Abstract: The banking sector is increasingly leaning on advanced technologies to optimize infrastructure services, particularly through the deployment of Artificial Intelligence (AI) and machine learning methodologies. This movement is driven by the need for enhanced decision-making capabilities, improved operational efficiencies, and robust risk management strategies. Federated Learning emerges as a pivotal framework within this context, allowing institutions to collaboratively train AI models without compromising sensitive customer data. This decentralized approach not only mitigates privacy risks, but also enriches the training datasets, ultimately yielding more accurate and reliable predictive models. Such models are crucial for applications that encompass fraud detection, customer segmentation, and algorithmic trading. Furthermore, the implementation of AI governance frameworks is instrumental in navigating the ethical and regulatory complexities that accompany the integration of AI in banking. Effective governance ensures that AI systems operate transparently, with accountability mechanisms in place to address potential biases and ethical dilemmas. This is particularly important in a sector that manages vast amounts of personal and financial information. By instituting comprehensive oversight policies, banks can foster trust among stakeholders while enhancing compliance with regulatory requirements. The synergy between Federated Learning and AI governance thus not only fortifies the technological backbone of banking IT but also aligns operational practices with ethical standards and consumer protection mandates. This interplay between collaborative AI initiatives and stringent governance encapsulates the future of banking infrastructure. As institutions embrace these innovative solutions, they position themselves to harness the full potential of data-driven insights while safeguarding customer interests. Consequently, the dual focus on optimizing infrastructure services through Federated Learning and enforcing AI governance emerges as a key strategic approach within the banking landscape. This comprehensive framework is essential for navigating the complexities of a rapidly evolving financial ecosystem, ultimately facilitating sustainable growth and enhancing competitive advantage in the marketplace.
Keywords: Autonomous agents, agent-based systems, real-time processing, credit risk assessment, credit scoring, financial decisioning, intelligent systems, machine learning, risk modeling, dynamic data analysis, automated decision-making, adaptive algorithms, transactional data, behavioral analytics, predictive modeling, data-driven insights, financial technology, AI in finance, risk evaluation, creditworthiness analysis.
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DOI:
10.17148/IJARCCE.2023.121224
