Securing Tomorrow: AI-Driven Threat Detection and Response in Banking Applications

The increasing sophistication of cyberattacks targeting financial institutions necessitates advanced threat detection mechanisms to safeguard sensitive banking data and prevent financial losses. Traditional methods often fall short in addressing evolving threats such as fraud, phishing, and ransomware, making it crucial for banking systems to adopt real-time, efficient, and scalable solutions. This research proposes a hybrid deep learning model, combining Bidirectional Long Short-Term Memory with an Autoencoder for anomaly detection in banking transactions. The model utilizes the strengths of sequence modeling and unsupervised learning to detect fraudulent activities by analyzing transactional data patterns. A comprehensive dataset of 2,512 simulated banking transactions was used. The model achieved impressive performance metrics, with 98.51% validation accuracy, 97.01% precision, 96.37% recall, and 96.68% F1 score, indicating robust detection capability. The Receiver Operating Characteristic curve further validated the model's effectiveness, with an Area Under the Curve of 0.971, demonstrating its superior ability to distinguish between legitimate and fraudulent transactions. These results suggest that the BiLSTM-Autoencoder hybrid model offers a reliable, high-performance solution for real-time threat detection in banking applications, ensuring both security and customer trust in increasingly complex cybersecurity landscapes.