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Abstract
This study develops an early warning system for detecting operational anomalies (red flags) in the food and beverage industry, specifically for businesses lacking historical fraud data, using Terminal Coffee as a case study. The system integrates rule-based methods and logistic regression, combining classification logic and probabilistic prediction. The initial stage applies a rule-based approach by setting statistical thresholds (mean ± standard deviation) for eight operational indicators, including COGS ratio, electricity cost, average items and sales per transaction, and discount-to-sales ratio. From a total of 1,449 shift-level observations collected over one year, 436 (30.09%) were classified as red flags. These classifications were then used as the dependent variable in a binary logistic regression model. The estimation results identified four statistically significant predictors (p < 0.05): COGS per item, average sales per transaction, average sales per item, and discount ratio. The final model demonstrated strong classification performance, with 92% accuracy, 83% sensitivity, 95.3% specificity, 86.7% precision, and an AUC of 0.957—indicating excellent discriminatory ability. These findings suggest that combining rule-based logic and logistic regression effectively builds a reliable and adaptive early warning system for operational monitoring, even in the absence of historical fraud records. The proposed system is applicable for integration into managerial dashboards as a data-driven decision support tool to facilitate proactive, objective, and timely interventions in daily operational oversight. Key Words : red flags, rule-based, logistic regression, anomaly detection, early warning system.
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References
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References
Ahmed, M., Ansar, K., Muckley, C. B., Khan, A., Anjum, A., & Talha, M. (2021). A semantic rule based digital fraud detection. PeerJ Computer Science. https://doi.org/10.7717/peerj-cs.649
Albrecht, W. S., Albrecht, C. C., Albrecht, C. O., & Zimbelman, M. F. (2019). Fraud Examination (6th ed.). Cengage Learning.
Baumann, M. (2021). Improving a rule based fraud detection system with classification based on association rule mining. In INFORMATIK 2021, Lecture Notes in Informatics (LNI). Gesellschaft für Informatik, Bonn.
Bello, O. A., Folorunso, A., Ejiofor, O. E., Budale, F. Z., Adebayo, K., & Babatunde, O. A. (2023). Machine learning approaches for enhancing fraud prevention in financial transactions. International Journal of Management Technology, 10(1), 85–109.
Bradley, A. P. (1997). The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition, 30(7), 1145–1159. https://doi.org/10.1016/S0031-3203(96)00142-2
Géron, A. (2019). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow (2nd ed.). O’Reilly Media.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2020). Multivariate Data Analysis (8th ed.). Cengage Learning.
Haghighi, M., Johnson, S. B., Qian, X., Lynch, K. F., Vehik, K., Huang, S., & The TEDDY Study Group. (2016). A comparison of rule based analysis with regression methods in understanding the risk factors for study withdrawal in a pediatric study. Scientific Reports, 6, 30828. https://doi.org/10.1038/srep30828
Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied Logistic Regression (3rd ed.). Wiley. https://doi.org/10.1002/9781118548387
Islam, S., Haque, M. M., & Karim, A. N. M. R. (2024). A rule based and logistic regression approach to fraud detection. International Journal of Electrical and Computer Engineering, 14(1), 759–771. https://doi.org/10.11591/ijece.v14i1.pp759-771
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An Introduction to Statistical Learning: with Applications in R (2nd ed.). Springer.
Knuth, T., & Ahrholdt, D. C. (2022). Consumer fraud in online shopping: Detecting risk indicators through data mining. International Journal of Electronic Commerce, 26(3), 388–411. https://doi.org/10.1080/10864415.2022.2076199
Kotagiri, A., & Yada, A. (2024). Crafting a strong anti-fraud defense: RPA, ML, and NLP collaboration for resilience in US Finance. International Journal of Management Education for Sustainable Development, 7(7), 1–15.
Kuhn, M., & Johnson, K. (2020). Feature Engineering and Selection: A Practical Approach for Predictive Models. CRC Press.
MDPI Electronics. (2024). Explainable artificial intelligence-based decision support systems. Electronics, 13(14), 2842. https://www.mdpi.com/2079-9292/13/14/2842
Meduri, K. (2024). Cybersecurity threats in banking: Unsupervised fraud detection analysis. International Journal of Science and Research Archive, 11(2), 915–925.
Onwujekwe, G., & Weistroffer, H. R. (2025). Intelligent decision support systems: An analysis of the literature and a framework for development. Information Systems Frontiers. https://doi.org/10.1007/s10796-024-10571-1
Prastiwi, D. (2024, February 29). Tips cegah kerugian akibat kecurangan kasir pada bisnis restoran dengan aplikasi POS. Liputan6. Retrieved from https://www.liputan6.com/news/read/217790/tips-cegah-kerugian-akibat-kecurangan-kasir-pada-bisnis-restoran-dengan-aplikasi-pos?page=2
Sarker, I. H., Janicke, H., Ferrag, M. A., & Abuadbba, S. (2024). Multi-aspect rule based AI: Methods, taxonomy, challenges and directions. Internet of Things, 25, 101110.
Virgeniya, S. C., & Ramaraj, E. (2019). Predictive analytics using rule based classification and hybrid logistic regression (HLR) algorithm for decision making. International Journal of Scientific & Technology Research, 8(10), 1509–1513.
Zainuddin, M., Rahman, A., & Sari, D. (2021). Pengaruh teknologi informasi terhadap deteksi fraud di sektor usaha mikro, kecil, dan menengah. Jurnal Ekonomi dan Bisnis.
Zhang, C. (2020). Classification rule mining algorithm combining intuitionistic fuzzy rough sets and genetic algorithm. International Journal of Fuzzy Systems, 22, 1694–1715.