A Deep Learning Approach to Credit Scoring Using Credit History Data

The paper discusses an approach to credit scoring of retail customers in the banking industry. The approach is based on the development of recurrent neural network models on customer’s credit history data. The advantages and disadvantages of the approach are discussed. A couple of solutions aimed to...

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Veröffentlicht in:	Lobachevskii journal of mathematics 2023, Vol.44 (1), p.198-204
Hauptverfasser:	Smirnov, V. S., Stupnikov, S. A.
Format:	Artikel
Sprache:	eng
Schlagworte:	Algebra Analysis Customers Geometry Mathematical Logic and Foundations Mathematics Mathematics and Statistics Probability Theory and Stochastic Processes Recurrent neural networks
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description	The paper discusses an approach to credit scoring of retail customers in the banking industry. The approach is based on the development of recurrent neural network models on customer’s credit history data. The advantages and disadvantages of the approach are discussed. A couple of solutions aimed to serve as a baseline for the approach are implemented using traditional approaches to credit scoring and evaluated over two datasets from large European banks.
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subjects	Algebra Analysis Customers Geometry Mathematical Logic and Foundations Mathematics Mathematics and Statistics Probability Theory and Stochastic Processes Recurrent neural networks
title	A Deep Learning Approach to Credit Scoring Using Credit History Data
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