Credit risk prediction is a crucial component of modern financial analytics. With the widespread use of neural networks and cloud computing, the data owner can obtain the result of credit risk prediction through a neural network model deployed in the cloud. This approach enhances the efficiency and accuracy of credit risk assessment. However, without additional security measures, adversaries may gain access to private data and model parameters. This paper presents a verifiable and privacy-preserving credit prediction model designed to protect both the data owner’s data and the model parameters in credit risk prediction. The scheme employs a functional encryption mechanism to protect the data owner’s data and also ensures model parameters’ privacy by adding fake neurons to two non-colluding cloud servers. We employ the checksum mechanism to ensure the verifiability of the neural network computations. The experimental results demonstrate that the proposed scheme achieves a secure credit risk assessment accuracy of approximately 90% on the Australian Credit Approval DataSet.