Integrating machine learning for sustaining cybersecurity in digital banks

Abstract

Cybersecurity continues to be an important concern for financial institutions given the technology's rapid development and increasing adoption of digital services. Effective safety measures must be adopted to safeguard sensitive financial data and protect clients from potential harm due to the rise in cyber threats that target digital organizations. The aim of this study is to investigates how machine learning algorithms are integrated into cyber security measures in the context of digital banking and its benefits and drawbacks. We initially provide a general overview of digital banks and the particular security concerns that differentiate them from conventional banks. Then, we explore the value of machine learning in strengthening cybersecurity defenses. We revealed that insider threats, distributed denial of service (DDoS) assaults, ransomware, phishing attacks, and social engineering are main cyberthreats that are digital banks exposed. We identify the appropriate machine learning algorithms such as support vector machines (SVM), recurrent neural networks (RNN), hidden markov models (HMM), and local outlier factor (LOF) that are used for detection and prevention cyberthreats. In addition, we provide a model that considers ethical concerns while constructing a cybersecurity framework to address potential vulnerabilities in digital banking systems. The advantages and disadvantages of incorporating machine learning into the cybersecurity strategy of digital banks are outlined using strengths, weaknesses, opportunities, threats (SWOT) analysis. This study seeks to provide a thorough knowledge of how machine learning may strengthen cybersecurity procedures, protect digital banks, and maintain customer trust in the ecosystem of digital banking.