Named Entity Recognition for Payment Data Using NLP

TL;DR

This paper evaluates various NLP-based Named Entity Recognition models for extracting structured information from payment data, demonstrating that domain-specific transformer models significantly outperform traditional methods in accuracy and efficiency.

Contribution

It introduces PaymentBERT, a hybrid model that combines financial domain embeddings with contextual representations, achieving state-of-the-art performance in payment data NER tasks.

Findings

Fine-tuned BERT achieves 94.2% F1-score, outperforming CRF by 12.8 points.

PaymentBERT reaches 95.7% F1-score with real-time processing.

Transformer-based models outperform traditional NER approaches in payment data extraction.

Abstract

Named Entity Recognition (NER) has emerged as a critical component in automating financial transaction processing, particularly in extracting structured information from unstructured payment data. This paper presents a comprehensive analysis of state-of-the-art NER algorithms specifically designed for payment data extraction, including Conditional Random Fields (CRF), Bidirectional Long Short-Term Memory with CRF (BiLSTM-CRF), and transformer-based models such as BERT and FinBERT. We conduct extensive experiments on a dataset of 50,000 annotated payment transactions across multiple payment formats including SWIFT MT103, ISO 20022, and domestic payment systems. Our experimental results demonstrate that fine-tuned BERT models achieve an F1-score of 94.2% for entity extraction, outperforming traditional CRF-based approaches by 12.8 percentage points. Furthermore, we introduce PaymentBERT, a novel hybrid architecture combining domain-specific financial embeddings with contextual representations, achieving state-of-the-art performance with 95.7% F1-score while maintaining real-time processing capabilities. We provide detailed analysis of cross-format generalization, ablation studies, and deployment considerations. This research provides practical insights for financial institutions implementing automated sanctions screening, anti-money laundering (AML) compliance, and payment processing systems.