Responsible Software Development Framework for Cloud-Native Financial Applications: Leveraging Safe Reinforcement Learning and Ethical AI Governance
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Abstract
The growing reliance on cloud-native architectures for financial applications has accelerated innovation in digital banking, credit assessment, and financial inclusion. However, the increasing integration of autonomous AI agents—particularly those based on reinforcement learning (RL)—raises critical concerns around safety, fairness, and ethical governance. This paper presents a Responsible Software Development Framework (RSDF) designed for cloud-native financial systems that incorporates Safe Reinforcement Learning (Safe-RL) and Ethical AI Governance throughout the software engineering lifecycle. The proposed framework unifies DevSecOps principles, model governance pipelines, and explainable AI (XAI) techniques to ensure transparency, resilience, and regulatory compliance in dynamic financial environments. It introduces a multi-tiered ethical control layer combining human-in-the-loop supervision, bias mitigation, and continuous safety verification for learning-based agents. By leveraging containerized microservices and federated data protocols, RSDF enhances adaptability and security across distributed cloud ecosystems. Case simulations in credit risk modeling and fraud detection demonstrate how Safe-RL agents can optimize decision-making while adhering to fairness and accountability metrics. The results underscore the potential of ethical, AI-driven software engineering to promote sustainable innovation and trust in digital financial infrastructures.
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