• automated compliance
• machine learning
• natural language processing
• regulatory technology
• sentence vector representation
• text similarity

The increase in supervisory scrutiny activities following the 2008 financial crisis has led to a significant increase in the complexity and cost of banking compliance functions. To respond to this, financial institutions have been investing in RegTech, which are technological solutions that leverage Artificial Intelligence to optimize compliance, conformity, and adherence to rules, regulations, laws, and reports. Natural Language Processing has been particularly important in this regard, as it provides the tools to make regulatory textual data - which is typically unstructured - machine-readable, actionable, and interpretable, in order to extract various types of relevant information.
This paper presents an initial attempt to automate the monitoring of noncompliance risk, using measures of text similarity, with the aim of identifying all documents affected by the hypothetical promulgation and publication of a set of laws, normative documents, and regulations within a collection of corporate regulations. Two experiments were conducted, one using a discrete vector representation of the texts through an unsupervised approach, and the other using a continuous, contextualized representation through a supervised approach, using a labeled dataset created ad hoc. Finally, the two models were tested on the same real-world use case, which was achieving GDPR compliance.