Data Analytics in the Banking Sector with Emerging Technology: Machine Learning Approaches for Customer Loss Prediction

Year 2023, Volume: 2 Issue: 1, 74 - 84, 31.07.2023

Büşra Özcan , Kübra Kayapınar , Kemal Adem

Abstract

With the advancement of technology, the banking sector has experienced significant development and has become highly reliant on data. It has become crucial for banks to assess and utilize the increasing volume of data with the aid of advancing technology. In this article, a dataset belonging to bank customers was evaluated using machine learning methods. The objective was to predict whether the bank would lose a customer based on the given dataset. The methods employed in this study were Random Forest, Decision Tree, Gaussian Naive Bayes, K-Nearest Neighbors, Adaboost and Logistic Regression algorithms respectively. Prior to comparing the models, hyperparameter optimization was applied to ensure obtaining the best possible results and enable a fari comparison among the models. Among the algorithms compared in this study, the Random Forest algorithm yielded superior results. The success rates of the other compared algorithms were determined as follows: K-Nearest Neighhbors, Decision Tree, Adaboost, Gaussian Naiva Bayes and Logistic Regression.

Keywords

Bank, Customer, Artificial Intelligence, Tree Algorithms

Gelişen Teknoloji ile Bankacılık Sektöründe Veri Analitiği: Müşteri Kaybı Tahmini İçin Makine Öğrenmesi Yaklaşımları

Abstract

Günümüz gelişen teknoloji ile birlikte banka sektörü daha da gelişmiş ve verilerden çokça faydalanır duruma gelmişlerdir. Artan verileri gelişen teknoloji ile birlikte değerlendirmeleri ve kendi faydalarına kullanmaları elzemdir. Bu makalede makine öğrenmesi yöntemleri ile banka müşterilerine ait olan bir veri seti değerlendirilmiştir. Veri seti üzerinde bankanın müşteriyi kayıp edip etmeyeceği tahmin edilmiştir. Bu çalışma kapsamında kullanılan yöntemler sırasıyla Rastgele Orman, Karar Ağacı, Gauss, K-En Yakın Komşu, Adaboost ve Lojistik Regresyon algoritmalarıdır. Modeller karşılaştırılmadan önce en iyi sonuçları alabilmek ve eşit bir karşılaştırma sağlayabilmek için modeller üzerinde hiperparametre optimizasyonu uygulanmıştır. Bu çalışmada Rastgele Orman algoritması karşılaştırılan diğer algoritmalardan daha iyi sonuçlar elde etmiştir. Karşılaştırılan diğer algoritmaların başarı sonuçları ise sırasıyla K-En Yakın Komşu, Karar Ağacı, Adaboost, Gauss ve Lojistik Regresyon şeklinde belirlenmiştir.

Keywords

Banka, Müşteri, Yapay zeka, Ağaç Algoritmaları

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