Multivariate risk assessment via Poisson Shelves
AssigneeVerint Americas Inc.
InventorsJoseph Wayne Dumoulin, Cynthia Freeman, James DelloStritto
Life of this patent
Estimating the term…
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.
Claims19
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 for identifying outliers for risk assessment using count data “Records” having y values, the method comprising:
- computing a sample mean {circumflex over (λ)} of y values in Records;
- calculating threshold value ω={circumflex over (λ)}+n√{circumflex over (λ)}, from n=0 to nmax, where n is the number of standard deviation units in ω;
- for every ω, computing a number of surviving records according to |{r∈Records:
- r[y]>ω}|;
- identifying at least one shelf in surviving data by identifying an abrupt variation in generative parameters of a sequence of data;
- and
- defining outliers for further risk assessment according to {r∈Records r[y]>{circumflex over (λ)}+n*√{circumflex over (λ)}}.
A method of identifying outliers for risk assessment using count data “Records” having y values, comprising:
- computing a sample mean {circumflex over (λ)} of y values in Records;
- calculating threshold value ω={circumflex over (λ)}+n√{circumflex over (λ)}, from n=0 to nmax, where n is the number of standard deviation units in ω;
- for every ω, computing a number of surviving records according to |{r∈Records:
- r[y]>ω}|;
- identifying a shelf in surviving data by identifying an abrupt variation in generative parameters of a sequence of data;
- and
- defining outliers for further risk assessment according to {r∈Records r[y]>{circumflex over (λ)}+n*√{circumflex over (λ)}}.
A system for identifying outliers for risk assessment using count data “Records” having y values, comprising:
- a memory comprising executable instructions;
- a processor configured to execute the executable instructions and cause the system to:
- compute a sample mean {circumflex over (λ)} of y values in Records;
- calculating threshold value ω={circumflex over (λ)}+n√{circumflex over (λ)}, from n=0 to nmax, where n is the number of standard deviation units in ω;
- for every ω, computing a number of surviving records according to |{r∈Records:
- r[y]>ω}|;
- identify a shelf in surviving data by identifying an abrupt variation in generative parameters of a sequence of data;
- and
- define outliers for further risk assessment according to {r∈Records:
- r[y]>{circumflex over (λ)}+n*√{circumflex over (λ)}}.
Dependencies are inferred from the claim text.