Scalable Cloud Intelligence for Financial and Enterprise Systems Integrating SAP, Open Banking APIs, and Gradient-Boosted LLM Models for Automated Software Testing

Abstract

In a landscape of rapid digital transformation in both financial and enterprise domains, the demand for scalable, intelligent software testing frameworks is intensifying. This paper proposes an end-to-end cloud intelligent architecture that integrates enterprise resource planning (ERP) systems—specifically SAP S/4HANA—with open banking APIs, and leverages a hybrid machine-learning stack combining gradient-boosted decision tree ensembles and large language model (LLM) components for automated testing of business-critical workflows. The architecture supports continuous integration/continuous delivery (CI/CD) pipelines in the cloud, enabling on-the-fly regression testing, API orchestration testing, load/fuzz testing, and intelligent test-case generation driven by domain models from fintech and enterprise operations. We present the design of a prototype that connects SAP modules (Financials, Treasury, Risk) with external banking APIs, uses XGBoost/LightGBM-style gradient boosting models for anomaly detection in testing outcomes, and LLM-based test-case generation and adaptation. The evaluation shows improvement in fault-detection rates, test-coverage metrics, and reduction in manual test-case maintenance effort by over forty percent compared to traditional scripted frameworks. Key challenges such as API versioning, high-throughput scaling, interpretability of gradient-boosted models, and governance of generative LLM outputs are discussed. The paper contributes a reference architecture, empirical performance data, and lessons learned for enterprises seeking to adopt cloud-native intelligent test automation across SAP and banking integration landscapes.