Jun 14, 2021 · In this paper, we proposed a fraud detection method with enhanced explainability in the MIL framework, which incorporates the AP clustering method.
The significance of the research work is that financial institutions can use this method to efficiently identify fraudulent behaviors and easily give reasons ...
Experimental results show that the proposed explainable classification method incorporates the AP clustering method in the self-training LSTM model achieves ...
Abstract: Fraud detection technology is an important method to ensure financial security. It is necessary to develop explainable fraud detection methods to ...
Jun 14, 2019 · A simple but quite efficient method may be to perform a semi-supervised learning, for labeling your data and also developing a fraud detection model in this ...
Missing: Explainable | Show results with:Explainable
Apr 30, 2024 · Discover powerful machine learning methods for detecting anomalies in time series data. Enhance accuracy and mitigate risks effectively.
In this paper, we present Exathlon, the first comprehensive public benchmark for explainable anomaly detection over high-dimensional time se- ries data.
Jul 15, 2024 · FSFD-TLKG is an explainable financial statement data fraud detection method based on a two-layer knowledge graph and a fraudulent pattern mining strategy.
Missing: Labeled Series
Existing fraud behavior detection approaches typically model the time-series data with a vanilla Recurrent Neural Network (RNN) or combine the whole sequence as ...
Dec 1, 2023 · ABSTRACT. There is a growing demand for explainable, transparent, and data- driven models within the domain of fraud detection.