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.

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.

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.

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.

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.

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.

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.

Abstract: Experiential parsing (EP) is a technique for natural language parsing that falls into the category of dependency parsing. EP supports applications that derive meaning from chat language. An experiential language model parses chat data, and uses documented experiences with language without using automatic natural language processing (NLP) methods. A descriptive grammar is built at word level rather than a prescriptive grammar at phrase level. The experiential model is designed to understand that word “A” associates with word “B” by function “C”. The experiential model understands the relationship between words, independent of whether or not the overall phrase structure is grammatical. A high accuracy of producing the syntactic roles (such as main verb, direct object, etc.) is attained even when confronted with a variety of agrammatical inputs.

Abstract: Experiential parsing (EP) is a technique for natural language parsing that falls into the category of dependency parsing. EP supports applications that derive meaning from chat language. An experiential language model parses chat data, and uses documented experiences with language without using automatic natural language processing (NLP) methods. A descriptive grammar is built at word level rather than a prescriptive grammar at phrase level. The experiential model is designed to understand that word “A” associates with word “B” by function “C”. The experiential model understands the relationship between words, independent of whether or not the overall phrase structure is grammatical. A high accuracy of producing the syntactic roles (such as main verb, direct object, etc.) is attained even when confronted with a variety of agrammatical inputs.

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.

Abstract: Experiential parsing (EP) is a technique for natural language parsing that falls into the category of dependency parsing. EP supports applications that derive meaning from chat language. An experiential language model parses chat data, and uses documented experiences with language without using automatic natural language processing (NLP) methods. A descriptive grammar is built at word level rather than a prescriptive grammar at phrase level. The experiential model is designed to understand that word “A” associates with word “B” by function “C”. The experiential model understands the relationship between words, independent of whether or not the overall phrase structure is grammatical. A high accuracy of producing the syntactic roles (such as main verb, direct object, etc.) is attained even when confronted with a variety of agrammatical inputs.