Automated Risk Scoring in Oracle Fusion ERP Using Machine Learning
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V5I4P111Keywords:
Oracle Fusion ERP, Machine Learning, Risk Scoring, Procurement, Financial Risk, Fraud Detection, AI in ERP, ComplianceAbstract
Enterprise Resource Planning (ERP) systems, such as Oracle Fusion ERP, serve as the backbone of financial and operational processes for many organizations. These systems manage vast amounts of data, making them ripe for risk assessment initiatives. However, traditional rule-based risk evaluation mechanisms within ERP systems can be static, slow to adapt, and limited in scope. This paper presents an automated, intelligent risk scoring framework using machine learning (ML) techniques integrated within Oracle Fusion ERP. The framework aims to assess risks associated with transactions, suppliers, and employees across financial and procurement modules. We developed and evaluated several ML models using historical ERP datasets and tested their performance using key metrics like precision, recall, and F1-score. Feature engineering focused on contextual ERP attributes like transaction frequency, supplier ratings, fraud indicators, and user behavior anomalies. The results demonstrate the potential of AI to transform risk management by enabling dynamic, real-time risk scoring within ERP systems. This approach improves decision-making, reduces financial fraud, and ensures better compliance with regulatory frameworks. The paper concludes with an analysis of implementation challenges, integration strategies, and the future scope of AI-driven risk scoring in enterprise environments
References
[1] Bejar, I. I. (2017). Threats to the score's meaning in automated scoring. Validation of score meaning for the next generation of assessments, 75-84.
[2] Williamson, D. M., Bejar, I. I., & Sax, A. (2004). Automated tools for subject matter expert evaluation of automated scoring. Applied Measurement in Education, 17(4), 323-357.
[3] Citron, D. K., & Pasquale, F. (2014). The scored society: Due process for automated predictions. Wash. L. Rev., 89, 1.
[4] Parimi, S. S. (2019). Automated Risk Assessment in SAP Financial Modules through Machine Learning. Available at SSRN 4934897.
[5] Rondy Ng et al. (2019) – Oracle extends AI within ERP Cloud and EPM Cloud, including expense reporting assistant, project management digital assistant, advanced financial controls, and supply chain management enhancements.
[6] Aloini, D., Dulmin, R., & Mininno, V. (2012). Risk assessment in ERP projects. information systems, 37(3), 183-199.
[7] Zeng, Y., & Skibniewski, M. J. (2013). Risk Assessment for Enterprise Resource Planning (ERP) System Implementations: A Fault Tree Analysis Approach. Enterprise Information Systems, 7(3), 332-353.
[8] Iskanius, P. (2009). Risk Management in ERP Project in the Context of SMEs. Engineering Letters, 17(4).
[9] Dey, P. K., Clegg, B., & Cheffi, W. (2013). Risk management in enterprise resource planning implementation: a new risk assessment framework. Production Planning & Control, 24(1), 1-14.
[10] Al-Ghofaili, A. A., & Al-Mashari, M. A. (2014, August). ERP system adoption: traditional ERP systems vs. cloud-based ERP systems. In the Fourth edition of the International Conference on the Innovative Computing Technology (INTECH 2014) (pp. 135-139). IEEE.
[11] Boppana, S. B., Moore, C. S., Bodepudi, V., Jha, K. M., Maka, S. R., & Sadaram, G. (2021). AI And ML Applications In Big Data Analytics: Transforming ERP Security Models For Modern Enterprises.
[12] Zhang, H. (2022). A deep learning model for an ERP enterprise financial management system. Advances in Multimedia, 2022(1), 5783139.
[13] Williamson, D. M., Xi, X., & Breyer, F. J. (2012). A framework for evaluation and use of automated scoring. Educational measurement: issues and practice, 31(1), 2-13.
[14] Poba-Nzaou, P., & Raymond, L. (2011). Managing ERP system risk in SMEs: A multiple case study. Journal of Information Technology, 26(3), 170-192.
[15] Arvidsson, J., & Kojic, D. (2017). Critical Success Factors in ERP Implementation: The Perspective of the Procurement System User.
