Temporal Knowledge Distillation for Time-Sensitive Financial Services
  Applications
Hongda Shen Eren Kurshan
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.