Risk assessment using Poisson Shelves
US11334832B2 · kind B2 · utility
17Cited by
4References
17Claims
0Family size
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Key dates
| Filing date | Oct 1, 2019 |
| Grant date | May 17, 2022 |
| Priority date | — |
| Expiry date | Jan 8, 2040 |
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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