[16] Oracle updates ERP and EPM with machine learning, finance reporting, AI risk management, and unified projects. ItBrief, 2021. online. https://itbrief.co.nz/story/oracle-updates-erp-and-epm-with-machine-learning-finance-reporting-ai-risk-management-unified-projects
[17] Parisa Golbayani, Ionuț Florescu, Rupak Chatterjee (2020) – A comparative study of forecasting Corporate Credit Ratings using Neural Networks, Support Vector Machines, and Decision Trees.
[18] Gonugunta, K. C. (2018). Apply Machine Learning Oracle Analytics–Combined. The Computertech, 37-44.
[19] Linwei Hu, Jie Chen, Joel Vaughan, Hanyu Yang, Kelly Wang, Agus Sudjianto, Vijayan N. Nair (2020) – Supervised Machine Learning Techniques: An Overview with Applications to Banking.
[20] Rusum, G. P., Pappula, K. K., & Anasuri, S. (2020). Constraint Solving at Scale: Optimizing Performance in Complex Parametric Assemblies. International Journal of Emerging Trends in Computer Science and Information Technology, 1(2), 47-55. https://doi.org/10.63282/3050-9246.IJETCSIT-V1I2P106
[21] Pappula, K. K., & Anasuri, S. (2020). A Domain-Specific Language for Automating Feature-Based Part Creation in Parametric CAD. International Journal of Emerging Research in Engineering and Technology, 1(3), 35-44. https://doi.org/10.63282/3050-922X.IJERET-V1I3P105
[22] Rahul, N. (2020). Optimizing Claims Reserves and Payments with AI: Predictive Models for Financial Accuracy. International Journal of Emerging Trends in Computer Science and Information Technology, 1(3), 46-55. https://doi.org/10.63282/3050-9246.IJETCSIT-V1I3P106
[23] Enjam, G. R. (2020). Ransomware Resilience and Recovery Planning for Insurance Infrastructure. International Journal of AI, BigData, Computational and Management Studies, 1(4), 29-37. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V1I4P104
[24] Pappula, K. K., Anasuri, S., & Rusum, G. P. (2021). Building Observability into Full-Stack Systems: Metrics That Matter. International Journal of Emerging Research in Engineering and Technology, 2(4), 48-58. https://doi.org/10.63282/3050-922X.IJERET-V2I4P106
[25] Rahul, N. (2021). Strengthening Fraud Prevention with AI in P&C Insurance: Enhancing Cyber Resilience. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 2(1), 43-53. https://doi.org/10.63282/3050-9262.IJAIDSML-V2I1P106
[26] Enjam, G. R. (2021). Data Privacy & Encryption Practices in Cloud-Based Guidewire Deployments. International Journal of AI, BigData, Computational and Management Studies, 2(3), 64-73. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V2I3P108
[27] Rusum, G. P. (2022). WebAssembly across Platforms: Running Native Apps in the Browser, Cloud, and Edge. International Journal of Emerging Trends in Computer Science and Information Technology, 3(1), 107-115. https://doi.org/10.63282/3050-9246.IJETCSIT-V3I1P112
[28] Pappula, K. K. (2022). Architectural Evolution: Transitioning from Monoliths to Service-Oriented Systems. International Journal of Emerging Research in Engineering and Technology, 3(4), 53-62. https://doi.org/10.63282/3050-922X.IJERET-V3I4P107
[29] Jangam, S. K. (2022). Self-Healing Autonomous Software Code Development. International Journal of Emerging Trends in Computer Science and Information Technology, 3(4), 42-52. https://doi.org/10.63282/3050-9246.IJETCSIT-V3I4P105
[30] Anasuri, S. (2022). Adversarial Attacks and Defenses in Deep Neural Networks. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(4), 77-85. https://doi.org/10.63282/xs971f03
[31] Rahul, N. (2022). Automating Claims, Policy, and Billing with AI in Guidewire: Streamlining Insurance Operations. International Journal of Emerging Research in Engineering and Technology, 3(4), 75-83. https://doi.org/10.63282/3050-922X.IJERET-V3I4P109
[32] Enjam, G. R. (2022). Energy-Efficient Load Balancing in Distributed Insurance Systems Using AI-Optimized Switching Techniques. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(4), 68-76. https://doi.org/10.63282/3050-9262.IJAIDSML-V3I4P108
