ARCHIVES
Original Article
An Intelligent Framework for Financial Contract Risk Analysis using Transformer-Based Clause Modelling and Graph Neural Networks
Alok Kushwaha1
Dr. Mohammed Abuzar A2
1 M.Sc. Data Science, SIES College of Arts, Science and Commerce, (Empowered Autonomous), Mumbai, India. 2 Head of Department, Data Science, SIES College of Arts, Science and Commerce (Empowered Autonomous), Mumbai, India.
Published Online: January-April 2026
Pages: 293-300
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260501041
References
1. I. Chalkidis, M. Fergadiotis, P. Malakasiotis, N. Aletras, and I. Androutsopoulos, “LEGAL-BERT: The Muppets straight out of Law School,” in Findings of the Association for Computational Linguistics: EMNLP 2020, Online, Nov. 2020, pp. 2898–2904, doi: 10.18653/v1/2020.findings-emnlp.261. arXiv:2010.02559.
2. D. Hendrycks, C. Burns, A. Chen, and S. Ball, “CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review,” arXiv preprint arXiv:2103.06268, Mar. 2021. (Presented at NeurIPS 2021 Datasets and Benchmarks Track.)
3. S. Moon, J. Lee, S. Lee, J. Oh, and S. Chi, “Automated detection of contractual risk clauses from construction specifications using bidirectional encoder representations from transformers (BERT),” Automation in Construction, vol. 142, art. 104486, Oct. 2022, doi: 10.1016/j.autcon.2022.104486.
4. A. Louis, G. van Dijck, and G. Spanakis, “Finding the Law: Enhancing Statutory Article Retrieval via Graph Neural Networks,” in Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2023), Dubrovnik, Croatia, May 2023, pp. 2761–2776, doi: 10.18653/v1/2023.eacl-main.203. arXiv:2301.12847.
5. F. Ariai, J. Mackenzie, and G. Demartini, “Natural Language Processing for the Legal Domain: A Survey of Tasks, Datasets, Models, and Challenges,” ACM Computing Surveys, vol. 58, no. 6, art. 163, Apr. 2026, pp. 1–37, doi: 10.1145/3777009. arXiv:2410.21306 [cs.CL].
6. K. Vuthoo, S. Khetarpaul, and L. Venkata Subramaniam, “Datasets, Models and NLP Techniques for Legal Contracts – A Survey,” Authorea preprint, Oct. 2025, doi: 10.22541/au.176044880.08032215/v1.
7. P. Shah, S. Joshi, and A. K. Pandey, “Legal Clause Extraction From Contract Using Machine Learning with Heuristics Improvement,” in 2018 4th International Conference on Computing Communication and Automation (ICCCA), Greater Noida, India, Dec. 2018, pp. 1–5, doi: 10.1109/CCAA.2018.8777602.
8. Y. Tang, X. Wang, Y. Zhang, and J. Xiao, “CaseGNN: Graph Neural Networks for Legal Case Retrieval with Text-Attributed Graphs,” arXiv preprint arXiv:2312.11229, Dec. 2023.
9. S. Tong, Y. Liu, and Z. Li, “Legal Judgment Prediction via graph boosting with constraints,” Information Processing & Management, vol. 61, no. 3, art. 103678, May 2024, doi: 10.1016/j.ipm.2024.103678.
10. J. Niklaus, V. Sovukluk, M. Stürmer, I. Chalkidis, and M. Grabmair, “MultiLegalPile: A 689GB Multilingual Legal Corpus,” in Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024), Bangkok, Thailand, Aug. 2024, pp. 14947–14965, doi: 10.18653/v1/2024.acl-long.805.
11. C. Niklaus, M. Stürmer, I. Chalkidis, and M. Grabmair, “Legal-XLM-R: A Multilingual Legal Pre-trained Language Model,” arXiv preprint (related to MultiLegalPile extensions), 2024.
12. A. Singh, “A Survey of Classification Tasks and Approaches for Legal Contracts,” Artificial Intelligence Review, vol. 58, art. 380, Oct. 2025, doi: 10.1007/s10462-025-11359-8. arXiv:2507.21108.
13. S. Jha, “Natural Language Processing in Legal Practice: An Empirical Study on Contract Review and Risk Assessment,” in 2025 IEEE International Conference on Big Data and Smart Computing (BigComp), 2025, pp. 1–8, doi: 10.1109/BigComp.2025.11135214.
14. M. H. Kazemi, S. Moon, and S. Chi, “Application of NLP-based models in automated detection of risky clauses in construction contracts,” Expert Systems with Applications, vol. 255, art. 124678, Dec. 2025, doi: 10.1016/j.eswa.2024.124678.
