# Achieving Excellence in Cyber Fraud Detection: A Hybrid ML+DL Ensemble Approach for Credit Cards  
[PERSON]  
1 School of Business, University of Southern Queensland (UniSQ), Springfield, QLD 4300, Australia; [EMAIL_ADDRESS] (X.Z. ); [EMAIL_ADDRESS] (R.G. ); [EMAIL_ADDRESS] (K.C.C. ); [EMAIL_ADDRESS] (O.A.) 1 School of Computing, SRM Institute of Science and Technology, Chennai 603203, India1  
2  
[PERSON]  
1 School of Business, University of Southern Queensland (UniSQ), Springfield, QLD 4300, Australia; [EMAIL_ADDRESS] (X.Z. ); [EMAIL_ADDRESS] (R.G. ); [EMAIL_ADDRESS] (K.C.C. ); [EMAIL_ADDRESS] (O.A.) [PERSON]  
1 School of Business, University of Southern Queensland (UniSQ), Springfield, QLD 4300, Australia; [EMAIL_ADDRESS] (X.Z. ); [EMAIL_ADDRESS] (R.G. ); [EMAIL_ADDRESS] (K.C.C. ); [EMAIL_ADDRESS] (O.A.) 2 School of Computing, SRM Institute of Science and Technology, Chennai 603203, India1  
reach USD 43 billion by 2026. Australia experienced a substantial rise in credit card fraud, with an estimated loss of AUD 2.2 billion in 2023, according to the Australian Bureau of Statistics. These statistics underscore the urgent need for improved fraud detection systems to counter the escalating threat of credit card cyber fraud, particularly as the landscape of online transactions continues to evolve [3]. The incidence of card cyber fraud among Australians saw a substantial increase in 2023. According to a report from the Australian Bureau of Statistics (ABS), an estimated gross amount of $2.2 billion was lost due to card cyber fraud. The proportion of Australians affected by card cyber fraud has increased from 6.9 percent in 2020-2021 to 8.7 percent in 2022-2023. Figure 1 shows the change in Australians' affected by personal cyber fraud from 2020 to 2023 [4]. Detecting credit card cyber fraud is crucial for maintaining financial security, requiring continuous advancement to address evolving fraudulent tactics [5]. Traditional methods have limitations, prompting the exploration of Machine Learning (ML) and Deep Learning (DL) techniques [6; 7] to improve accuracy, adaptability, and performance. However, ML and DL face challenges such as class imbalance, overfitting, and scalability issues [8], along with data-related challenges like unbalanced class distributions and evolving fraud patterns [9]. This paper develops and evaluates a Hybrid ML+DL model to improve fraud detection efficiency and accuracy. The motivation for employing a hybrid ML+DL model in credit card fraud detection stems from the limitations of traditional models and the need to combine the strengths of both ML and DL techniques. ML models, such as Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Logistic Regression (LR) address challenges like imbalanced datasets and complex fraud patterns but struggle with sequential and temporal dependencies in transaction data. DL models like Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory Network (BiLSTM) address these dependencies, uncover hidden relationships, and adapt to varying fraud patterns [10]. The inclusion of attention mechanisms enhances the model's focus on critical features, improving interpretability and accuracy [11]. By combining ML's efficiency with DL's depth, the hybrid model offers a robust, scalable, and highly accurate framework for tackling evolving credit card fraud. The novelty of this approach lies in its integration of both ML and DL techniques into a hybrid ensemble model for cyber fraud detection in the banking sector. Unlike traditional ensemble methods, this model combines classical ML models (e.g., RF, SVM, LR)  
Figure 1: Change in Australians affected by personal fraud. and advanced DL techniques (e.g., CNN-BiLSTM-Attention). This hybridisation allows the model to detect complex fraud strategies, overcoming challenges such as imbalanced datasets, evolving attack patterns, and real-time detection needs.