[33] Rusum, G. P., & Anasuri, S. (2023). Composable Enterprise Architecture: A New Paradigm for Modular Software Design. International Journal of Emerging Research in Engineering and Technology, 4(1), 99-111. https://doi.org/10.63282/3050-922X.IJERET-V4I1P111
[34] Pappula, K. K. (2023). Reinforcement Learning for Intelligent Batching in Production Pipelines. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 4(4), 76-86. https://doi.org/10.63282/3050-9262.IJAIDSML-V4I4P109
[35] Jangam, S. K., & Pedda Muntala, P. S. R. (2023). Challenges and Solutions for Managing Errors in Distributed Batch Processing Systems and Data Pipelines. International Journal of Emerging Research in Engineering and Technology, 4(4), 65-79. https://doi.org/10.63282/3050-922X.IJERET-V4I4P107
[36] Anasuri, S. (2023). Secure Software Supply Chains in Open-Source Ecosystems. International Journal of Emerging Trends in Computer Science and Information Technology, 4(1), 62-74. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I1P108
[37] Rahul, N. (2023). Transforming Underwriting with AI: Evolving Risk Assessment and Policy Pricing in P&C Insurance. International Journal of AI, BigData, Computational and Management Studies, 4(3), 92-101. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V4I3P110
[38] Enjam, G. R. (2023). Modernizing Legacy Insurance Systems with Microservices on Guidewire Cloud Platform. International Journal of Emerging Research in Engineering and Technology, 4(4), 90-100. https://doi.org/10.63282/3050-922X.IJERET-V4I4P109
[39] Pappula, K. K. (2020). Browser-Based Parametric Modeling: Bridging Web Technologies with CAD Kernels. International Journal of Emerging Trends in Computer Science and Information Technology, 1(3), 56-67. https://doi.org/10.63282/3050-9246.IJETCSIT-V1I3P107
[40] Rahul, N. (2020). Vehicle and Property Loss Assessment with AI: Automating Damage Estimations in Claims. International Journal of Emerging Research in Engineering and Technology, 1(4), 38-46. https://doi.org/10.63282/3050-922X.IJERET-V1I4P105
[41] Enjam, G. R., & Chandragowda, S. C. (2020). Role-Based Access and Encryption in Multi-Tenant Insurance Architectures. International Journal of Emerging Trends in Computer Science and Information Technology, 1(4), 58-66. https://doi.org/10.63282/3050-9246.IJETCSIT-V1I4P107
[42] Pappula, K. K. (2021). Modern CI/CD in Full-Stack Environments: Lessons from Source Control Migrations. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 2(4), 51-59. https://doi.org/10.63282/3050-9262.IJAIDSML-V2I4P106
[43] Rahul, N. (2021). AI-Enhanced API Integrations: Advancing Guidewire Ecosystems with Real-Time Data. International Journal of Emerging Research in Engineering and Technology, 2(1), 57-66. https://doi.org/10.63282/3050-922X.IJERET-V2I1P107
[44] Enjam, G. R., Chandragowda, S. C., & Tekale, K. M. (2021). Loss Ratio Optimization using Data-Driven Portfolio Segmentation. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 2(1), 54-62. https://doi.org/10.63282/3050-9262.IJAIDSML-V2I1P107
[45] Rusum, G. P., & Pappula, K. K. (2022). Federated Learning in Practice: Building Collaborative Models While Preserving Privacy. International Journal of Emerging Research in Engineering and Technology, 3(2), 79-88. https://doi.org/10.63282/3050-922X.IJERET-V3I2P109
[46] Pappula, K. K. (2022). Modular Monoliths in Practice: A Middle Ground for Growing Product Teams. International Journal of Emerging Trends in Computer Science and Information Technology, 3(4), 53-63. https://doi.org/10.63282/3050-9246.IJETCSIT-V3I4P106
[47] Jangam, S. K., & Pedda Muntala, P. S. R. (2022). Role of Artificial Intelligence and Machine Learning in IoT Device Security. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(1), 77-86. https://doi.org/10.63282/3050-9262.IJAIDSML-V3I1P108
[48] Anasuri, S. (2022). Next-Gen DNS and Security Challenges in IoT Ecosystems. International Journal of Emerging Research in Engineering and Technology, 3(2), 89-98. https://doi.org/10.63282/3050-922X.IJERET-V3I2P110
