Smart Contractual Risk Evaluation System Using Machine Learning
Keywords:
Artificial intelligence, Clause extraction, Contract analysis, Contract summarization, Deep learning, LawGPT, LegalT5, Machine learning, Natural language processing, Risk assessmentAbstract
This study presents a systematic literature review and proposes a conceptual framework for smart contractual risk evaluation systems using artificial intelligence (AI) and machine learning (ML), without conducting empirical implementation or experimental validation. AI models, particularly those based on natural language processing (NLP) have shown promising results in automating contract analysis and risk assessment. This review examines key AI-based techniques, including LegalT5, LawGPT, and K-means clustering, in enhancing contract summarisation, clause extraction, and risk evaluation. By leveraging deep learning models fine-tuned on domain-specific legal corpora, these systems enable efficient clause identification, contextual understanding, and semantic risk categorization. Furthermore, the integration of abstractive summarization models with unsupervised clustering algorithms enhances interpretability by grouping similar contractual clauses and identifying potential areas of risk or non-compliance. The paper also examines hybrid AI techniques such as expert systems, artificial neural networks (ANN), and adaptive neuro fuzzy inference systems (ANFIS) that support decision-making under uncertainty. Overall, this review provides a comprehensive overview of current AI-driven methodologies in contract risk analysis and outlines future directions for developing explainable, scalable, and legally compliant intelligent systems.
References
D. Chakrabarti, N. Patodia, U. Bhattacharya, I. Mitra, S. Roy, J. Mandi, N. Roy, and P. Nandy, “Use of artificial intelligence to analyse risk in legal documents for better decision support,” in Proc. IEEE Region 10 Conf. (TENCON 2018), Jeju, South Korea, Oct. 2018, pp. 683–688.
I. Dikmen, G. Eken, H. Erol, and M. T. Birgonul, “Automated construction contract analysis for risk and responsibility assessment using natural language processing and machine learning,” Computers in Industry, vol. 166, Art. no. 104251, Apr. 2025.
M. H. Kazemi and A. Alvanchi, “Application of NLP-based models in automated detection of risky contract statements written in complex script system,” Expert Systems with Applications, vol. 259, Art. no. 125296, Jan. 2025.
Z. M. Yaseen, Z. H. Ali, S. Q. Salih, and N. Al-Ansari, “Prediction of risk delay in construction projects using a hybrid artificial intelligence model,” Sustainability, vol. 12, no. 4, Art. no. 1514, 2020.
A. Erfani and H. Khanjar, “Large language models for construction risk classification: A comparative study,” Buildings, vol. 15, Art. no. 3379, 2025.
B. Shuai, “A rationale-augmented NLP framework to identify unilateral contractual change risk for construction projects,” Computers in Industry, vol. 149, Art. no. 103940, Aug. 2023.
H. Zhou, L. Zhou, B. Gao, W. Huang, W. Huang, J. Zuo, and X. Zhao, “Intelligent identification of risks in construction contract clauses based on semantic reasoning,” Engineering, Construction and Architectural Management, vol. 32, no. 10, pp. 6593–6626, 2025.
S. Xu, C. Zhang, and D. Hong, “BERT-based NLP techniques for classification and severity modeling in basic warranty data study,” Insurance: Mathematics and Economics, vol. 107, pp. 57–67, Nov. 2022.
I. Chalkidis, I. Androutsopoulos, and A. Michos, “Extracting contract elements,” in Proc. 16th Int. Conf. Artificial Intelligence and Law (ICAIL 2017), London, U.K., Jun. 2017, pp. 19–28.
A. Guzmán, S. Ishida, E. Choi, and A. Aoyama, “Artificial intelligence improving safety and risk analysis: A comparative analysis for critical infrastructure,” in Proc. IEEE Int. Conf. Industrial Engineering and Engineering Management (IEEM 2016), Bali, Indonesia, Dec. 2016, pp. 1223–1227.
S. J. Choi, S. W. Choi, J. H. Kim, and E.-B. Lee, “AI and text-mining applications for analyzing contractor’s risk in invitation to bid (ITB) and contracts for engineering procurement and construction (EPC) projects,” Energies, vol. 14, no. 15, Art. no. 4632, 2021.
J. Zhang and N. M. El-Gohary, “Semantic NLP-based information extraction from construction regulatory documents for automated compliance checking,” Journal of Computing in Civil Engineering, vol. 30, no. 2, Art. no. 04015014, Mar. 2016.
V. Carneiro-Diaz, A. Grille-Zallas, and D. Lage-Etchart, “Automated legal analysis of rental contract clauses using large language models,” SoftwareX, vol. 31, Art. no. 102337, Sep. 2025.
W. Y. Mok and J. R. Mok, “Classification of breach of contract court decision sentences,” in Proc. 3rd Workshop Automated Semantic Analysis of Information in Legal Text (ASAIL 2019), Montreal, QC, Canada, Jun. 21, 2019, pp. 1–8.
R. S. Al-Sabah and S. S. Al-Enezi, “Reducing contract disputes: A comparative analysis of FIDIC and GCC standard general conditions of contract for construction projects,” Journal of Engineering Research, Oct. 2024.
X. Kholdorov, “Natural language processing applications for document automation and claim analysis in construction contracts,” International Journal of Business Intelligence Research (IJBIR), vol. 1, no. 2, pp. 1–6, 2022.
F. ul Hassan and T. Le, “Extraction of activities information from construction contracts using natural language processing (NLP) methods to support scheduling,” in Proc. Construction Research Congress 2022, Arlington, VA, USA, Mar. 2022, pp. 773–781.
S. Wong, C. Zheng, X. Su, and Y. Tang, “Construction contract risk identification based on knowledge-augmented language models,” Computers in Industry, vols. 157–158, Art. no. 104082, May 2024.
S. Bashir et al., “Requirements ambiguity detection and explanation with LLMs: An industrial study,” Tech. Rep., 2024.
N. Rastogi and M. Trivedi, “Risk mitigation in construction contracts,” Tech. Rep., 2016.
