This paper suggests using machine learning (ML), fuzzy logic and cryptographic techniques together to form a complete framework for predicting financial risks. The goal is to perform accurate, understandable and safe analysis that determines financial risk, for example, in credit scoring or determining if a loan will be granted. Machine learning is used to spot patterns in past and current financial data and fuzzy logic explains decisions and handles the risks that are uncertain. Cryptographic methods named AES, RSA and federated learning make sure security is maintained in the updates. Fuzzy-based access control is supported by the framework which makes it easy to change data access based on people’s roles and actions. Results of the experiments prove that the principal can predict accurately, score risks according to experts’ views and protect the data and accounts.