Elsevier

Expert Systems with Applications

Volume 61, 1 November 2016, Pages 343-355
Expert Systems with Applications

Financial innovation: Credit default hybrid model for SME lending

https://doi.org/10.1016/j.eswa.2016.05.029Get rights and content

Highlights

  • We propose an ANN/logistic credit risk hybrid model for SME lending.

  • We find that the hybrid model is more accurate than either of the separate ones.

  • Our study is one of few that sheds light on the hybrid model.

  • Our study is one of few which focuses on credit risk models for SMEs.

  • The hybrid model can help the bank decrease the errors in credit risk evaluations.

Abstract

Credit risk evaluation is an integral part of any lending process, and even more so for financial institutions involved in lending to SMEs. The importance of credit scoring has increased recently because of the financial crisis and increased capital requirements for banks. There are, however, only few studies that develop credit coring models for SME lending. The objective of this study is to introduce a novel, more accurate credit risk estimation approach for SMEs business lending. Based on traditional statistical methods and recent artificial intelligence (AI) techniques, we proposed a hybrid model which combines the logistic regression approach and artificial neural networks (ANN). In order to test the effectiveness and feasibility of the proposed hybrid model, we use the data of Finnish SMEs from the fiscal years 2004 to 2012. Our results suggest that the proposed ANN/logistic hybrid model is more accurate than either of the initial models ANN or logistic regression. This improvement in the accuracy of the credit scoring model decreases evaluation errors and has thereby many potential practical implications. First of all, a more accurate credit scoring model can result in better performance of the whole SME loan portfolio. Second, it can also result in lower capital requirements from the banks perspective and lower interest rates from the individual firm's perspective. Combined, these effects will enhance the banks competitiveness in the market for SME loans.

Introduction

Most firms around the world are SMEs, and they are recognized worldwide as the engine of economic growth. It has also been suggested in the literature, that from a credit risk point of view, SMEs are different from large corporations for a number of reasons (Altman & Sabato, 2007). Jacobson, Lindé, and Roszbach (2005) and Dietsch and Petey (2004) show that bank loan portfolios of SME loans are usually riskier than corporate credit. Altman and Sabato (2007) further suggest that more accurate credit scoring models in the market for SME loans have many potential benefits; First of all, if banks are able to improve the accuracy of their credit scoring models, their capital requirements may be lower. Second of all, if banks are able to reduce their capital requirements in SME lending, this could result in lower interest rates for their SME customers. Tsai and Wu (2008) further suggest that even a slight improvement in credit scoring accuracy might reduce credit risk and translate into significant future savings. It may therefore be plausible to assume, that more accurate credit risk evaluation can strengthen the competitive advantage of financial institutions and reduce the financing difficulties for SMEs.

Financial innovation has been described as the life blood of efficient and responsive capital markets, and one of the most important innovations since the 1990 s is banks’ use of credit risk evaluation in SME business lending (Akhavein, Frame, & White, 2005). SME business lending based on credit risk evaluation is a relatively new technology that involves processing data about the firm and its owner by using statistical methods to predict applicants’ expected future loan performance (Hand & Henley, 1997). Previous studies indicate that many methods can be applied in credit risk evaluation. These methods can be classified into traditional statistical methods and recent artificial intelligence approaches.

Statistical methods are the first and most frequently used methods in credit scoring or credit risk evaluation. Many researchers used statistical methods to build a credit risk model (Altman, 1968, Altman and Sabato, 2007, Banasik et al., 2001, Boyes et al., 1989, Durand, 1941, Ewert, 1968, Makowski, 1985, Myers and Forgy, 1963, Orgler, 1970, Šarlija et al., 2004, Steenackers and Goovaerts, 1989, Wiginton, 1980). With the development of information and computational technologies, recently more accurate credit risk models have been developed based on sophisticated intelligence approaches which are more capable of modelling nonlinear or extremely complex functions. Credit risk modeling is one of the main areas in accounting and finance which artificial intelligent technologies have been applied into successfully (Angelini et al., 2008, Arminger et al., 1997, Chatterjee and Barcun, 1970, Desai et al., 1996, Piramuthu, 1999, Tsai and Wu, 2008, West, 2000). In recent years, hybrid prediction models, which combine traditional statistical methods and artificial intelligence technologies have been suggested to have better prediction ability than either of the two components. For instance, in environmental engineering, Schafer (2008) shows that the accuracy of prediction of a logistic regression model is lower than that of the ANN/logistic hybrid model. Lin (2009) obtained similar results when she investigate financially distressed banks. It has been suggested that neural network may not be as stable as standard statistical techniques, and combinations in certain applications might be more valuable (Paliwal & Kumar, 2009).

