Academic

Named Entity Recognition for Payment Data Using NLP

arXiv:2602.14009v1 Announce Type: new 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-specifi

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Srikumar Nayak · · 1 min read · 2 views
#cs.CL #cs.AI #cs.LG

arXiv:2602.14009v1 Announce Type: new 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.

Executive Summary

The article titled 'Named Entity Recognition for Payment Data Using NLP' explores the application of advanced NLP techniques to automate the extraction of structured information from unstructured payment data. The study evaluates several state-of-the-art NER algorithms, including CRF, BiLSTM-CRF, BERT, and FinBERT, on a dataset of 50,000 annotated payment transactions across various formats such as SWIFT MT103, ISO 20022, and domestic payment systems. The research demonstrates that fine-tuned BERT models achieve superior performance with an F1-score of 94.2%, outperforming traditional CRF-based approaches. Additionally, the article introduces PaymentBERT, a novel hybrid architecture that combines domain-specific financial embeddings with contextual representations, achieving a state-of-the-art F1-score of 95.7% while maintaining real-time processing capabilities. The study provides insights into cross-format generalization, ablation studies, and deployment considerations, offering practical implications for financial institutions in areas such as sanctions screening, AML compliance, and payment processing.

Key Points

  • ▸ Evaluation of state-of-the-art NER algorithms for payment data extraction.
  • ▸ Fine-tuned BERT models achieve an F1-score of 94.2%, outperforming CRF-based approaches.
  • ▸ Introduction of PaymentBERT, a novel hybrid architecture achieving 95.7% F1-score.
  • ▸ Comprehensive analysis of cross-format generalization and deployment considerations.
  • ▸ Practical implications for financial institutions in sanctions screening and AML compliance.

Merits

Comprehensive Evaluation

The study provides a thorough evaluation of various NER algorithms, including traditional and transformer-based models, on a large and diverse dataset of payment transactions.

Innovative Architecture

The introduction of PaymentBERT, a hybrid architecture combining domain-specific financial embeddings with contextual representations, represents a significant advancement in the field.

Practical Insights

The research offers practical insights and recommendations for financial institutions looking to implement automated payment processing systems.

Demerits

Dataset Limitations

While the dataset is large, it may not fully capture the diversity of payment formats and transactions in the real world, potentially limiting the generalizability of the findings.

Computational Resources

The use of transformer-based models, while highly effective, requires significant computational resources, which may be a barrier for smaller financial institutions.

Real-World Deployment

The study does not extensively address the challenges and considerations of deploying these models in real-world environments, such as integration with existing systems and regulatory compliance.

Expert Commentary

The article presents a rigorous and well-reasoned analysis of NER techniques for payment data extraction, demonstrating the superior performance of transformer-based models, particularly BERT and the novel PaymentBERT architecture. The study's comprehensive evaluation and innovative contributions are notable strengths, offering valuable insights for both academia and industry. However, the limitations related to dataset diversity and computational resources should be acknowledged. The practical implications for financial institutions are significant, particularly in the areas of sanctions screening and AML compliance. The study also raises important questions about the regulatory and policy implications of deploying such advanced NLP techniques in the financial sector. Overall, this research represents a significant advancement in the field and provides a solid foundation for future work in automated payment processing and financial compliance.

Recommendations

  • ✓ Further research should explore the generalizability of the findings to a broader range of payment formats and transactions.
  • ✓ Financial institutions should consider the computational requirements and potential integration challenges when implementing advanced NER techniques.
  • ✓ Regulatory bodies should develop guidelines to address the use of AI and NLP techniques in financial transactions, ensuring compliance with data privacy and security standards.

Sources

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