The value of text for small business default prediction: A deep learning approach
This addresses credit risk modeling for mSME lending, offering a partly automated solution, but it is incremental as it applies existing NLP methods to a specific domain problem.
The paper tackled the challenge of predicting default for micro, small, and medium enterprises (mSMEs) by using deep learning (BERT) to analyze 60,000 textual loan assessments, finding that text alone was effective for prediction (with AUC and Brier Score metrics), but adding traditional data did not improve performance, and performance depended on text length.
Compared to consumer lending, Micro, Small and Medium Enterprise (mSME) credit risk modelling is particularly challenging, as, often, the same sources of information are not available. Therefore, it is standard policy for a loan officer to provide a textual loan assessment to mitigate limited data availability. In turn, this statement is analysed by a credit expert alongside any available standard credit data. In our paper, we exploit recent advances from the field of Deep Learning and Natural Language Processing (NLP), including the BERT (Bidirectional Encoder Representations from Transformers) model, to extract information from 60 000 textual assessments provided by a lender. We consider the performance in terms of the AUC (Area Under the receiver operating characteristic Curve) and Brier Score metrics and find that the text alone is surprisingly effective for predicting default. However, when combined with traditional data, it yields no additional predictive capability, with performance dependent on the text's length. Our proposed deep learning model does, however, appear to be robust to the quality of the text and therefore suitable for partly automating the mSME lending process. We also demonstrate how the content of loan assessments influences performance, leading us to a series of recommendations on a new strategy for collecting future mSME loan assessments.