15. Q. Wang, Y. Zhang, and Z. Li, “D2GCLF: Document-to-Graph Classifier for Legal Document Classification,” in Findings of the Association for Computational Linguistics: NAACL 2022, Seattle, WA, Jul. 2022, pp. 170–183.
16. M. Dechtiar, “Software engineering meets legal texts: LLMs for auto detection of contract smells,” Journal of High Technology Management Research, vol. 36, no. 1, art. 100022, 2025, doi: 10.1016/j.hitech.2025.100022.
17. R. Ricciardi, “A multilingual BERT-based classification of reviews for enhanced visitors’ experience analysis,” Scientific Reports, vol. 15, art. 9418, Aug. 2025, doi: 10.1038/s41598-025-09418-9.
18. I. Chalkidis et al., “LLMs for Law: Evaluating Legal-Specific LLMs on Contract Understanding,” arXiv preprint arXiv:2508.07849, Aug. 2025.
19. Y. Koreeda and C. D. Manning, “ContractNLI: A Dataset for Document-level Natural Language Inference for Contracts,” in Findings of the Association for Computational Linguistics: EMNLP 2021, Punta Cana, Dominican Republic, Nov. 2021, pp. 1907–1919.
20. I. Chalkidis, A. Xenos, and I. Androutsopoulos, “Legal Judgment Prediction: If You Are Going to (Predict) the Future, You Need to Look at the Past,” Artificial Intelligence and Law, vol. 30, no. 1, pp. 1–35, Mar. 2022, doi: 10.1007/s10506-021-09295-0.
21. F. Ariai and G. Demartini, “Challenges and Opportunities in Multilingual Legal NLP: From Monolingual to Cross-Lingual Contract Analysis,” in Proceedings of the 2025 ACL Workshop on Legal NLP (LegalNLP 2025), Vienna, Austria, Jul. 2025.
22. M. Lippi and G. Torroni, “Context-independent claim detection for argument mining,” in Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI), Buenos Aires, Argentina, Jul. 2015, pp. 185–191.
23. M. Ruggeri, F. Zollo, and W. Quattrociocchi, “Risky clause detection in legal contracts via deep learning,” arXiv preprint, 2022.
24. P. Lewis et al., “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks,” in Proc. 34th Conf. Neural Inf. Process. Syst. (NeurIPS 2020), Vancouver, Canada, 2020.
25. Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and P. S. Yu, “A Comprehensive Survey on Graph Neural Networks,” IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 1, pp. 4–24, Jan. 2021, doi: 10.1109/TNNLS.2020.2978386.
26. [Dataset] Government of India, General Financial Rules (Updated2024). https://doe.gov.in/files/circulars_document/FInal_GFR_upto_31_07_2024.pdf
2. D. Hendrycks, C. Burns, A. Chen, and S. Ball, “CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review,” arXiv preprint arXiv:2103.06268, Mar. 2021. (Presented at NeurIPS 2021 Datasets and Benchmarks Track.)
3. S. Moon, J. Lee, S. Lee, J. Oh, and S. Chi, “Automated detection of contractual risk clauses from construction specifications using bidirectional encoder representations from transformers (BERT),” Automation in Construction, vol. 142, art. 104486, Oct. 2022, doi: 10.1016/j.autcon.2022.104486.
4. A. Louis, G. van Dijck, and G. Spanakis, “Finding the Law: Enhancing Statutory Article Retrieval via Graph Neural Networks,” in Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2023), Dubrovnik, Croatia, May 2023, pp. 2761–2776, doi: 10.18653/v1/2023.eacl-main.203. arXiv:2301.12847.
5. F. Ariai, J. Mackenzie, and G. Demartini, “Natural Language Processing for the Legal Domain: A Survey of Tasks, Datasets, Models, and Challenges,” ACM Computing Surveys, vol. 58, no. 6, art. 163, Apr. 2026, pp. 1–37, doi: 10.1145/3777009. arXiv:2410.21306 [cs.CL].
6. K. Vuthoo, S. Khetarpaul, and L. Venkata Subramaniam, “Datasets, Models and NLP Techniques for Legal Contracts – A Survey,” Authorea preprint, Oct. 2025, doi: 10.22541/au.176044880.08032215/v1.
7. P. Shah, S. Joshi, and A. K. Pandey, “Legal Clause Extraction From Contract Using Machine Learning with Heuristics Improvement,” in 2018 4th International Conference on Computing Communication and Automation (ICCCA), Greater Noida, India, Dec. 2018, pp. 1–5, doi: 10.1109/CCAA.2018.8777602.
