Multivariate risk assessment via Poisson shelves
AssigneeVerint Americas Inc.
InventorsJoseph Wayne Dumoulin, Cynthia Freeman, James DelloStritto
Life of this patent
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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 system for performing risk assessment, comprising:
- a memory comprising executable instructions;
- a processor configured to execute the executable instructions and cause the system to:
- access 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;
- calculate a threshold value, ω, using the probabilistic model;
- partition the count data based on the threshold ω, using the probabilistic model;
- determine a number of surviving records from among the partitioned count data based on the threshold ω, using the probabilistic model;
- identify at least one shelf in the surviving records;
- and
- define outliers for risk assessment according to the at least one shelf.
The system of 4, wherein the executable instructions, when executed, cause the system to identifying an abrupt variation in generative parameters of a sequence of data.
A non-transitory computer readable medium comprising instructions that, when executed by a processor of a processing system, cause the processing system to perform a method, the method 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.
Dependencies are inferred from the claim text.