[49] Rahul, N. (2022). Enhancing Claims Processing with AI: Boosting Operational Efficiency in P&C Insurance. International Journal of Emerging Trends in Computer Science and Information Technology, 3(4), 77-86. https://doi.org/10.63282/3050-9246.IJETCSIT-V3I4P108
[50] Enjam, G. R., & Tekale, K. M. (2022). Predictive Analytics for Claims Lifecycle Optimization in Cloud-Native Platforms. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(1), 95-104. https://doi.org/10.63282/3050-9262.IJAIDSML-V3I1P110
[51] Rusum, G. P., & Pappula, K. K. (2023). Low-Code and No-Code Evolution: Empowering Domain Experts with Declarative AI Interfaces. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 4(2), 105-112. https://doi.org/10.63282/3050-9262.IJAIDSML-V4I2P112
[52] Pappula, K. K., & Rusum, G. P. (2023). Multi-Modal AI for Structured Data Extraction from Documents. International Journal of Emerging Research in Engineering and Technology, 4(3), 75-86. https://doi.org/10.63282/3050-922X.IJERET-V4I3P109
[53] Jangam, S. K., Karri, N., & Pedda Muntala, P. S. R. (2023). Develop and Adapt a Salesforce User Experience Design Strategy that Aligns with Business Objectives. International Journal of Emerging Trends in Computer Science and Information Technology, 4(1), 53-61. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I1P107
[54] Anasuri, S. (2023). Confidential Computing Using Trusted Execution Environments. International Journal of AI, BigData, Computational and Management Studies, 4(2), 97-110. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V4I2P111
[55] Rahul, N. (2023). Personalizing Policies with AI: Improving Customer Experience and Risk Assessment. International Journal of Emerging Trends in Computer Science and Information Technology, 4(1), 85-94. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I1P110
[56] Enjam, G. R. (2023). AI Governance in Regulated Cloud-Native Insurance Platforms. International Journal of AI, BigData, Computational and Management Studies, 4(3), 102-111. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V4I3P111
[57] Pappula, K. K., & Rusum, G. P. (2020). Custom CAD Plugin Architecture for Enforcing Industry-Specific Design Standards. International Journal of AI, BigData, Computational and Management Studies, 1(4), 19-28. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V1I4P103
[58] Enjam, G. R., & Tekale, K. M. (2020). Transitioning from Monolith to Microservices in Policy Administration. International Journal of Emerging Research in Engineering and Technology, 1(3), 45-52. https://doi.org/10.63282/3050-922X.IJERETV1I3P106
[59] Pappula, K. K., & Anasuri, S. (2021). API Composition at Scale: GraphQL Federation vs. REST Aggregation. International Journal of Emerging Trends in Computer Science and Information Technology, 2(2), 54-64. https://doi.org/10.63282/3050-9246.IJETCSIT-V2I2P107
[60] Rusum, G. P. (2022). Security-as-Code: Embedding Policy-Driven Security in CI/CD Workflows. International Journal of AI, BigData, Computational and Management Studies, 3(2), 81-88. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V3I2P108
[61] Enjam, G. R., & Chandragowda, S. C. (2021). RESTful API Design for Modular Insurance Platforms. International Journal of Emerging Research in Engineering and Technology, 2(3), 71-78. https://doi.org/10.63282/3050-922X.IJERET-V2I3P108
[62] Pappula, K. K. (2022). Containerized Zero-Downtime Deployments in Full-Stack Systems. International Journal of AI, BigData, Computational and Management Studies, 3(4), 60-69. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V3I4P107
[63] Jangam, S. K., Karri, N., & Pedda Muntala, P. S. R. (2022). Advanced API Security Techniques and Service Management. International Journal of Emerging Research in Engineering and Technology, 3(4), 63-74. https://doi.org/10.63282/3050-922X.IJERET-V3I4P108
[64] Anasuri, S. (2022). Zero-Trust Architectures for Multi-Cloud Environments. International Journal of Emerging Trends in Computer Science and Information Technology, 3(4), 64-76. https://doi.org/10.63282/3050-9246.IJETCSIT-V3I4P107
