Intelligent Continuous Integration and Delivery for Banking Systems using Machine Learning Driven Risk Detection with Real World Deployment Evaluation
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I4P118Keywords:
Continuous Integration, Machine Learning, Risk Detection, Banking Systems, Deployment AutomationAbstract
The pressures to keep this system reliable while increasing the software release cycles have never been greater in the banking sector. This study explores the incorporation of risk detection mechanisms utilizing machine learning into CI/CD pipelines for banking systems. It focuses on how deployment safety for financial institutions can be improved and operational risks reduced via balance using various artificial intelligence techniques as it addresses the question that concerns authorities and the general public: due to the fact that financial institutions are subject to highly regulated and risk-averse environments, how can regulatory compliance be enforced and achieved with the use of artificial intelligence? We used a mixed-method approach integrating a quantitative analysis of deployment metrics and qualitative analysis of risk patterns across multiple banks. This hypothesis stated that machine learning methods should be able to use little to no deployment failures and should be less vulnerable to security issues than traditional methods. In results, there was a 67% reduction in production incidents, 82% increase in risk prediction accuracy, and 45% improvement in deployment cycles. The ML-driven risk scores were statistically associated with the actual deployment outcomes. These results demonstrate that smart CI/CD systems can significantly improve banking workflows without compromising on code security. This research provides valuable frameworks for deploying AI-driven deployment strategies at scale at what framework(AI) in regulated financial environments as well as providing insights for digital transformation initiatives
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