Intelligent Real-Time Audit Analytics Framework for Automated Manufacturing Finance Operations
DOI:
https://doi.org/10.5281/zenodo.20442266Keywords:
Compliance, smart manufacturing, governance, explainability, data privacy, data lineage, risk mitigation, model risk, continuous controls monitoring, anomaly detection, event correlation, supervision, testing, validation, assurance, mapping, pattern design, and validation cycle.Abstract
Smart manufacturing offers an increasingly integrated and data-driven approach to production, benefitting from the latest technologies like Industry 4.0, Internet of Things (IoT), cloud computing, and Big Data. Real-time artificial intelligence (AI)-enabled auditing and compliance can exploit these technologies to manage compliance in real-time. The advantages of continuous auditing compared with batch auditing are obvious. AI supports the automation of many audit and compliance functions. Although companies have adopted artificial intelligence for various operations, the AI model development process requires considerable resources, time, and expertise. Therefore, the AI-supported real-time audit and compliance framework is within smart manufacturing finance. Key definitions are clarified: Real-Time Audit (RTA), Continuous Controls Monitoring (CCM), AI components (models, data preparation), data lineage, and governance terms. A Continuous Auditing and Continuous Controls Monitoring (CACM) model supports more efficient compliance management and verification. Information Technology Controls and General Application Controls aligned with common regulations can now be automated.
Real-time AI-driven event detection, triggered event correlation, preventive controls, and data-driven tests enhance the framework. Control objectives aligned with mother regulations support validation and assurance. Updated AI model governance principles allow support for production without expertise, acquiring test data during the normal course of business. As datasets accumulate, AI can evolve to operate with minimal specification. With present advances in AI and data-and-compute-storage cloud solutions, the necessary compute and storage resources are available to combine operation and audit seamlessly.
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Data Availability Statement
The paper is conceptual/framework-oriented; illustrative data (e.g., anomaly score charts, bar charts, confusion matrices) appear to be synthetically generated for demonstration purposes, with no mention of publicly available datasets or repositories.
