Credit Risk Scoring Forecasting Using a Time Series Approach

Abstract

Credit risk assessments are vital to the operations of financial institutions. These activities depend on the availability of data. In many cases, the records of financial data processed by the credit risk models are frequently incomplete. Several methods have been proposed in the literature to address the problem of missing values. Yet, when assessing a company, there are some critical features that influence the final credit assessment. The availability of financial data also depends strongly on the country to which the company belongs. This is due to the fact there are countries where the regulatory frameworks allow companies to not publish their financial statements. In this paper, we propose a framework that can process historical credit assessments of a large number of companies, which were performed between 2008 and 2019, in order to treat the data as time series. We then used these time series data in order to fit two different models: a traditional statistics model (an autoregressive moving average model) and a machine-learning based model (a gradient boosting model). This approach allowed the generation of future credit assessments without the need for new financial data.