The rapid evolution of technology has revolutionized the financial industry with digital banking and financial services becoming increasingly prevalent. The prevailing trend in the contemporary financial services sector centers around the transition to digital platforms, particularly mobile and online banking. In an age marked by unparalleled convenience and speed, consumers no longer prefer visiting physical bank branches for their transactions. As banks strive to introduce new features to attract and retain customers, disruptive banking technologies from startups and neo banks are emerging. The integration of artificial intelligence (AI) and machine learning (ML) in the banking sector holds the potential to transform operational processes and enhance services which leads to improved efficiency, productivity and customer experience. This chapter explores the role of AI and ML in addressing information privacy and security concerns in the arena of digital banking and financial services in digital age.
TopIntroduction And Background
The cloud platforms are essential to modern organizations in order to grow, stay flexible and boost productivity (Ray et al., 2024). However, there is an increased danger of cybersecurity risks with the growing trend of cloud adoption (Dinesh Arokia Raj et al., 2024). Cyberattacks targeting cloud infrastructure can provide significant risks to data confidentiality, security and uptime (Yue & Shyu, 2024). Organisations utilising cloud services face a serious danger from adversaries that possess a deep grasp of cloud-specific features (Mithas et al., 2022). These actors are known as “cloud-conscious” attacker and there was a significant 95% rise in cloud exploitation cases compared to 2022 (Ivanov et al., 2019). The number of events where attackers targeted cloud settings nearly tripled, indicating a 288% increase in yearly rates (Munirathinam, 2020). This increasing trend illustrates a larger pattern in which nation-state actors and cybercriminals modify their methods and expertise to more successfully take use of cloud systems (Javaid et al., 2022). These days, adversaries concentrate on breaking into endpoints and exploiting access to go into the cloud, transforming it into a vital theater of operations for stopping security lapses (Trung et al., 2021).
There is a pressing need for creative ways to safeguard sensitive data since sophisticated cloud infrastructures are more vulnerable to cyberattacks (Trakadas et al., 2020) (Radanliev et al., 2021). Businesses must have sophisticated security measures in place to guard against such intrusions and guarantee the integrity of their operations (Sigov et al., 2022) (Rymarczyk, 2020). To address security concerns, organizations are adopting more sophisticated machine learning (ML) and artificial intelligence (AI) technologies (Mhlanga, 2020). These technological advancements are essential for bolstering cybersecurity defenses against ever-changing threats. Businesses may improve their capacity to identify and address cyber risks in a number of ways by employing AI and ML tools (Javaid et al., 2020). These include thwarting hostile AI, reacting to threats instantly, and anticipating possible future assaults (Ahmad et al., 2022). Employing machine learning algorithms makes it possible to spot irregularities and potential security breaches, giving companies early warning systems that can help avert disastrous data breaches (Sakhawat et al., 2024). Businesses’ security architectures become increasingly complicated as they implement multi-cloud platforms to handle a variety of workloads (Karisma, 2024). In particular, generative AI is a powerful tool for defenders, allowing them to automate repetitive operations and use generative processes to improve their abilities, reduce time and boost speed (Sobb et al., 2020). With increasing productivity in security operations centers, automation helps to implement cybersecurity measures that are more effective and efficient (Wan et al., 2020).