Multivariate risk assessment via poisson shelves
US11928634B2 · kind B2 · utility
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15Claims
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Key dates
| Filing date | Sep 7, 2022 |
| Grant date | Mar 12, 2024 |
| Priority date | — |
| Expiry date | Sep 7, 2042 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04M2017/14
- WIPO fieldIT methods for management
- WIPO sectorElectrical engineering
Abstract
Detecting fraudulent activity can be a complex, manual process. In this paper, we adapt statistical properties of count data in a novel algorithm to uncover records exhibiting high risk for fraud. Our method identifies shelves, partitioning data under the counts using a Student's t-distribution. We apply this methodology on a univariate dataset including cumulative results from phone calls to a customer service center. Additionally, we extend this technique to multivariate data, illustrating that the same method is applicable to both univariate and multivariate data.
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