  This paper explores the use of clustering methods and machine learning
algorithms, including Natural Language Processing (NLP), to identify and
classify problems identified in credit risk models through textual information
contained in validation reports. Using a unique dataset of 657 findings raised
by validation teams in a large international banking group between January 2019
and December 2022. The findings are classified into nine validation dimensions
and assigned a severity level by validators using their expert knowledge. The
authors use embedding generation for the findings' titles and observations
using four different pre-trained models, including "module\_url" from
TensorFlow Hub and three models from the SentenceTransformer library, namely
"all-mpnet-base-v2", "all-MiniLM-L6-v2", and "paraphrase-mpnet-base-v2". The
paper uses and compares various clustering methods in grouping findings with
similar characteristics, enabling the identification of common problems within
each validation dimension and severity. The results of the study show that
clustering is an effective approach for identifying and classifying credit risk
model problems with accuracy higher than 60\%. The authors also employ machine
learning algorithms, including logistic regression and XGBoost, to predict the
validation dimension and its severity, achieving an accuracy of 80\% for
XGBoost algorithm. Furthermore, the study identifies the top 10 words that
predict a validation dimension and severity. Overall, this paper makes a
contribution by demonstrating the usefulness of clustering and machine learning
for analyzing textual information in validation reports, and providing insights
into the types of problems encountered in the development and validation of
credit risk models.
