Investigation of Total Debit and Credit Card Spending by Sectors with Different Learning Algorithms

Abstract

Global economic activity is expanding day by day. On an individual level, people are meeting their needs; furthermore, they are frequently driven by discounts to make unnecessary purchases. Similarly, companies must actively engage in this economic activity and satisfy consumer demands to sustain their commercial operations. Governments are tasked with encouraging this growth while maintaining a balance through savings or precautionary measures. In this context, the usage of debit and credit cards, the cornerstone of modern economic activity, is rapidly increasing. This study aims to use various learning algorithms to classify spending volumes on debit and credit cards in Türkiye. To this end, classification successes will be achieved with the help of learning algorithms, and their results were compared to determining the top performing algorithms. The algorithms used in this study ‘Deep learning algorithm’, ‘Artificial neural network classifier’, ‘Conjunctive rule algorithm’, and ‘Alternating Decision tree algorithm’. The performance of these algorithms was evaluated using criteria such as ‘True positive rate’, ‘F-measure’, ‘Roc area value’, ‘Kappa statistic’, ‘Mean absolute error’, and ‘Root mean square error’. The analysis revealed that the Alternating Decision tree algorithm was the most successful, achieving a classification accuracy of 85.10%.

Keywords

• Learning algorithms
• Debit card
• Credit card
• Decision trees

Supporting Institution

No

Ethical Statement

The authors declare that ethical rules were followed throughout the preparation of this study.

Thanks

No

Banka ve Kredi Kartı Toplam Harcamasının Sektörlere Göre Farklı Öğrenme Algoritmaları ile Araştırılması

Abstract

Dünya üzerinde ekonomik hareketlilik her geçen gün artmaktadır. Bireysel anlamda insanlar ihtiyaçlarını karşılamakta; bunun yanında ihtiyacı olmasa bile indirim etiketi üzerinden harcama yapmaktadır. Firmalarda benzer şekilde ticaretlerini devam ettirebilmek adına bu ekonomik hareketliliğe katılmak ve insanların ihtiyaçlarını karşılamak zorundadır. Ülkeler bu durumu teşvik ederek, tasarruf yaparak ya da tedbir alarak dengede tutmakla yükümlüdür. Bu bağlamda ekonomik hareketliliğin en önemli noktası olan banka ve kredi kartlarının kullanım yoğunluğu ve ekonomik hareketlilikteki payı hızla yükselmektedir. Bu çalışmada, Türkiye’deki banka ve kredi kartlarının harcama tutarlarının sınıflandırılmasında bazı öğrenme algoritmalarının kullanılması amaçlanmaktadır. Bu doğrultuda öğrenme algoritmaları yardımıyla sınıflandırma başarıları elde edilecek ve sonuçlar karşılaştırılarak hangi algoritmanın en başarılı sınıflandırma performansı gösterdiği belirlenecektir. Çalışmada öğrenme algoritmalarından ‘Derin öğrenme algoritması’, ‘Yapay sinir ağları sınıflandırıcısı’, ‘Birleşik kural algoritması’ ve ‘Değişmeli karar ağaçları algoritması’ kullanılmıştır. Algoritmaların performanslarını incelemek için ise kullanılan sınıflandırma performans kriterleri sırasıyla: ‘Doğru pozitif oranı’, ‘F-ölçütü’, ‘Roc değeri’, ‘kappa istatistiği’, ‘ortalama mutlak hata’ ve ‘Kök ortalama hata kare’ dir. Yapılan analizler sonucunda %85,10 ile Değişmeli karar ağaçları algoritması en başarılı sınıflandırma algoritması olarak belirlenmiştir

Keywords

• Öğrenme algoritmaları
• Banka kartı
• Kredi kartı
• Karar ağaçları

Supporting Institution

Yok

Ethical Statement

Yazarlar, bu çalışmanın hazırlanması boyunca etik kurallara uyulduğunu beyan ederler.

Thanks

Yok

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