Applied data science in financial industry: Natural language processing techniques for bank policies

Abstract

In a time when the employment of Natural Language Processing techniques in domains such as biomedicine, national security, finance and law, is flourishing, this study takes a deep look in its application in policy documents. Besides providing an overview of the current state of the literature that treats these concepts, the study at hand implements a set of unprecedented Natural Language Processing techniques on internal bank policies. The implementation of these techniques, together with the results that derive from the experiment and the experts’ evaluation, introduce a Meta-Algorithmic Modelling framework for processing internal business policies. This framework relies on three Natural Language Processing techniques, namely information extraction, automatic summarization and automatic keyword extraction. For the reference extraction and keyword extraction tasks we calculated Precision, Recall and F-scores. For the former we obtained 0.99, 0.84, and 0.89; for the latter we obtained 0.79, 0.87 and 0.83, respectively. Finally, our summary extraction approach was positively evaluated using a qualitative assessment.