Credit Risk Assessment Model for Small and Micro-Enterprises: The Case of Lithuania

Abstract

In this research, trade credit is analysed form a seller (supplier) perspective. Trade credit allows the supplier to increase sales and profits but creates the risk that the customer will not pay, and at the same time increases the risk of the supplier’s insolvency. If the supplier is a small or micro-enterprise (SMiE), it is usually an issue of human and technical resources. Therefore, when dealing with these issues, the supplier needs a high accuracy but simple and highly interpretable trade credit risk assessment model that allows for assessing the risk of insolvency of buyers (who are usually SMiE). The aim of the research is to create a statistical enterprise trade credit risk assessment (ETCRA) model for Lithuanian small and micro-enterprises (SMiE). In the empirical analysis, the financial and non-financial data of 734 small and micro-sized enterprises in the period of 2010–2012 were chosen as the samples. Based on the logistic regression, the ETCRA model was developed using financial and non-financial variables. In the ETCRA model, the enterprise’s financial performance is assessed from different perspectives: profitability, liquidity, solvency, and activity. Varied model variants have been created using (i) only financial ratios and (ii) financial ratios and non-financial variables. Moreover, the inclusion of non-financial variables in the model does not substantially improve the characteristics of the model. This means that the models that use only financial ratios can be used in practice, and the models that include non-financial variables can also be used. The designed models can be used by suppliers when making decisions of granting a trade credit for small or micro-enterprises.

Prediksi Status Pinjaman Bank dengan Deep Learning Neural Network

“…Padahal, proses penentuan nasabah yang berpotensi bermasalah daripada yang baik adalah suatu hal yang penting karena dapat mempengaruhi usaha perbankan (Patel et al, 2020). Berbagai teknik dalam klasifikasi kreditur apakah digolongkan sebagai kreditur yang bakal melunasi atau akan menunggak pembayaran pinjaman telah banyak dilakukan baik secara akademik maupun dalam industry (Lu et al, 2020) (Kanapickiene & Spicas, 2019) (Golbayani et al, 2020). Model klasifikasi dan regresi telah lama dipakai untuk mengklasifikasikan kualitas pinjaman dalam golongan baik maupun buruk.…”

“…Bagaimanapun diantara 5 klasifier popular seperti Naïve Bayes, Logistic Regression, Random Forest, Decission Tree, dan k-NN, menunjukkan bahwa setiap klasifier memiliki kelebihan dan kelemahan masingmasing (Lu et al, 2020). Pada penelitian Kanapickiene, model logistic regression dikembangkan dengan menggunakan variable finansial dan non-finansial, namun dihasilkan bahwa variable non-finansial secara substansi tidak dapat memberi perkembangan yang berarti pada model yang dibangun (Kanapickiene & Spicas, 2019).…”

The Perception of Business Risks by SMEs in the Czech Republic

“…According to Ključnikov et al (2017), it is necessary to choose business partners very carefully because one of the important factors influencing a company's ability or inability to pay liabilities is payment unwillingness (despite funds a company refuses or forgets to pay). Kanapickiene and Spicas (2019) state that trade credit allows the supplier to increase sales and profits but creates the risk that the customer will not pay, and at the same time, this increases the risk of the supplier's insolvency. Regarding the fact that business credit is the most abundant form of credit, the authors try to avoid risks connected with trade credit by creating a statistical enterprise trade credit risk assessment (ETCRA) model for small and micro enterprises in Lithuania using financial (profitability, liquidity, solvency, activity) and non-financial variables.…”

Impact of Economic Policy Uncertainty on Trade Credit Provision: The Role of Social Trust

“…In our sample covering almost 4000 firms from 16 economies, trade credit provided accounts for 20% of total assets on average. As a crucial sustainable resource of firms [4], trade credit helps maintain firms' R&D investment [5], alters the degree of financial riskiness [6], and has substantial impacts on the profitability and sustainable growth of enterprises [7,8].…”

“…Heteroscedasticity-robust standard errors clustered at the country and quarter levels are reported in parentheses. This table shows the estimated impact of the interaction between EPU and social trust on firms' provision of trade credit based on Equation(6).…”