The bulk of previous studies in credit scoring models concentrate on either traditional statistical methods or artificial intelligence technologies and there is little evidence on hybrid credit risk models in the literature especially for SMEs business lending. The aim of this paper is to propose a novel hybrid credit risk model (ANN/logistic hybrid model) for SMEs lending integrating ANN with the logistic regression method. By using data Finnish SMEs from the fiscal years from 2004 to 2012, we are able to test whether the performance of credit risk models based on artificial intelligent technology can be improved by combining them with traditional statistical methods. To the best of our knowledge, our study is the first one to investigate the ANN/logistic hybrid model for data on SMEs.

Our main empirical results suggest that the ANN/logistic hybrid model is more accurate than either of the separate ANN or logistic regression approach. Additionally, we find that credit risky firms are less profitable than non-credit risky firms. Furthermore, they are more levered and have higher asset turnover than non-credit risky firms. Also, credit risky firms are more likely to be larger and younger than non-credit risky firms. This study extends the findings of previous studies on four aspects. First, our study is one of few that sheds light on the hybrid model for financial data, while most of the previous ones concentrate on statistical methods or computational (artificial) intelligence methods. Second, SMEs have unique accounting characteristics compared to large firms. In order to develop SME business lending, instead of using a credit risk model for corporations, a powerful and reliable credit risk estimation model just for SME is necessary. Third, we propose a credit risk model for Finnish firms, whereas most of existing studies have used data on the US and UK. Fourth, this study demonstrates that with the inclusion of traditional statistic methods (e.g. logistic regression), the accuracy of artificial intelligent technologies (e.g. ANN) in credit scoring models can be improved.

The remainder of this paper is structured as follows. Section two of the study discusses the relevant literature. Section three presents the methodology in this study. Section four presents the data and descriptive statistics on the variables. Section five presents the main empirical results. Section six is the discussions and conclusions.

Section snippets

Literature review

Previous studies have applied various methods to credit risk evaluation. These methods can be classified into traditional statistical methods and artificial intelligence approaches. Statistical methods are the first and the most frequently used methods in credit risk evaluation. These methods include linear regression, discriminate analysis, and logistic regression, etc. Artificial intelligence methods have been presented to credit risk evaluation much more recently. Even more recent and much

The principle of a feed-forward Neural Network

A BP neural network is one type of artificial intelligence algorithm in ANN. In this study, we chose a feed-forward neural network to build the model. The BP neural network is a two-tier or multi-layer feed forward neural network whose neurons transfer function is Sigmoid-function. The output of the network is continuous between 0 and 1 volume. The feed-forward neural network can realize the mapping from input to output of any non-linear function. A typical feed-forward neural network is an

Sample selection

The data used in this study is a combination of credit related data and financial data of Finnish SMEs from the fiscal years from 2004 to 2012. The credit related data were collected through a VOITTO database from Suomen Asiakastieto Ltd, which is a credit rating and financial information company. The firms’ financial data were collected from Amadeus Database of Bureau van Dijk, which covers firms’ financial and business information all over the Europe. Finally, the total number of observations

First step logistic regression in modeling samples

We put the selected six components as the inputs and CREDIT_RISK as the output to run a logistic regression model. In Table 5 we can see that the total number of the training samples for modeling is 1883 (70% of total sample 2681) and the pseudo R2 is 0.179. Based on the results, we define a new variable named Logistic_Results, which is the real prediction value from the first step logistic regression.

