Generational Learning for Robust, Agile and Compliant Models in Dynamic Environments Through Temporal Knowledge Distillation

Dynamic data patterns and adversarial environments present significant challenges for machine learning models in financial service applications, such as fraud detection and financial crime prevention. These systems must continuously adapt to evolving behaviors, requiring frequent model retraining with up-to-date data. However, this introduces a trade-off between incorporating recent trends and preserving valuable historical knowledge, all while managing the scalability of training datasets. Moreover, in high-throughput, time-sensitive deployment scenarios, the latency introduced by retraining can critically impair a model’s ability to respond promptly to emerging threats. Ensuring model agility under strict latency and governance constraints thus becomes a key challenge. In this study, we propose a temporal knowledge distillation (TKD)-based label augmentation framework that leverages insights from prior model generations to accelerate the training of new models. By transferring distilled knowledge from older models to augment the labeling of current training data, our approach significantly reduces retraining time while enhancing model performance. Experimental evaluations demonstrate that the proposed method improves retraining efficiency while consistently enhancing predictive accuracy, training time, model agility and robustness under dynamic, real-world financial conditions.