# Achieving Excellence in Cyber Fraud Detection: A Hybrid ML+DL Ensemble Approach for Credit Cards
## 5 Results and Discussion
### Comparison Between Hybrid ML+DL and Individual Techniques:  
The comparison of performance across ML techniques, DL techniques, and the hybrid ML+DL model highlights significant advancements in detecting credit card fraud, particularly when focusing on the F1 score as a key measure of effectiveness. Each approach demonstrates unique strengths, but the hybrid model clearly outperforms standalone methods by leveraging their complementary capabilities.  
Among the evaluated ML algorithms, CatBoost demonstrated the best performance, achieving an F1 score of 86.36%, closely followed by RF with 85.71%. These models also excelled in precision, with CatBoost and RF achieving 97.44% and 97.40%, respectively. However, both struggled with lower recall rates (77.55% and 76.53%), indicating limitations in identifying all fraudulent transactions. Other ML algorithms, such as DT and LR, performed less effectively, with F1 scores of 85.56% and 71.52%, respectively. These results show that, while ML models are highly precise, they may fail to capture a significant portion of fraudulent cases, which is critical in cyber fraud detection.  
DL techniques offered a competitive edge, particularly in handling sequential data and capturing intricate patterns. Among the DL methods, BiLSTM outperformed CNN, achieving an F1 score of 85.86% at larger epoch and batch sizes (50 and 128), compared to CNN's 83.98%. BiLSTM's superior recall of 83.67% highlights its strength in detecting a broader range of fraudulent transactions, making it more reliable in minimising false negatives. On the other hand, CNN demonstrated slightly higher precision (91.57% vs. BiLSTM's 88.17%), indicating better accuracy in correctly identifying fraud but with a slight compromise in recall. Overall, while DL methods show promise, they are most effective when optimised for the characteristics of fraud detection datasets.  
The hybrid ML+DL model achieved the highest overall performance, combining the strengths of both ML and DL techniques. With an F1 score of 90.11%, it surpassed all standalone models, balancing precision (97.62%) and recall (83.67%) more effectively. By integrating ML algorithms, such as RF and CatBoost, with DL architectures like CNN and BiLSTM using a stacking ensemble framework, the hybrid model leveraged their complementary strengths. Its superior accuracy (99.97%) and robust handling of imbalanced datasets demonstrate its ability to detect fraudulent transactions more reliably than individual methods. The hybrid approach's success highlights its ability to mitigate the weaknesses of standalone models while capitalising on their unique advantages.  
The analysis demonstrates that, while ML and DL techniques independently offer strong capabilities, they are limited in their ability to address all aspects of credit card fraud detection. The hybrid ML+DL model, by combining the precision of ML with the sequential data processing capabilities of DL, delivers the most balanced and effective solution. This underscores the potential of hybrid approaches to set a new benchmark in fraud detection, ensuring higher accuracy, better generalisation, and improved robustness in real-world applications.  
The novel hybrid stacking ML+DL model demonstrates superior performance compared to the most advanced models in terms of accuracy and F1 score on the European dataset. While some models achieve high accuracy individually, the novel hybrid approach, which combines ML and DL through stacking, achieves the highest accuracy of 99.97% and a competitive F1 score of 90.11%. This suggests that the integration of both ML and DL techniques in a stacking framework enhances the model's predictive capabilities, offering promising results for card cyber fraud detection. Table 7 shows a comparison of performance with existing models.