Predicting SME loan delinquencies during recession using accounting data and SME characteristics: The case of Greece

Giannopoulos, Vasilios; Aggelopoulos, Eleftherios

doi:10.1002/isaf.1456

Cited by 7 publications

(7 citation statements)

References 36 publications

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“…One of the most relevant findings in this cluster is to demonstrate the effectiveness of non-financial factors related to product innovation (patents and brand products) as predictive variables together with financial ratios, macroeconomic indicators, and some characteristics of legal representatives such as age, gender and the value of their real estate properties (Chi and Meng 2018 ; Yu et al 2019 ). Few recent studies in this cluster try to solve the methodological issues generated by the need to combine different sources of information to assess SME default risk better, suggesting the use of machine learning/non-linear programming tools such as MCDA (Corazza et al 2016 ; Gonçalves et al 2016 ), fuzzy clustering (Chai et al 2019 ), neural networks (Giannopoulos and Aggelopoulos 2019 ), non-linear programming with maximum discriminating power of credit scores (Chi et al 2019 ) and cognitive mapping (Oliveira et al 2017 ).…”

Section: Results Of the Vos Analysis And The Systematic Literature Rementioning

confidence: 99%

Rethinking SME default prediction: a systematic literature review and future perspectives

et al. 2021

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Over the last dozen years, the topic of small and medium enterprise (SME) default prediction has developed into a relevant research domain that has grown for important reasons exponentially across multiple disciplines, including finance, management, accounting, and statistics. Motivated by the enormous toll on SMEs caused by the 2007–2009 global financial crisis as well as the recent COVID-19 crisis and the consequent need to develop new SME default predictors, this paper provides a systematic literature review, based on a statistical, bibliometric analysis, of over 100 peer-reviewed articles published on SME default prediction modelling over a 34-year period, 1986 to 2019. We identified, analysed and reviewed five streams of research and suggest a set of future research avenues to help scholars and practitioners address the new challenges and emerging issues in a changing economic environment. The research agenda proposes some new innovative approaches to capture and exploit new data sources using modern analytical techniques, like artificial intelligence, machine learning, and macro-data inputs, with the aim of providing enhanced predictive results.

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Section: Results Of the Vos Analysis And The Systematic Literature Rementioning

confidence: 99%

Rethinking SME default prediction: a systematic literature review and future perspectives

et al. 2021

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“…There are 35 loan subgrades in total for borrowers from A1 down to G5, with A1 subgrade being the safest. Dti: a ratio calculated using the borrower's total monthly debt payments on the total debt obligations, excluding mortgage and the requested LendingClub loan, divided by the borrower's self‐reported monthly income. Revol_Util: revolving line utilization rate, or the amount of credit the borrower is using relative to all available revolving credit. Int_Rate: the interest rate on the loan paid by the borrower. Loan Amount To Annual Income: loan amount to the reported annual income. Annual Instalment To Income: the annual payment owed by the borrower divided by the annual income provided by the borrower during registration. For return evaluation, this study utilizes ANNs. ANNs are based on neurons that are connected via weights (Giannopoulos & Aggelopoulos, 2019; Sun & Vasarhelyi, 2018; Trinkle & Baldwin, 2016). This paper is one of the few comparative studies that utilize different ANN models and compare their performance for a special problem in the P2P lending market.…”

Section: Methodsmentioning

confidence: 99%

A neural‐network‐based decision‐making model in the peer‐to‐peer lending market

Babaei

Bamdad

2020

Intell Sys Acc Fin Mgmt

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Summary This study proposes an investment recommendation model for peer‐to‐peer (P2P) lending. P2P lenders usually are inexpert, so helping them to make the best decision for their investments is vital. In this study, while we aim to compare the performance of different artificial neural network (ANN) models, we evaluate loans from two perspectives: risk and return. The net present value (NPV) is considered as the return variable. To the best of our knowledge, NPV has been used in few studies in the P2P lending context. Considering the advantages of using NPV, we aim to improve decision‐making models in this market by the use of NPV and the integration of supervised learning and optimization algorithms that can be considered as one of our contributions. In order to predict NPV, three ANN models are compared concerning mean square error, mean absolute error, and root‐mean‐square error to find the optimal ANN model. Furthermore, for the risk evaluation, the probability of default of loans is computed using logistic regression. Investors in the P2P lending market can share their assets between different loans, so the procedure of P2P investment is similar to portfolio optimization. In this context, we minimize the risk of a portfolio for a minimum acceptable level of return. To analyse the effectiveness of our proposed model, we compare our decision‐making algorithm with the output of a traditional model. The experimental results on a real‐world data set show that our model leads to a better investment concerning both risk and return.

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“…Giannopoulos and Aggelopoulos (2019) have explicitly focused on the comparison of various credit scoring models in evaluating the risk of small and micro enterprises (SMEs) loan delinquencies based on accounting data and applicants specific attributes. Bo‐Wen and Chi (2012) have proposed a genetic algorithm‐based credit scoring model to classify good and bad customers using the German credit dataset.…”

Section: Related Workmentioning

confidence: 99%

Biogeography based optimization for mining rules to assess credit risk

Giri

Dehuri

et al. 2021

Intell Sys Acc Fin Mgmt

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Financial institutions, by and large, rely on the use of machine learning techniques to improve the classic credit risk assessment model for reduction of costs, delivery of faster decisions, guaranteed credit collections, and risk mitigations. As such, several data mining and machine learning approaches have been developed for computation of credit scores over the last few decades. Moreover, the existing rule-based classification algorithms tend to generate a number of rules with a large number of conditions in the antecedent part. However, these algorithms fail to demonstrate high predictive accuracy while balancing coverage and simplicity. Thus, it becomes quite a challenging task for the researchers to generate an optimal rule set with high predictive accuracy. In this paper, we present an effective rule based classification technique for the prediction of credit risk using a novel Biogeography Based Optimization (BBO) method. The novel BBO in the context of rule mining is named as locally and globally tuned biogeography based rule-miner (LGBBO-RuleMiner). This is applied for discovering optimal rule set with high predictive accuracy from the dataset containing both the categorical and continuous attributes. The performance of the proposed algorithm is compared against a variety of rule-miners such as OneR (1R), PART, JRip, Decision Table, Conjunctive Rule, J48, and Random Tree, along with some meta-heuristic based rule mining techniques by considering two credit risk datasets obtained from University of California, Irvine (UCI) repository. It is found from the comparative study that the proposed rule miner in ten independent runs of tenfold cross validation outperforms all of the aforesaid algorithms in terms of predictive accuracy, coverage, and simplicity.

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Predicting SME loan delinquencies during recession using accounting data and SME characteristics: The case of Greece

Cited by 7 publications

References 36 publications

Rethinking SME default prediction: a systematic literature review and future perspectives

Rethinking SME default prediction: a systematic literature review and future perspectives

A neural‐network‐based decision‐making model in the peer‐to‐peer lending market

Biogeography based optimization for mining rules to assess credit risk

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