Security Risks and Mitigation Strategies in Mobile Banking Applications

Financial services have undergone a change thanks to mobile banking apps, which give consumers quick and easy access to their accounts. Significant security risks, such as phishing attacks, data breaches, and unauthorized access, are associated with this convenience. In order to assess mitigation measures, this study employs a variety of algorithms to explore major security concerns related to mobile banking applications. Intrusion detection and data encryption are our two main areas of concentration.We evaluate the capability of Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) for intrusion detection in terms of their ability to recognize hostile activity. Our findings show that CNNs achieve a 15% accuracy gain in detecting sophisticated attacks compared to SVMs due to their superior capacity to understand complicated patterns. We assess the Elliptic Curve Cryptography (ECC) and Advanced Encryption Standard (AES) algorithms in the field of data encryption. While ECC provides a more effective solution with similar security standards, AES performs better in terms of speed and durability.Based on the findings, mitigation options such as the implementation of hybrid encryption schemes and improved anomaly detection systems are suggested. In order to safeguard mobile banking applications from new dangers, the study emphasizes the significance of integrating diverse security methods.