# Label Augmentation via Time-based Knowledge Distillation for Financial Anomaly Detection

arXiv: https://arxiv.org/abs/2101.01689 · HTML: https://mlsanity.com/paper/2101.01689/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Detection and dense prediction distillation, one of 12 directions in Knowledge distillation.
- Builds on 8 of its 17 references that are in the map.
- First posted on arXiv 5 Jan 2021.

## Placed in

- Knowledge distillation / [Detection and dense prediction distillation](https://mlsanity.com/distill/detection-and-dense-prediction-distillation/)

## Builds on (most cited first, 8 of 8)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [FitNets: Hints for Thin Deep Nets](https://mlsanity.com/paper/1412.6550/) (2014)
- [Born Again Neural Networks](https://mlsanity.com/paper/1805.04770/) (2018)
- [Do Deep Nets Really Need to be Deep?](https://mlsanity.com/paper/1312.6184/) (2013)
- [Label Refinery: Improving ImageNet Classification through Label Progression](https://mlsanity.com/paper/1805.02641/) (2018)
- [Do Deep Convolutional Nets Really Need to be Deep and Convolutional?](https://mlsanity.com/paper/1603.05691/) (2016)
- [Computer-Assisted Fraud Detection, From Active Learning to Reward Maximization](https://mlsanity.com/paper/1811.08212/) (2018)

## Built on by (most cited first, 1 of 1)

- [A Closer Look at AUROC and AUPRC under Class Imbalance](https://mlsanity.com/paper/2401.06091/) (2024)

## Abstract (arXiv)

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.