Enhancing Tax Technology with AI/ML: Monitoring ERP Transactions for Real-Time Compliance and Efficiency

Abstract

In the complex tax environment of today, companies face more stringent legal scrutiny, more data, and evolving compliance criteria. Though they are essential for managing financial transactions for global businesses, Enterprise Resource Planning (ERP) systems sometimes struggle with the complexity and dynamic character of present tax responsibilities. This study looks at how Machine Learning (ML) and Artificial Intelligence (AI), including advanced approaches like deep learning and reinforcement learning, can transform tax compliance systems. Embedding AI/ML within ERP systems lets businesses achieve adaptive compliance, automatic anomaly detection, and real-time transaction monitoring, so significantly reducing risk and human effort. By means of use cases and case studies, this paper demonstrates measurable gains in operational efficiency and compliance accuracy, hence evaluating the efficacy of AI/ML.

Introduction

1. Introduction

Enterprise Resource Planning (ERP) systems like SAP, Oracle Cloud ERP, and Microsoft Dynamics are central to managing financial and tax-related data. Traditional tax compliance methods—relying on static rules and manual audits—struggle to cope with today’s complex, high-volume financial data.

Modernizing these systems with Artificial Intelligence (AI) and Machine Learning (ML) enables:

• Real-time anomaly detection

• Dynamic classification

• Proactive compliance

• Improved accuracy and efficiency

2. Problem Statement

Traditional ERP tax compliance:

• Relies on manual processes and rigid rule engines

• Is inefficient and vulnerable to audits

• Cannot keep up with dynamic tax regulations and massive data volumes

3. Research Objectives

• Analyze how AI/ML can improve real-time monitoring and anomaly detection

• Identify challenges in integrating these technologies into existing ERP tax workflows

4. Literature Review & Challenges

Studies show AI/ML improves tax risk detection and data analysis. Key challenges include:

• Time-intensive manual reviews

• Inflexible rule-based systems

• Unstructured, fragmented data across ERP modules

5. Methodology

Data Sources: General ledger, procurement, A/P, A/R
Data Processing:

• Normalization, feature extraction, and NLP for unstructured data
  Models Used:

• Supervised learning: Classify known transaction types

• Unsupervised learning: Detect unknown anomalies

• Reinforcement learning: Optimize decision-making over time
  Evaluation Metrics: Accuracy, recall, F1-score, adaptability

6. AI/ML Capabilities in ERP Tax Monitoring

AI/ML enhances ERP tax compliance by:

• Detecting fraud, misclassifications, and duplicated entries

• Monitoring jurisdiction-specific thresholds (e.g., EU VAT)

• Providing predictive alerts before compliance breaches occur

• Analyzing invoice text, product codes, and contract language using NLP

• Learning over time via feedback from audits and rule changes

7. System Architecture for AI Integration

A layered system includes:

• Data layer: Structured ERP + unstructured docs

• Feature engineering: Extract tax-relevant info

• Model layer: Neural networks, reinforcement learning

• Orchestration layer: Automates reviews and alerts

• Interface layer: Dashboards, interpretability tools (e.g., SHAP, LIME)

• Integration APIs: Connect to tax engines and filing systems

8. Benefits

• Real-time compliance monitoring

• Reduced manual labor

• Improved accuracy and audit-readiness

• Scalability across jurisdictions and business units

• Adaptive learning through reinforcement learning

• NLP + OCR for smart data extraction from documents

9. Risks and Considerations

• Data privacy (e.g., GDPR)

• Explainability for regulatory/legal clarity

• Change management (training tax professionals)

• Model maintenance (via MLOps for versioning, deployment, accuracy)

10. Case Studies

Infosys:
Automated tax audit proposal generation using AI/ML—reduced manual work and improved consistency.

PwC (Halo):
AI-powered tax optimization—improved reporting accuracy by 20%, lowered compliance costs.

MTN Group:
Used Oracle Cloud EPM + AI to streamline budgeting/tax provisioning across 23 regions—cut prep time by 50%.

11. Key Contributions

• Showcased practical application of AI/ML (NLP, reinforcement learning) in ERP tax workflows

• Proposed a multi-layered architecture for real-time tax compliance

• Demonstrated impact via real-world industry case studies

• Outlined best practices for MLOps and explainable AI in tax governance

Conclusion

ERP-based tax compliance is being transformed for companies by the inclusion of artificial intelligence and machine learning, which changes their management of operational efficiency and regulatory risk. AI-enabled systems can smartly monitor tax-relevant data in real time by means of supervised learning for transaction classification, unsupervised learning for anomaly detection, and reinforcement learning for adaptive decision-making. From automated tax audit proposal generation to predictive tax optimization, industry case study findings show significant reductions in manual effort, enhanced accuracy, and prompt compliance actions. Furthermore, by means of explainable AI models and dashboards, these smart systems provide more transparency, so helping tax experts to grasp and rely on automated results. Although issues still exist—such as preserving data privacy, guaranteeing explainability, and controlling model drift—strong governance systems and MLOps techniques can help to solve these. In the end, companies that include AI/ML technology into their ERP systems will be better able to negotiate the changing tax environment, expand their compliance activities, and achieve long-term strategic advantages. Although issues still exist—such as preserving data privacy, guaranteeing explainability, and controlling model drift—strong governance systems and MLOps techniques can help to solve these. Future studies could look at the creation of AI-driven worldwide tax standards, comparative research across several ERP systems, and the integration of large language models (LLMs) for automated tax document generation.

References

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Copyright

Copyright © 2025 Prashant Gupta, Vinay Singh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.