Designing a Predictive Model for Audit Data Analytics of Manufacturing Industry
Keywords:
Audit Data Analytics, Predictive Modeling, Manufacturing Industry, Machine Learning, Risk Assessment, Fraud DetectionAbstract
The model combines feature engineering on enterprise resource planning (ERP) data with ahybrid machine learning pipeline that includes Random Forest, Gradient Boosting, and astacked ensemble for risk classification. A dataset of 48,750 transaction records collected from four mid-sized manufacturing firms over a 24-month period was used to train and validate themodel. Features included transactional attributes, vendor behavior
References
1: A. Mahendra Vardhan and S. Sridhar, "Determining False Positive Analysis of Software Vulnerabilities with Predefined Scan Rules using Random Forest Classifier and Decision Tree Technique," 2022 4th International Conference on Advances in Computing, Communication Control
and Networking (ICAC3N), Greater Noida, India, 2022, pp. 622-625, DOI: https://doi.org/10.1109/ICAC3N56670.2022.10074458
