Detecting anomalies has become an increasingly critical function in the
financial service industry. Anomaly detection is frequently used in key
compliance and risk functions such as financial crime detection fraud and
cybersecurity. The dynamic nature of the underlying data patterns especially in
adversarial environments like fraud detection poses serious challenges to the
machine learning models. Keeping up with the rapid changes by retraining the
models with the latest data patterns introduces pressures in balancing the
historical and current patterns while managing the training data size.
Furthermore the model retraining times raise problems in time-sensitive and
high-volume deployment systems where the retraining period directly impacts the
models ability to respond to ongoing attacks in a timely manner. In this study
we propose a temporal knowledge distillation-based label augmentation approach
(TKD) which utilizes the learning from older models to rapidly boost the latest
model and effectively reduces the model retraining times to achieve improved
agility. Experimental results show that the proposed approach provides
advantages in retraining times while improving the model performance.

在金融服务行业中，检测异常已成为一项越来越关键的功能。该研究提出了一种基于时间知识蒸馏的标签增强方法，利用旧模型的学习来快速提升最新模型的能力，从而有效减少模型重新训练的时间并改善其性能。