AI and Machine Learning-Enhanced Software Quality Assurance Framework for Business Intelligence in Banking and Healthcare Financial Platforms
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Abstract
This research proposes an AI and Machine Learning (ML)-enhanced Software Quality Assurance (SQA) framework designed to optimize Business Intelligence (BI) processes in banking and healthcare financial platforms. The framework leverages intelligent automation, predictive analytics, and anomaly detection to ensure high software reliability, compliance, and performance. By integrating neural network-based defect prediction models, natural language processing (NLP) for requirement validation, and reinforcement learning for continuous testing optimization, the proposed approach strengthens decision accuracy and operational resilience. The model also incorporates cloud-native CI/CD pipelines and data governance mechanisms, ensuring scalability, interoperability, and regulatory adherence in cross-sector environments. The study demonstrates how AI-driven SQA can improve test coverage, defect detection rate, and time-to-market, advancing the quality and trustworthiness of BI-driven financial services.
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