Öz
Kredi kartı ile ödeme, e-ticaret sitelerinin en çok tercih edilen yöntemlerinden biridir. Dolandırıcılık şüphesi olan siparişler, alışveriş siteleri için en büyük endişe kaynağıdır. Sahtekarlık işlemleri sadece müşterileri değil, aynı zamanda şirketleri ve bankaları da etkiler. Bu nedenle, şirketler emirleri sınıflandırabilmeli ve şüpheli işlemlere karşı önlemler alabilmelidir. Bankacılık tarafında, müşteriler hakkında daha fazla bilgi olması nedeniyle sınıflandırma daha kolaydır, ancak bu süreci e-ticaret sitelerinde belirlemek daha zordur. Bu çalışmada, özel bir e-ticaret girişiminin gerçek sipariş verileri incelenmiş ve şüpheli işlemler belirlenmiştir. Öncelikle, tüm sipariş verileri analiz edildi ve filtrelendi. Sınıflandırma için en iyi değişkenler değişken seçim algoritmaları ile belirlenmiştir. Daha sonra sınıflandırma algoritmaları uygulanmış ve %92 başarı oranı ile şüpheli siparişler belirlenmiştir. Karşılaştırmalı veri madenciliği yöntemleri olarak Naive Bayesian, Karar Ağaçları ve Yapay Sinir Ağı kullanılmıştır.
Anahtar Kelimeler
Kredi Kartı Dolandırıcılığı Tespiti Sınıflandırma Veri Madenciliği
Kaynakça
- CyberSource a Visa Company. (2013, January) 2013 Online Fraud Report. Document.
- Djamila Aouada, Aleksandar Stojanovic, Björn Ottersten Alejandro Correa Bahnsen, "Feature engineering strategies for credit card fraud detection," Expert Systems with Applications, vol. 51, no. 1, pp. 134-142, June 2016.
- Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, Ian H. Witten Mark Hall, "The WEKA Data Mining Software: An Update," SIGKDD Explorations, vol. 11, no. 1, 2009.
- S. Benson Edwin Raj and A. Annie Portia, "Analysis on credit card fraud detection methods," in International Conference on Computer, Communication and Electrical Technology (ICCCET), 2011, pp. 152-156.
- P. K. Chan W. Fan A. L. Prodromidis and S. J. Stolfo, "Distributed data mining in credit card fraud detection," IEEE Intelligent Systems and their Applications, vol. 14, no. 6, pp. 67-74.
- Siddhartha Bhattacharyya, Sanjeev Jha, Tharakunnel Kurian , and J. Christopher Westland, "Data mining for credit card fraud: A comparative study," Decision Support Systems, vol. 50, no. 3, pp. 602-613, February 2011.
- Joyce, James (2003), "Bayes' Theorem", in Zalta, Edward N. (ed.), The Stanford Encyclopedia of Philosophy (Spring 2019 ed.), Metaphysics Research Lab, Stanford University, retrieved 2020-01-17.
- Ian H. Witten and Eibe Frank, Data Mining Practical Machine Learning Tools and Techniques, Jim Gray, Ed.: Elsevier Morgan Kaufman Publishers, 2005.
- N. Kwak and Chong-Ho Choi, "Input feature selection for classification problems," IEEE Transactions on Neural Networks, vol. 13, no. 1, pp. 143-159, 2002.
- Akbulut S., Veri Madenciliği Teknikleri ile Bir Kozmetik Markanın Ayrılan Müşteri Analizi ve Müşteri Segmentasyonu.: Yüksek Lisans Tezi, Gazi Üniversitesi Fen Bilimleri Enstitüsü, 2006.
- Alpaydın, E., Introduction to Machine Learning , The MIT Press, Massachusetts, 2nd edition, 2010,
- J. R Quinlan, C4.5: Programs for machine learning.: San Francisco: Morgan Kaufmann, 1993.
- Kohavi, "Scaling up the accuracy of Naïve Bayes classifiers: A decision tree hybrid.," in Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, Portland, 1996, pp. 202-207.
- O. Sutton, “Introduction to k Nearest Neighbour Classification and Condensed Nearest Neighbour Data Reduction The k Nearest Neighbours Algorithm,” pp. 1–10, 2012. Wikipedia. [Online]. https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm
- Zhang, A., Lipton, Z. C., Li, M. and Smola, A. J., «Dive into Deep Learning. » http://en.diveintodeeplearning.org, 2018, [Reach Date: 21.12.2019].
