Machine Learning Applied to Point-of-Sale Fraud Detection
This paper applies machine learning (ML) techniques including neural networks, support vector machines Random Forest, and Adaboost to detecting insider fraud in restaurant point-of-sales data. With considerable engineering of the features, and by applying under-sampling techniques we show that ML techniques deliver very high fraud-detection performance. In particular, RandomForest can achieve 91% or better across all metrics when using a model trained on one restaurant to detect fraud in a separate restaurant. However, there must be sufficient fraud samples in the model for this to occur. Knowledge and techniques from this research could be used to develop a low-cost product to automate fraud detection for restaurant owners.
KeywordsMachine learning Classification Outlier detection Fraud detection Point-of-sale data
Data and expertise on normal restaurant server practices, and restaurant fraud was provided by an industry expert with over 20 years experience selling, installing, upgrading, troubleshooting, and providing training for POS systems in multiple geographic regions within the United States.
- 1.Association of Certified Fraud Examiners (ACFE): Report to the nations on occupational fraud and abuse (2017)Google Scholar
- 2.National Restaurant Association: 2016 Restaurant operations report (2016)Google Scholar
- 3.Bhattacharyya, S., Jha, S., Tharakunnel, K., Westland, J.C.: Data mining for credit card fraud: a comparative study. In: 14th International Conference on Security and Cryptography, SECRYPT 2017 (2017)Google Scholar
- 4.Jotheeswaran, J., Loganathan, R., Madhu Sudhanan, B.: Feature reduction using principal component analysis for opinion mining. Int. J. Comput. Sci. Telecommun. 3(5), 118–121 (2012). ISSN 2047-3338Google Scholar
- 5.Phua, C., Lee, V., Smith, K., Gayler, R.: A comprehensive survey of data mining-based fraud detection research. In: 2010 International Conference on Intelligent Computation Technology and Automation, pp. 11–12, May 2010Google Scholar
- 6.Whitfield, C.A.: Guess who’s eating your profits: the manager’s essential guide to restaurant and bar loss prevention and investigations. AuthorHouse, 23 April 2013. ISBN-10: 1481725130Google Scholar
- 7.Patidar, R., Sharma, L.: Credit card fraud detection using neural network. Int. J. Soft Comput. Eng. (IJSCE) 1, 32–38 (2011). ISSN 2231-2307Google Scholar
- 8.Sahin, Y., Duman, E.: Detecting credit card fraud by decision trees and support vector machines. In: International MultiConference of Engineers and Computer Scientists (IMECS), 16–18 March 2011, vol. I, pp. 442–447 (2011)Google Scholar