The current work contributes to the development of stochastic models for credit spreads, frameworks for transitioning between risk-neutral (RN) and real-world (RW) measures, AI-driven calibration techniques, and a methodology for data quality measurement. These advancements aim to improve the precision and practical use of financial models. The methodology, based on Girsanov`s theorem, is designed to incorporate real-world dynamics, making it crucial for risk management. The framework is built to be applicable to a variety of models, including models with non-additive noise, such as the CIR$++$ model. We validate the framework`s versatility through case studies involving forecasts and stress tests.