- Shanmugamani, R. «Deep Learning for Computer Vision» Pactc. 2018. ss 6-7.
- Baughman, D.R., Liu, Y.A., in Neural Networks in Bioprocessing and Chemical Engineering, 1995.
Öz
Credit card payment is one of the most preferred methods of e-commerce sites. Fraud orders are the biggest concerns for online shopping sites. Fraud operations affect not only customers but also companies and banks. Hence, companies should be able to classify orders and take measures against suspicious transactions. Classification is easier on the banking side because of more information about customers, but it is more difficult to determine this process on e-commerce sites. In this study, the actual order data of a private e-commerce enterprise was examined and suspicious transactions were determined. First of all, all order data was analyzed and filtered. The best variables for classification were determined by variable selection algorithms. Afterwards, classification algorithms were applied and suspicious orders were determined with 92% success rate. Naïve Bayesian, Decision Trees and Artificial Neural Network were used as comparative data mining methods.
Anahtar Kelimeler
Kaynakça
- CyberSource a Visa Company. (2013, January) 2013 Online Fraud Report. Document.
- Djamila Aouada, Aleksandar Stojanovic, Björn Ottersten Alejandro Correa Bahnsen, "Feature engineering strategies for credit card fraud detection," Expert Systems with Applications, vol. 51, no. 1, pp. 134-142, June 2016.
- Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, Ian H. Witten Mark Hall, "The WEKA Data Mining Software: An Update," SIGKDD Explorations, vol. 11, no. 1, 2009.
- S. Benson Edwin Raj and A. Annie Portia, "Analysis on credit card fraud detection methods," in International Conference on Computer, Communication and Electrical Technology (ICCCET), 2011, pp. 152-156.
- P. K. Chan W. Fan A. L. Prodromidis and S. J. Stolfo, "Distributed data mining in credit card fraud detection," IEEE Intelligent Systems and their Applications, vol. 14, no. 6, pp. 67-74.
- Siddhartha Bhattacharyya, Sanjeev Jha, Tharakunnel Kurian , and J. Christopher Westland, "Data mining for credit card fraud: A comparative study," Decision Support Systems, vol. 50, no. 3, pp. 602-613, February 2011.
- Joyce, James (2003), "Bayes' Theorem", in Zalta, Edward N. (ed.), The Stanford Encyclopedia of Philosophy (Spring 2019 ed.), Metaphysics Research Lab, Stanford University, retrieved 2020-01-17.
- Ian H. Witten and Eibe Frank, Data Mining Practical Machine Learning Tools and Techniques, Jim Gray, Ed.: Elsevier Morgan Kaufman Publishers, 2005.
- N. Kwak and Chong-Ho Choi, "Input feature selection for classification problems," IEEE Transactions on Neural Networks, vol. 13, no. 1, pp. 143-159, 2002.
- Akbulut S., Veri Madenciliği Teknikleri ile Bir Kozmetik Markanın Ayrılan Müşteri Analizi ve Müşteri Segmentasyonu.: Yüksek Lisans Tezi, Gazi Üniversitesi Fen Bilimleri Enstitüsü, 2006.
- Alpaydın, E., Introduction to Machine Learning , The MIT Press, Massachusetts, 2nd edition, 2010,
- J. R Quinlan, C4.5: Programs for machine learning.: San Francisco: Morgan Kaufmann, 1993.
- Kohavi, "Scaling up the accuracy of Naïve Bayes classifiers: A decision tree hybrid.," in Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, Portland, 1996, pp. 202-207.
- O. Sutton, “Introduction to k Nearest Neighbour Classification and Condensed Nearest Neighbour Data Reduction The k Nearest Neighbours Algorithm,” pp. 1–10, 2012. Wikipedia. [Online]. https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm
- Zhang, A., Lipton, Z. C., Li, M. and Smola, A. J., «Dive into Deep Learning. » http://en.diveintodeeplearning.org, 2018, [Reach Date: 21.12.2019].
- Shanmugamani, R. «Deep Learning for Computer Vision» Pactc. 2018. ss 6-7.
- Baughman, D.R., Liu, Y.A., in Neural Networks in Bioprocessing and Chemical Engineering, 1995.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Mühendislik |
| Bölüm | Makaleler |
| Yazarlar | |
| Yayımlanma Tarihi | 31 Aralık 2020 |
| Yayımlandığı Sayı | Yıl 2020 Sayı: 20 |
