The modeling of credit risk within the consortium based financial settings brings about great complexity, owing to the distributed ownership of data and the strict privacy considerations. Traditional risk assessment methods are usually based on a centralized data processing, and this means that sensitive financial data is at risk of misuse, there is a question of data integrity and there is lack of transparent audit trails. The recent development of privacy-preserving computation approaches like homomorphic encryption (HE) creates new possibilities as they allow performing mathematical operations on encrypted credit data without disclosing unencrypted values. Nonetheless, although HE maintains privacy, it does not intrinsically imply transparency, and thus it is hard to tell whether the intermediate computations are accurate, particularly in the multi-institutional scenario. In order to overcome these shortcomings, the block chain technology is proposed as an additional audit layer. The decentralized ledger design of block chain makes the computation log immutable and traceable, so that the participants of a consortium can verify the result independently without revealing the origin data. The combination of HE with block chain provides a secure framework of federated credit risk modelling, which concurrently protects privacy, enhanced computational verifiability and transparent decision-making. In this paper, we suggest a hybrid system, which integrates HE-based secure computation with block chain-verified audit trail in the credit risk assessment in consortium networks. The performance of the framework is assessed on a simulated dataset in comparison to standard HE-only methods. In both partitioning’s risk scores are calculated and the models are evaluated against standard performance measures, such as average prediction variance, computational efficiency, auditability and robustness to stochastic input perturbations. Findings show that the block chain-audited HE model is effective in improving robustness and accountability of the credit evaluations. The suggested framework is a decent solution to cooperative financial ecosystems where privacy, accuracy, and traceability are essential. The results lead to the future of secure and auditable financial modelling in decentralized and privacy-preserving settings.