[65] Rahul, N. (2022). Optimizing Rating Engines through AI and Machine Learning: Revolutionizing Pricing Precision. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(3), 93-101. https://doi.org/10.63282/3050-9262.IJAIDSML-V3I3P110
[66] Enjam, G. R. (2022). Secure Data Masking Strategies for Cloud-Native Insurance Systems. International Journal of Emerging Trends in Computer Science and Information Technology, 3(2), 87-94. https://doi.org/10.63282/3050-9246.IJETCSIT-V3I2P109
[67] Rusum, G. P. (2023). Large Language Models in IDEs: Context-Aware Coding, Refactoring, and Documentation. International Journal of Emerging Trends in Computer Science and Information Technology, 4(2), 101-110. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I2P110
[68] Pappula, K. K. (2023). Edge-Deployed Computer Vision for Real-Time Defect Detection. International Journal of AI, BigData, Computational and Management Studies, 4(3), 72-81. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V4I3P108
[69] Jangam, S. K. (2023). Importance of Encrypting Data in Transit and at Rest Using TLS and Other Security Protocols and API Security Best Practices. International Journal of AI, BigData, Computational and Management Studies, 4(3), 82-91. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V4I3P109
[70] Anasuri, S., & Pappula, K. K. (2023). Green HPC: Carbon-Aware Scheduling in Cloud Data Centers. International Journal of Emerging Research in Engineering and Technology, 4(2), 106-114. https://doi.org/10.63282/3050-922X.IJERET-V4I2P111
[71] Enjam, G. R. (2023). Optimizing PostgreSQL for High-Volume Insurance Transactions & Secure Backup and Restore Strategies for Databases. International Journal of Emerging Trends in Computer Science and Information Technology, 4(1), 104-111. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I1P112
[72] Pappula, K. K., & Rusum, G. P. (2021). Designing Developer-Centric Internal APIs for Rapid Full-Stack Development. International Journal of AI, BigData, Computational and Management Studies, 2(4), 80-88. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V2I4P108
[73] Rusum, G. P., & Pappula, kiran K. . (2022). Event-Driven Architecture Patterns for Real-Time, Reactive Systems. International Journal of Emerging Research in Engineering and Technology, 3(3), 108-116. https://doi.org/10.63282/3050-922X.IJERET-V3I3P111
[74] Jangam, S. K., & Karri, N. (2022). Potential of AI and ML to Enhance Error Detection, Prediction, and Automated Remediation in Batch Processing. International Journal of AI, BigData, Computational and Management Studies, 3(4), 70-81. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V3I4P108
[75] Anasuri, S. (2022). Formal Verification of Autonomous System Software. International Journal of Emerging Research in Engineering and Technology, 3(1), 95-104. https://doi.org/10.63282/3050-922X.IJERET-V3I1P110
[76] Rusum, G. P., & Anasuri, S. (2023). Synthetic Test Data Generation Using Generative Models. International Journal of Emerging Trends in Computer Science and Information Technology, 4(4), 96-108. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I4P111
[77] Jangam, S. K., & Karri, N. (2023). Robust Error Handling, Logging, and Monitoring Mechanisms to Effectively Detect and Troubleshoot Integration Issues in MuleSoft and Salesforce Integrations. International Journal of Emerging Research in Engineering and Technology, 4(4), 80-89. https://doi.org/10.63282/3050-922X.IJERET-V4I4P108
[78] Anasuri, S. (2023). Synthetic Identity Detection Using Graph Neural Networks. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 4(4), 87-96. https://doi.org/10.63282/3050-9262.IJAIDSML-V4I4P110
[79] Enjam, G. R., Tekale, K. M., & Chandragowda, S. C. (2023). Zero-Downtime CI/CD Production Deployments for Insurance SaaS Using Blue/Green Deployments. International Journal of Emerging Research in Engineering and Technology, 4(3), 98-106. https://doi.org/10.63282/3050-922X.IJERET-V4I3P111
