Moving banking systems and data to the cloud requires high quality automated testing. Securely transforming data to enable bank modernization requires accurate data. Regulated banks are migrating to the cloud at an accelerated pace. Optimal system performance, transparency into system behavior, and organizational risk are all critical considerations. Manually verifying and validating large datasets has always been a time-consuming and error prone activity, but automated data validation is a viable alternative. Streamlined automated pipelines can process high volumes of complex data while reducing the likelihood of errors and maintaining data consistency. In this paper, we propose a theoretical approach to automate testing banking data in cloud migration process. We implement an automated tool for testing banking data at three levels pre-migration, migration and post-migration. The model automates the process of testing and validation of data using AI-powered test generation technique to ensure that all data transformations are 100% complete and accurate. By incorporating automated testing in all phases of development lifecycle and cloud migration, we ensure data accuracy, integrity and consistency and enforce financial business rules and regulations. This model provides an integrated approach to migrate core banking systems to cloud environments in a secure manner.