8. Y. Tang, X. Wang, Y. Zhang, and J. Xiao, “CaseGNN: Graph Neural Networks for Legal Case Retrieval with Text-Attributed Graphs,” arXiv preprint arXiv:2312.11229, Dec. 2023.
9. S. Tong, Y. Liu, and Z. Li, “Legal Judgment Prediction via graph boosting with constraints,” Information Processing & Management, vol. 61, no. 3, art. 103678, May 2024, doi: 10.1016/j.ipm.2024.103678.
10. J. Niklaus, V. Sovukluk, M. Stürmer, I. Chalkidis, and M. Grabmair, “MultiLegalPile: A 689GB Multilingual Legal Corpus,” in Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024), Bangkok, Thailand, Aug. 2024, pp. 14947–14965, doi: 10.18653/v1/2024.acl-long.805.
11. C. Niklaus, M. Stürmer, I. Chalkidis, and M. Grabmair, “Legal-XLM-R: A Multilingual Legal Pre-trained Language Model,” arXiv preprint (related to MultiLegalPile extensions), 2024.
12. A. Singh, “A Survey of Classification Tasks and Approaches for Legal Contracts,” Artificial Intelligence Review, vol. 58, art. 380, Oct. 2025, doi: 10.1007/s10462-025-11359-8. arXiv:2507.21108.
13. S. Jha, “Natural Language Processing in Legal Practice: An Empirical Study on Contract Review and Risk Assessment,” in 2025 IEEE International Conference on Big Data and Smart Computing (BigComp), 2025, pp. 1–8, doi: 10.1109/BigComp.2025.11135214.
14. M. H. Kazemi, S. Moon, and S. Chi, “Application of NLP-based models in automated detection of risky clauses in construction contracts,” Expert Systems with Applications, vol. 255, art. 124678, Dec. 2025, doi: 10.1016/j.eswa.2024.124678.
15. Q. Wang, Y. Zhang, and Z. Li, “D2GCLF: Document-to-Graph Classifier for Legal Document Classification,” in Findings of the Association for Computational Linguistics: NAACL 2022, Seattle, WA, Jul. 2022, pp. 170–183.
16. M. Dechtiar, “Software engineering meets legal texts: LLMs for auto detection of contract smells,” Journal of High Technology Management Research, vol. 36, no. 1, art. 100022, 2025, doi: 10.1016/j.hitech.2025.100022.
17. R. Ricciardi, “A multilingual BERT-based classification of reviews for enhanced visitors’ experience analysis,” Scientific Reports, vol. 15, art. 9418, Aug. 2025, doi: 10.1038/s41598-025-09418-9.
18. I. Chalkidis et al., “LLMs for Law: Evaluating Legal-Specific LLMs on Contract Understanding,” arXiv preprint arXiv:2508.07849, Aug. 2025.
19. Y. Koreeda and C. D. Manning, “ContractNLI: A Dataset for Document-level Natural Language Inference for Contracts,” in Findings of the Association for Computational Linguistics: EMNLP 2021, Punta Cana, Dominican Republic, Nov. 2021, pp. 1907–1919.
20. I. Chalkidis, A. Xenos, and I. Androutsopoulos, “Legal Judgment Prediction: If You Are Going to (Predict) the Future, You Need to Look at the Past,” Artificial Intelligence and Law, vol. 30, no. 1, pp. 1–35, Mar. 2022, doi: 10.1007/s10506-021-09295-0.
21. F. Ariai and G. Demartini, “Challenges and Opportunities in Multilingual Legal NLP: From Monolingual to Cross-Lingual Contract Analysis,” in Proceedings of the 2025 ACL Workshop on Legal NLP (LegalNLP 2025), Vienna, Austria, Jul. 2025.
22. M. Lippi and G. Torroni, “Context-independent claim detection for argument mining,” in Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI), Buenos Aires, Argentina, Jul. 2015, pp. 185–191.
23. M. Ruggeri, F. Zollo, and W. Quattrociocchi, “Risky clause detection in legal contracts via deep learning,” arXiv preprint, 2022.
24. P. Lewis et al., “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks,” in Proc. 34th Conf. Neural Inf. Process. Syst. (NeurIPS 2020), Vancouver, Canada, 2020.
25. Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and P. S. Yu, “A Comprehensive Survey on Graph Neural Networks,” IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 1, pp. 4–24, Jan. 2021, doi: 10.1109/TNNLS.2020.2978386.
26. [Dataset] Government of India, General Financial Rules (Updated2024). https://doe.gov.in/files/circulars_document/FInal_GFR_upto_31_07_2024.pdf
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