Start Date
10-12-2017 12:00 AM
Description
Credit risk is at the core of banking business and its adequate measurement is crucial for financial institutions. Due to lack of historical default data and heterogeneity of customers, qualitative expert-based information is an important factor in measuring the creditworthiness of large companies. However, such information is often extracted manually, causing inefficiencies and possible subjectivity. In this paper we develop the RatingBot: a text mining based rating approach, which efficiently and objectively models relevant qualitative information based on annual reports. It combines both the literature on text mining in finance and machine learning in credit rating to derive the credit rating of a company. We evaluate our approach on two datasets: a publicly available one that facilitates replicability, and a dataset provided by a major European bank representing real-world scenario. The results show that RatingBot delivers additional predictive power and should be considered in future research on credit rating models.
Recommended Citation
Hristova, Diana; Probst, Johannes; and Eckrich, Emanuel, "RatingBot: A Text Mining Based Rating Approach" (2017). ICIS 2017 Proceedings. 8.
https://aisel.aisnet.org/icis2017/DataScience/Presentations/8
RatingBot: A Text Mining Based Rating Approach
Credit risk is at the core of banking business and its adequate measurement is crucial for financial institutions. Due to lack of historical default data and heterogeneity of customers, qualitative expert-based information is an important factor in measuring the creditworthiness of large companies. However, such information is often extracted manually, causing inefficiencies and possible subjectivity. In this paper we develop the RatingBot: a text mining based rating approach, which efficiently and objectively models relevant qualitative information based on annual reports. It combines both the literature on text mining in finance and machine learning in credit rating to derive the credit rating of a company. We evaluate our approach on two datasets: a publicly available one that facilitates replicability, and a dataset provided by a major European bank representing real-world scenario. The results show that RatingBot delivers additional predictive power and should be considered in future research on credit rating models.