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Theoretical and Applied Economics
No. 4 / 2019 (621), Winter

A Deep Neural Network (DNN) based classification model in application to loan default prediction

Selçuk BAYRACI
R&D Center, C/S Information Technologies, Istanbul, Turkey
Orkun SUSUZ
R&D Center, C/S Information Technologies, Istanbul, Turkey

Abstract. In this study, we applied a Deep Neural Networks (DNN) based classification model along with the conventional classification methods (Logistic Regression, Decision Tree, Naïve Bayes and Support Vector Machines) on a two distinct datasets containing characteristics of the loan clients in a medium-sized Turkish commercial bank. Python programming language and libraries (Sklearn, Tensorflow and Keras) have been used in data cleaning, data preparation, feature engineering and model implementation processes. Our empirical findings document that the accuracy of the deep learning classification model increases with the size of the dataset, implying that the deep learning models might yield better results than regression-based models in more complex datasets.

Keywords: data analytics; credit scoring; deep learning; risk management.

Contents

1

Expanding the Eurozone. The stage of economic convergence for Bulgaria, Czech Republic, Croatia, Hungary, Poland and Romania

Iulian Nicolae VASILOIU

2

Business R&D investments in the EU: Main dynamics and economic effects

Mihaela DIACONU

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Volatility experience of major world stock markets

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Unemployment hysteresis analysis for OECD countries

Muhammed TIRAŞOĞLU

5

Culture as a driving factor for foreign direct investments in the Western Development Region of Romania

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6

A Deep Neural Network (DNN) based classification model in application to loan default prediction

Selçuk BAYRACI · Orkun SUSUZ

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