ABSTRACT
The most critical challenge facing micro-, small-, and medium-sized enterprises (MSMEs), especially startups and knowledge-based companies, is their difficulty in accessing financing. Recent fintech developments may be able to mitigate this problem, as they provide a solution for financial institutions to control their operational costs and simultaneously manage their credit risk. Lendtech companies, as part of the fintech industry, provide some solutions for the operation of lending and mitigating credit risk. Data availability is crucial in running an eligible model for calculating the probability of default for MSMEs. Technological progress, like artificial intelligence (AI) and big data change, increases the efficiency of credit risk assessment models. This chapter develops a conceptual model for assessing the credit risk of knowledge-based companies using quantitative and qualitative variables by employing a statistical analysis technique, namely principal component analysis (PCA). The results demonstrate that the combination of financial and non-financial variables is required to assess credit risk, the evaluation of which should be based on components such as total equities, total revenues, and age of the company. Collecting financial and non-financial information and making big data will help assess credit risk with higher accuracy.
