Interpretable Multi-Label Rule Extraction from Financial Regulatory Texts Using Transformer Models: A Comparative Study on EURLEX

Bhalilkar, Atharva Sanjay (2025) Interpretable Multi-Label Rule Extraction from Financial Regulatory Texts Using Transformer Models: A Comparative Study on EURLEX. Masters thesis, Dublin, National College of Ireland.

Preview	PDF (Master of Science) Download (3MB) | Preview
Preview	PDF (Configuration Manual) Download (3MB) | Preview

Abstract

Increased complexities of the financial regulations require automated systems that can extract interpretable representations of formulated rules into legal texts. The following paper describes the use of transformer-based models such as BERT, RoBERTa, and FinBERT to carry out multi-label classification of regulatory documents. The consideration of the whole spectrum of the NLP pipeline, encompassing such aspects as data preprocessing, label binarization, feature engineering, model training, and evaluation, is carried out by applying EURLEX dataset of the annotated EU legal texts. A traditional rule-based classification technique is also formulated to be used as benchmarking of performance. Micro and macro F1 score, precision, recall and confusion matrices are utilized to conduct the model evaluation. Interpretability is used in the form of SHAP, LIME, and attention visualization methods, where the input of the model prediction can be seen and rule importance can be learned. The assessments characterize that the transformer models have bettered the predictive accuracy and labelling coverage than the rule-based basic one and can generate scalable, albeit not completely explicable, outputs. This study would be a contribution to the field of law NLP since it proves that transformer-based models are effective in classifying and understanding the structure of financial regulatory documents, and they hold practical potential to automate compliance and application of legal AI.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Singh, Jaswinder UNSPECIFIED
Subjects:	H Social Sciences > HG Finance Q Science > QH Natural history > QH301 Biology > Methods of research. Technique. Experimental biology > Data processing. Bioinformatics > Artificial intelligence Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Artificial intelligence P Language and Literature > P Philology. Linguistics > Computational linguistics. Natural language processing
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	24 Aug 2026 15:52
Last Modified:	24 Aug 2026 15:52
URI:	https://norma.ncirl.ie/id/eprint/9620

Actions (login required)

View Item