The current financial services sector is realising considerable changes in its operations due to development in technology and embracing of digital platforms. This evolution is changing the established concepts of business, consumers and channels of delivery of services. Financial services firms are changing the way they work through digital transformation due to developments in technology, changes in customer needs, and an increase in emphasis on sustainability. Understanding the opportunities, risks, and new trends in digital transformation is the focus of this paper. Opportunities include efficient real-time decision-making processes, increased transparency and better process controls, which are balanced by the threats of change management, dubious organization-technology fit, and high implementation costs. The study also examines recent advancements, including the application of machine learning and artificial intelligence, developments in mobile and online banking, integration of blockchain, and increasing focus on security and personalised banking. A literature review yields some findings from different studies on rural financial services, the evolution of the blockchain, drivers of digital transformation, cloud-based learning approaches, and emerging sustainability practices. All of these results suggest that more strategic planning, analytics, and more focus on ensuring that organisational objectives are met with transformations should be pursued. Hence, this research findings add to the existing literature in determining how innovative and digital technologies are likely to transform the financial services sector and advance sustainability.

Deep learning approaches are very useful to enhance cybersecurity protocols for industry-integrated big data enterprise resource planning systems. This research study develops deep learning architectures of variational autoencoder, sparse autoencoder, and deep belief network for detecting anomalies, fraud, and preventing cybersecurity attacks. These cybersecurity issues occur in finance, human resources, supply chain, and marketing in the big data integrated ERP systems or cloud-based ERP systems. The main objectives of this creative research work are to identify the vulnerabilities in various ERP systems, databases, and the interconnected domains; to introduce a conceptual cybersecurity network model that incorporates variational autoencoders, sparse autoencoders, and deep belief networks; to evaluate the performance of the proposed cybersecurity model by employing the appropriate parameters with real-time and synthetic databases and simulated scenarios; and to validate the model performance by comparing it with traditional algorithms. A big data platform with an integrated business management system is known as an integrated ERP system, which plays an instrumental role in conducting business for various organizations in society. In recent times, as uncertainty and disparity increase, the cyber ecosystem becomes more complex, volatile, dynamic, and unpredictable. In particular, the number of cyber-attacks is increasing at an alarming rate; the resultant security breaches have a disruptive and disturbing effect on businesses around the world, with a loss of billions of dollars. To combat these threats, it is essential to develop a conceptual cybersecurity network model to secure systems by functioning as a mutually supporting and strengthening network model rather than working in isolation. In this dynamic and fluid environment, introducing a deep learning approach helps to support and prevent fraud and other illicit activities related to human resources and the supply chain, among others. Some cybersecurity vulnerabilities include, for example, database vulnerabilities, service level vulnerabilities, and system vulnerabilities, among others. The proposed methodology focuses only on database vulnerabilities, with the main aim of detecting and mitigating new potential vulnerabilities in other dependent domains as a future initiative.

The most prevalent technique behind security data breaches exists through SQL Injection Attacks. Organizations and individuals suffer from sensitive information exposure and unauthorized entry when attackers take advantage of SQL injection (SQLi) attack vulnerability’s severe risks. Static and heuristic defense methods remain conventional detection tools for previous SQL injection attacks study's foundation is a detection system developed using the Gated Recurrent Unit (GRU) network, which attempts to efficiently identify SQL Injection attacks (SQLIAs). The suggested Gated Recurrent Unit model was trained using an 80:20 train-test split, and the results showed that SQL injection attacks could be accurately identified with a precision rate of 97%, an accuracy rate of 96.65%, a recall rate of 92.5%, and an F1-score of 94%. The experimental results, together with their corresponding confusion matrix analysis and learning curves, demonstrate resilience and outstanding generalization ability. The GRU model outperforms conventional machine learning (ML) models, including K-Nearest Neighbor’s (KNN), and Support Vector Machine (SVM), in terms of identifying sequential patterns in SQL query data. Recurrent neural architecture proves effective in the detection of SQLi attacks through its ability to provide secure protection for contemporary web applications.