Diagnosis of Corporate Insolvency Using Massive News Articles for Credit Management

摘要

In the aftermath of the 4th Industrial Revolution, AI and Big data technology have been used in various fields in South Korea, and the techniques are being applied to and complemented in various service fields which were implemented without them before. Especially, in order to secure credit stability for borrowed companies from financial institutions and to preemptively respond to the risks about-by means of online news articles and SNS data-the attempts to forecast the possibility of insolvency and adopt them into actual business are actively conducted by major domestic banks. In this study, we describe several analytical methods, outputs, and problems that are encountered during the processes of developing the unstructured text-based prediction system to detect the possibility of corporate insolvency-which ordered by a national government bank and discuss related issues with a real case. As a result, we have implemented an automatic tagger program for labeling largely unlabeled articles, and newly devised a prediction algorithm of the possibility of corporate insolvency. We achieved the accuracy of 92% (AUC 0.96) in aspect of performance and the hit ratio of 50% among the number of predicted 26 candidates that have the possibility of insolvency. Thus, the result of our study is revealed to be complementary to the financial data analysis sufficiently in performance, but yet have several limitations such as data coverage, reliability, and the characteristics of Korean language.