Credit risk analysis of small and medium - sized enterprise loans

Abstract

Analyzing the credit risk is important in banking systems to ensure that debtors pay the loans regularly, on schedule. The inability of managing the credit risk may lead severe losses in financial institutes. Every financial institute must predict and manage the credit risk to avoid financial crises. Hence, finding an effective method for credit risk analysis is vital. Among various types of loans, Small and Medium-sized Enterprise (SME) loans dominate since SMEs are considered the backbone of any economy. With the higher amount of SME loans, the associated risk also gets increased. SME sector is considered as risky and costly than the large enterprises. The amount of non-performing SME loans has increased at a higher pace throughout the last few quarters in Sri Lanka. Hence, this study analyzes the credit risk of SME loans by using a data set received from a financial institute in Sri Lanka. It recognizes financial and non-financial attributes affecting the credit risk of SME loans. The data mining techniques were used to analyze the data and extract knowledge. It is an emerging technology that provides significant improvements in terms of making accurate decisions. Data mining is used widely for financial analysis since it facilitates knowledge extraction from large data sets and making effective decisions. The study will help financial institutes to predict the credit risk of potential SME borrowers and avoid inefficiencies in the lending process. It identifies the credit risk of debtors in different aspects. Ultimately, it provides valuable insights into effective decision making of an economy.