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.

翻译：异常检测已成为金融服务业中日益关键的功能。该技术在金融犯罪检测、欺诈防范和网络安全等核心合规与风险环节中得到广泛应用。底层数据模式的动态特性，特别是在欺诈检测这类对抗性环境中，对机器学习模型构成了严峻挑战。通过使用最新数据模式重新训练模型来紧跟快速变化，需要在管理训练数据规模的同时平衡历史模式与当前模式。此外，在时间敏感且高吞吐量的部署系统中，模型重训练时间会引发问题——重训练周期直接影响模型及时响应持续攻击的能力。本研究提出一种基于时间知识蒸馏的标签增强方法（TKD），该方法利用旧模型的学习成果快速提升新模型性能，有效缩短模型重训练时间以增强敏捷性。实验结果表明，所提方法在缩短重训练时间的同时提升了模型性能。