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A Framework for Sustaining Model Validity in Dynamic Financial Systems 1. Introduction Machine Learning (ML) models have become integral to modern financial systems, supporting critical decision-making processes such as credit scoring, fraud detection, risk assessment, and algorithmic trading. These models are typically trained on historical datasets and deployed with the expectation that learned relationships will generalize to future data. Formally, this assumption can be expressed as: P t r a i n ( X , Y ) = P f u t u r e ( X , Y ) X  represents input features Y  represents the target variable This assumption implies that the joint probability distribution governing the training data remains stable over time. However, financial systems operate in inherently non-stationary environments . Economic conditions fluctuate, customer behavior evolves, fraud patterns adapt, and regulatory frameworks change. Consequently, the underlying data-generating process is subject t...