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With the increasing number of legal and financial contracts, the analysis of contracts through a manual system of analysis has become inefficient. Hence, the need for an automated system that can not only extract the clauses from the contracts but also interpret them to assess the risks involved with the contracts arises. This paper proposes a machine learning-based system for the analysis of legal contracts through the application of Natural Language Processing techniques. This system can be applied to both scanned documents and digital documents by the application of a hybrid approach to the extraction of the content from the documents. The system will be capable of analysing the content of the documents by the application of a Legal-BERT model that has been fine-tuned on a dataset comprising the General Financial Rules (GFR) and additional legal contract clause datasets. The system will be capable of risk analysis by the application of a risk-aware clause classification system that not only classifies the clauses into various types of laws but also determines the risk level of the contracts by the application of three risk levels: low, medium, and high. To enhance interpretability and robustness, the framework includes a hybrid risk assessment mechanism that leverages the power of the transformer model’s predictions and rule-based compliance verification. Moreover, the framework includes a GNN approach to analyse the structural relationships between clauses in the contract document. The experimental results demonstrate that the proposed approach can effectively improve the accuracy and reliability of the automated risk detection process using the domain-specific transformer models and rule-based reasoning approach. The proposed approach can be considered an efficient and intelligent way to analyse contracts and can be effectively utilized by legal professionals and financial analysts.