Integrating Machine Learning with Business Rule Management Systems for Adaptive Enterprise.

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Published: 2023-01-10
DOI: https://doi.org/10.15662/IJRPETM.2023.0601004
Keywords:
Machine Learning, BRMS, Decision Intelligence, Adaptive Enterprises, Artificial Intelligence, Rule Engines, Explainable AI, Decision Automation

Main Article Content

Mallikarjun Bellundagi
Solution Architect, Information Technology, Chags Health Information Technology LLC (C-HIT), USA

Abstract

Modern enterprises operate in increasingly volatile, uncertain, complex, and ambiguous (VUCA) environments, necessitating adaptive decision-making mechanisms that can dynamically respond to changing business contexts. Business Rule Management Systems (BRMS) have traditionally enabled organizations to formalize and automate decision logic; however, their deterministic nature limits adaptability. Conversely, Machine Learning (ML) provides predictive and adaptive capabilities but often lacks transparency, interpretability, and governance. This study proposes a hybrid framework integrating ML with BRMS to enable adaptive enterprise decision-making. Using a design science research methodology, the paper develops and evaluates a multi-layered architecture combining predictive analytics with rule-based governance. A detailed healthcare fraud detection case study demonstrates the effectiveness of the approach. The findings indicate that hybrid systems significantly enhance decision accuracy, agility, and compliance, while mitigating risks associated with opaque AI models.

Article Details

Issue

Vol. 6 No. 1 (2023): International Journal of Research Publications in Engineering, Technology and Management

Section

Articles

How to Cite

Integrating Machine Learning with Business Rule Management Systems for Adaptive Enterprise. (2023). International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(1), 8023-8039. https://doi.org/10.15662/IJRPETM.2023.0601004

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