Anomaly Detection Model for Imbalanced Datasets
| Autor: | Houssou, R��gis, Robert-Nicoud, Stephan |
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| Rok vydání: | 2020 |
| Předmět: | |
| DOI: | 10.48550/arxiv.2011.12390 |
| Popis: | This paper proposes a method to detect bank frauds using a mixed approach combining a stochastic intensity model with the probability of fraud observed on transactions. It is a dynamic unsupervised approach which is able to predict financial frauds. The fraud prediction probability on the financial transaction is derived as a function of the dynamic intensities. In this context, the Kalman filter method is proposed to estimate the dynamic intensities. The application of our methodology to financial datasets shows a better predictive power in higher imbalanced data compared to other intensity-based models. 11 pages, 5 figures |
| Databáze: | OpenAIRE |
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