Intelligent CI/CD Pipelines Using AI-Based Risk Scoring for FinTech Application Releases

Abstract

The quick evolution of software engineering in the area of financial technology necessitates not only frequent deployments but also the strictest standards in reliability and security at the same time. The classical pipelines for continuous integration and continuous deployment (CI/CD) depend significantly on the static rules and manual approval gates and therefore take quite a long time to deliver without risk assessment for the deployments. The present study puts forth an advanced CI/CD model that is based on the use of AI to predict the risk of deployment relying on historical data and the application of anomaly classification models. The system provides automated deployment decisions by producing risk scores based on the analysis of deployment patterns, code changes, testing metrics and production incidents. The study applies a mixed-methods strategy that integrates the development of a framework, historical data analysis and a comparative evaluation in several FinTech deployment scenarios. The outcomes illustrate that the AI-based risk scoring system lessens production incidents by 47% with a corresponding increase in deployment frequency of 34% compared to the traditional approval processes. The framework demonstrates 89% accuracy in predicting high-risk deployments and keeping the false positive rates below 8%. These results are of great consequence for the DevOps practices in the regulated sectors where it is imperative to find the balance between speed and safety. This research not only lays down the theoretical groundwork but also gives practical guidelines for the intelligent deployment system that makes software delivery both agile and