employed to mitigate the bias towards the minority class and improve performance. The DNN model demonstrated robust performance, generalizability, and reliability, achieving over 99% accuracy across training, validation, and test sets. This indicates the model's potential as a powerful tool in the ongoing fight against financial fraud. The results of this study could have significant implications for the financial sector, corporations, and governments, contributing to safer and more secure financial transactions.
AMLs Deep Learning Dense Neural Networks Financial Fraud Fraud Transaction Detection
This study addresses the escalating problem of financial fraud, with a particular focus on credit card fraud, a phenomenon that has skyrocketed due to the increasing prevalence of online transactions. The research aims to strengthen anti-money laundering (AML) systems, thereby improving the detection and prevention of fraudulent transactions. For this study, a Dense Neural Network (DNN) has been developed to predict fraudulent transactions with efficiency and accuracy. The model is based on deep learning, and given the highly unbalanced nature of the dataset, balancing techniques were employed to mitigate the bias towards the minority class and improve performance. The DNN model demonstrated robust performance, generalizability, and reliability, achieving over 99% accuracy across training, validation, and test sets. This indicates the model's potential as a powerful tool in the ongoing fight against financial fraud. The results of this study could have significant implications for the financial sector, corporations, and governments, contributing to safer and more secure financial transactions.
AMLs Deep Learning Dense Neural Networks Financial Fraud Fraud Transaction Detection
Birincil Dil | İngilizce |
---|---|
Konular | Yazılım ve Uygulama Güvenliği |
Bölüm | Araştırma Makaleleri |
Yazarlar | |
Erken Görünüm Tarihi | 10 Mart 2024 |
Yayımlanma Tarihi | 10 Mart 2024 |
Gönderilme Tarihi | 30 Ocak 2024 |
Kabul Tarihi | 10 Mart 2024 |
Yayımlandığı Sayı | Yıl 2023 |
Journal of Emerging Computer Technologies
is indexed and abstracted by
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Publisher
Izmir Academy Association
www.izmirakademi.org