Incremental Feature Learning for Fraud Data Stream

Sadreddin, Armin; Sadaoui, Samira

doi:10.5220/0010812700003116

Cited by 3 publications

(1 citation statement)

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“…Notably, in specific dynamic settings, these models have demonstrated commendable efficacy. For instance, (Sadreddin and Sadaoui 2022) leverages an innovative adaptive learning approach, melding transfer learning with incremental learning. (Anowar and Sadaoui 2021)…”

Section: Introductionmentioning

confidence: 99%

Pre-trained Online Contrastive Learning for Insurance Fraud Detection

Zhang,

Cheng,

Yang

et al. 2024

AAAI

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Medical insurance fraud has always been a crucial challenge in the field of healthcare industry. Existing fraud detection models mostly focus on offline learning scenes. However, fraud patterns are constantly evolving, making it difficult for models trained on past data to detect newly emerging fraud patterns, posing a severe challenge in medical fraud detection. Moreover, current incremental learning models are mostly designed to address catastrophic forgetting, but often exhibit suboptimal performance in fraud detection. To address this challenge, this paper proposes an innovative online learning method for medical insurance fraud detection, named POCL. This method combines contrastive learning pre-training with online updating strategies. In the pre-training stage, we leverage contrastive learning pre-training to learn on historical data, enabling deep feature learning and obtaining rich risk representations. In the online learning stage, we adopt a Temporal Memory Aware Synapses online updating strategy, allowing the model to perform incremental learning and optimization based on continuously emerging new data. This ensures timely adaptation to fraud patterns and reduces forgetting of past knowledge. Our model undergoes extensive experiments and evaluations on real-world insurance fraud datasets. The results demonstrate our model has significant advantages in accuracy compared to the state-of-the-art baseline methods, while also exhibiting lower running time and space consumption. Our sources are released at https://github.com/finint/POCL.

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Section: Introductionmentioning

confidence: 99%