| 參考文獻: | Alexandrov, A., Bedre-Defolie, Ö., and Grodzicki, D. (2017). Consumer demand for credit card services.
Apley, D. W., . Z. J. (2020). Visualizing the effects of predictor variables in black box supervised learning models. Journal of the Royal Statistical Society Series B: Statistical Methodology, 82(4):1059–1086.
Attivilli, R. and Jothi, A. A. (2023). Serverless stream-based processing for real time credit card fraud detection using machine learning. In 2023 IEEE World AI IoT Congress (AIIoT), pages 0434–0439. IEEE.
Barbaglia, L., Manzan, S., and Tosetti, E. (2023). Forecasting loan default in europe with machine learning. Journal of Financial Econometrics, 21(2):569–596.
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. Breiman, L. (2001). Random forests. Machine Learning, 45:5–32.
Chawla, N. V., Bowyer, K. W., Hall, L. O., and Kegelmeyer, W. P. (2002). Smote: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16:321–357.
Chen, T. and Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 785–794.
Consulting, B. (2022). Digital Payment Market- Global Industry Size, Share, Trend Analysis and Forecast Report, 2018-2028, Segmented By Offering (Solution and Service) By Mode of Payment (Digital Currencies, Bank Cards, Digital Wallets, Net Banking, Point of Sale, and Others), By Deployment (On-Premise and Cloud), By Organization Size (Small Enterprises, Medium Enterprises, and Large Enterprises), By Sector (Banking, Financial Services and Insurance (BFSI), Retail/E-Commerce, Healthcare, Hospitality/Travel, Logistics and Transportation, Others), By Region (North America, Europe, Asia-Pacific (APAC), Latin America (LATAM), Middle East Africa (MEA). https://www.blueweaveconsulting.com/report/ digital-payment-market/report-sample. 27
Consulting, M. C. (2023). Credit Card Fraud Statistics (2024). https://merchantcostconsulting.com/lower-credit-card-processing-fees/ credit-card-fraud-statistics/.
Davis, J. and Goadrich, M. (2006). The relationship between precision-recall and roc curves. In Proceedings of the 23rd International Conference on Machine Learning, pages 233–240.
Ganong, P. and Noel, P. (2019). Consumer spending during unemployment: Positive and normative implications. American Economic Review, 109(7):2383–2424.
Hajek, P. and Henriques, R. (2017). Mining corporate annual reports for intelligent detection of financial statement fraud: A comparative study of machine learning methods. KnowledgeBased Systems, 128:139–152.
Horvath, A., Kay, B., and Wix, C. (2023). The covid-19 shock and consumer credit: Evidence from credit card data. Journal of Banking & Finance, 152:106854.
Huang, J. and Ling, C. X. (2005). Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering, 17(3):299–310.
Huddleston, D., Liu, F., and Stentoft, L. (2023). Intraday market predictability: A machine learning approach. Journal of Financial Econometrics, 21(2):485–527.
Hundtofte, S., Olafsson, A., and Pagel, M. (2019). Credit smoothing. Technical report, National Bureau of Economic Research.
Karpoff, J. M. (2021). The future of financial fraud. Journal of Corporate Finance, 66:101694.
KAZANINS, J. (2022). Notes on VISA FY Q4 2022 results: U.S. credit card holders drive payments volume up. https://www.popularfintech.com/p/notes-on-visa-fy-q4-2022-results.
Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., and Fotiadis, D. I. (2015). Machine learning applications in cancer prognosis and prediction. Computational and Structural biotechnology journal, 13:8–17.
Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R., and Yu, B. (2019). Interpretable machine learning: Definitions, methods, and applications.
arXiv preprint arXiv:1901.04592. Nobre, J. and Neves, R. F. (2019). Combining principal component analysis, discrete wavelet transform and xgboost to trade in the financial markets. Expert Systems with Applications, 125:181–194.
Perols, J. (2011). Financial statement fraud detection: An analysis of statistical and machine learning algorithms. Auditing: A Journal of Practice & Theory, 30(2):19–50. 28
Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). ” why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 1135–1144.
Sadgali, I., Sael, N., and Benabbou, F. (2019). Performance of machine learning techniques in the detection of financial frauds. Procedia Computer Science, 148:45–54.
Schiltz, F., Masci, C., Agasisti, T., and Horn, D. (2018). Using regression tree ensembles to model interaction effects: A graphical approach. Applied Economics, 50(58):6341–6354.
Scholnick, B., Massoud, N., Saunders, A., Carbo-Valverde, S., and Rodríguez-Fernández, F. (2008). The economics of credit cards, debit cards and atms: A survey and some new evidence. Journal of Banking & Finance, 32(8):1468–1483.
Shou, M., Bao, X., and Yu, J. (2023). An optimal weighted machine learning model for detecting financial fraud. Applied Economics Letters, 30(4):410–415.
Spathis, C., Doumpos, M., and Zopounidis, C. (2002). Detecting falsified financial statements: A comparative study using multicriteria analysis and multivariate statistical techniques. European Accounting Review, 11(3):509–535.
Yee, O. S., Sagadevan, S., and Malim, N. H. A. H. (2018). Credit card fraud detection using machine learning as data mining technique. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(1-4):23–27.
Yin, M., Wortman Vaughan, J., and Wallach, H. (2019). Understanding the effect of accuracy on trust in machine learning models. In Proceedings of the 2019 Chi Conference on Human Factors in Computing Systems, pages 1–12.
Zhao, Q. and Hastie, T. (2021). Causal interpretations of black-box models. Journal of Business & Economic Statistics, 39(1):272–281. |