Second step basic neural network in modeling samples

We added 22 dependent variables as the inputs and CREDIT_RISK as the output to train the basic

Conclusions and future research

The purpose of this study is to investigate the accuracy of a hybrid credit risk model (ANN/logistic hybrid model) for SME lending using data of Finnish SMEs for the period 2004 to 2012. To the best of our knowledge, our study is the first one to investigate the ANN/logistic hybrid model with data on SMEs.

Our main empirical results indicate that the ANN/logistic hybrid model is more accurate in evaluating credit risk in SMEs lending. Also, we find that, at least in our data on Finnish SMEs,

Acknowledgements

We would also like to acknowledge the financial support from OP-Pohjola Group Research Foundation, Liikesivistysrahasto, CSC and Jenny ja Antti Wihurin Rahasto.

References (66)

  • H. Etemadi et al.

    A genetic programming model for bankruptcy prediction: Empirical evidence from Iran

    Expert Systems with Applications

    (2009)
  • D.J. Hand et al.

    Discriminant analysis when the classes arise from a continuum

    Pattern Recognition

    (1998)
  • T. Harris

    Credit scoring using the clustered support vector machine

    Expert Systems with Applications

    (2015)
  • HuangC.L. et al.

    Credit scoring with a data mining approach based on support vector machines

    Expert systems with applications

    (2007)
  • A. Khashman

    Credit risk evaluation using neural networks: Emotional versus conventional models

    Applied Soft Computing

    (2011)
  • V. Kozeny

    Genetic algorithms for credit scoring: Alternative fitness function performance comparison

    Expert Systems with Applications

    (2015)
  • LiangD. et al.

    The effect of feature selection on financial distress prediction

    Knowledge-Based Systems

    (2015)
  • LinS.L.

    A new two-stage hybrid approach of credit risk in banking industry

    Expert Systems with Applications

    (2009)
  • N. Mahmoudi et al.

    Detecting credit card fraud by modified Fisher discriminant analysis

    Expert Systems with Applications

    (2015)
  • R. Malhotra et al.

    Evaluating consumer loans using neural networks

    Omega

    (2003)
  • D. Martens et al.

    Comprehensible credit scoring models using rule extraction from support vector machines

    European journal of operational research

    (2007)
  • S. Oreski et al.

    Hybrid system with genetic algorithm and artificial neural networks and its application to retail credit risk assessment

    Expert systems with applications

    (2012)
  • S. Oreski et al.

    Genetic algorithm-based heuristic for feature selection in credit risk assessment

    Expert systems with applications

    (2014)
  • M. Paliwal et al.

    Neural networks and statistical techniques: A review of applications

    Expert Systems with Applications

    (2009)
  • S. Piramuthu

    Financial credit-risk evaluation with neural and neurofuzzy systems

    European Journal of Operational Research

    (1999)
  • A. Steenackers et al.

    A credit scoring model for personal loans

    Insurance: Mathematics and Economics

    (1989)
  • M. Šušteršič et al.

    Consumer credit scoring models with limited data

    Expert Systems with Applications

    (2009)
  • TsaiC.-F. et al.

    Using neural network ensembles for bankruptcy prediction and credit scoring

    Expert Systems with Applications

    (2008)
  • D. West

    Neural network credit scoring models

    Computers and Operations Research

    (2000)
  • YaoX. et al.

    Support vector regression for loss given default modelling

    European Journal of Operational Research

    (2015)
  • ZhangG. et al.

    Forecasting with artificial neural networks: The state of the art

    International journal of forecasting

    (1998)
  • ZhaoZ. et al.

    Investigation and improvement of multi-layer perception neural networks for credit scoring

    Expert Systems with Applications

    (2015)
  • H.A. Abdou et al.

    Credit scoring, statistical techniques and evaluation criteria: A review of the literature

    Intelligent Systems in Accounting, Finance and Management

    (2011)
  • Cited by (0)

    1

    Researcher, University of Eastern Finland

    2

    Professor, University of Eastern Finland

    View full text