Risk assessment using Poisson Shelves
AssigneeVerint Americas Inc.
InventorsJoseph Wayne Dumoulin, Cynthia Freeman, James DelloStritto
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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.
Claims17
A computer program product comprising computer executable code embodied in a non-transitory computer readable medium that, when executing on one more computing devices performs a method of performing risk assessment using count data, comprising:
- accessing a probabilistic model in a library of known probabilistic models;
- modeling said count data using a Poisson distribution using the probabilistic model running on a processor;
- calculating a threshold value, ω, using the probabilistic model;
- partitioning the count data based on the threshold ω, using the probabilistic model;
- determining a number of surviving records from among the partitioned count data based on the threshold ω, using the probabilistic model;
- identifying at least one shelf in the surviving records;
- and
- defining outliers for risk assessment according to the at least one shelf.
A method of performing risk assessment using count data using a probabilistic model running on computing device, comprising:
- accessing a probabilistic model in a library of known probabilistic models;
- modeling said count data using a Poisson distribution using the probabilistic model;
- calculating a threshold value, ω, using the probabilistic model;
- partitioning the count data based on the threshold ω, using the probabilistic model;
- determining a number of surviving records from among the portioned count data based on the threshold ω, using the probabilistic model;
- identifying at least one shelf in the surviving records;
- and
- defining outliers for risk assessment according to the at least one shelf.
Dependencies are inferred from the claim text.