  Detecting anomalies has become increasingly critical to the financial service
industry. Anomalous events are often indicative of illegal activities such as
fraud, identity theft, network intrusion, account takeover, and money
laundering. Financial anomaly detection use cases face serious challenges due
to the dynamic nature of the underlying patterns especially in adversarial
environments such as constantly changing fraud tactics. While retraining the
models with the new patterns is absolutely essential; keeping up with the rapid
changes introduces other challenges as it moves the model away from older
patterns or continuously grows the size of the training data. The resulting
data growth is hard to manage and it reduces the agility of the models'
response to the latest attacks. Due to the data size limitations and the need
to track the latest patterns, older time periods are often dropped in practice,
which in turn, causes vulnerabilities. In this study, we propose a label
augmentation approach to utilize the learning from older models to boost the
latest. Experimental results show that the proposed approach provides a
significant reduction in training time, while providing potential performance
improvement.
