Open Access Open Access  Restricted Access Subscription Access

Contract IQ AI: Smart Contract Analyzer and Risk Scanner

Lakshmi Sharma, Devidas Thosar, Satyam Mishra, Raj Gupta, Santosh Singh

Abstract


As we all know that today legal contracts volume is increasing day by day also with large number of papers it becomes very difficult to read all those clauses in contract which sometimes can turn problematic if clauses are not proper read. To avoid such problem, this paper we will be seeing about an application called as ContractIQ AI, a web based system that integrates Natural Language Processing, GPT and BERT models these system can easily help you summarize big contracts in easy manner which not only saves times but also helps user to understand what are the clauses mentioned in contract. Also, this system consists of a Q & A chatbot where users can ask their doubts if any related to contract making it one of the efficient model.


Full Text:

PDF

References


Raffel, C. (2020). Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research, 21(1), 5485–5551.

Brown, T. (2020). Language models are few-shot learners. In Advances in Neural Information Processing Systems, 33, 1877–1901.

Aejas, B., Belhi, A., Zhang, H., & Bouras, A. (2024). Deep learning-based automatic analysis of legal contracts: A named entity recognition benchmark. Neural Computing and Applications, 36, 1–17. https://doi.org/10.1007/s00521-024-09869-7

Singh, A., Rose, A. P., Verma, A., Venkatesan, S., V. L., & Ghaisas, S. (2024). A data decomposition-based hierarchical classification method for multi-label classification of contractual obligations for the purpose of their governance. Scientific Reports, 14(1), 12755. https://doi.org/10.1038/s41598-024-63648-x

Yadav, D., Katna, R., Yadav, A. K., & Morato, J. (2022). Feature-based automatic text summarization methods: A comprehensive state-of-the-art survey. IEEE Access, 10, 133981–134003. https://doi.org/10.1109/ACCESS.2022.3231016

Mukherjee, R., Ghosh, K., Goyal, P., & Ghosh, S. (2022). Legal case document summarization: Extractive and abstractive methods and their evaluation. In Proceedings of the 2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL) (pp. 1048–1064). https://doi.org/10.18653/v1/2022.aacl-main.77


Refbacks

  • There are currently no refbacks.