Zero-Knowledge Proof Based Blockchain-enabled Personal Credit Scoring with SHAP Explainability

As the field of personal lending grows exponentially, the risk of credit default has become an issue of growing significance. Meanwhile, responsible and fair personal credit evaluation in safe environments is a key area of research in the field of financial technologies. Even the current strategies have a few problems in the real-world application: the imbalanced classes of credit data creates low detectability of delinquent cases, a centralized scoring solution does not provide credible verification methods, which makes them easily manipulated, and machine learning model decision-making is not typically transparent. In resolving these problems, this study introduces a verifiable and interpretable model of personal credit assessment that combines ensemble learning, blockchain-based data notarization, and zero-knowledge proofing. To begin with, a credit assessment model using soft-voting ensemble, with SMOTE oversampling is developed, to integrate the complementary advantages of random forest and gradient boosting classifier and thus would be useful in enhancing the detection of delinquent users. Second, a zero-knowledge proof system developed under the Groth16 protocol and integrated with blockchain notarization to make the verifiability and the immutability of the scoring outcomes accessible to protect data privacy. Third, the entire SHAP feature attribution vectors are hashed with the hash-digest algorithm SHA-256 and stored on-chain, providing end-to-end auditability over scoring results to the underlying rationale decision and raising decision transparency and regulatory acceptability. In the experiments conducted on UCI Germany credit dataset, the suggested method proves to be more efficient than the baseline models in major measurements including full term and Recall. The Recall was improved by approximately 30 percentage points and the AUC increased from 0.8938 to 0.9024. Both on-chain audit and zero-knowledge proof verification rates are 100%, and SHAP notarization completion is 100%, which show general benefits of the framework in predictive accuracy, privacy protection, model explainability, and result verifiability. The work presents a practicable technical route of the personal credit assessment frameworks within the financial regulation realms.