Machine learning has become a powerful tool in detecting fraudulent behavior in various industries, including healthcare, insurance, and manufacturing. ML algorithms and techniques have shown promise in improving quality control and prediction of equipment failures, with the potential to reduce human workload and improve quality assurance practices across different fields.
The study of different ways to detect Medicaid fraud using supervised and unsupervised machine learning algorithms provides insight into the strengths and limitations of these approaches. While supervised methods require regularly updated training datasets to identify fraudulent behavior based on predefined class labels, unsupervised techniques analyze the entire dataset to find patterns and identify outliers without prior knowledge of fraud or legitimate behavior.
Both methods present challenges and limitations, such as the need for costly and time-consuming training data sets for supervised methods, and performance issues with unsupervised approaches. To address the challenge of imbalanced data in scientific studies, event-based sampling and oversampling techniques can be used to ensure data is representative and accurate. Additionally, ensemble methods like Random Forest can be used to address other challenges, including classification accuracy and overfitting.
The potential and benefits of machine learning applications in quality control and fraud detection in various industries are significant, making it an important area of research and development. The use of deep learning models and other advances in ML algorithms can help overcome current limitations and expand the scope and accuracy of ML-based quality control and fraud detection.
Authors
Hoseini, C., Badar, M. A., Shahhosseini, A. M., & Kluse, C. J. (2021, March). A review of machine learning methods applicable to quality issues. In Proceedings of the International Conference on Industrial Engineering and Operations Management, Singapore (pp. 7-11).