Abstract: The disclosed fraud risk scoring tool provides data-driven identification of potential fraud cases. A fraud risk score is determined based at least on a weighted sum of multiple metrics, where weights are determined based on precision or inverse variance and suspension propensity importance, which is indicative of variable importance. An exemplary method includes: receiving and aggregating transaction metrics across customer IDs to produce an aggregated transaction metric data set; determining, for each aggregated transaction metric in the aggregated transaction metric data set, a precision value, a suspension propensity importance, and a scoring weight based at least on one of the precision value and the suspension propensity importance; determining, for each customer ID within the plurality of customer IDs, a risk score based at least on the scoring weights applied to corresponding transaction metrics and a risk priority; and reporting at least one customer ID associated with a selected risk priority.

Abstract: The disclosed fraud risk scoring tool provides data-driven identification of potential fraud cases. A fraud risk score is determined based at least on a weighted sum of multiple metrics, where weights are determined based on precision or inverse variance and suspension propensity importance, which is indicative of variable importance. An exemplary method includes: receiving and aggregating transaction metrics across customer IDs to produce an aggregated transaction metric data set; determining, for each aggregated transaction metric in the aggregated transaction metric data set, a precision value, a suspension propensity importance, and a scoring weight based at least on one of the precision value and the suspension propensity importance; determining, for each customer ID within the plurality of customer IDs, a risk score based at least on the scoring weights applied to corresponding transaction metrics and a risk priority; and reporting at least one customer ID associated with a selected risk priority.

Abstract: In some embodiments, apparatuses and methods are provided herein useful to analyzing and presenting data associated with customer feedback. In some embodiments, a system comprises a display device configured to present a GUI, a database configured to receive and store the customer feedback, and a control circuit including a feedback parsing module configured to retrieve a review for a product, break the review for the product into segments, a sentiment calculation module configured to assign tags associated with aspects of the product to the segments, calculate a sentiment score for each of the segments, and normalize the sentiment score for each of the segments, an aspect weighted rating module configured to calculate an aspect weighted rating for each of the aspects, wherein the aspect weighted rating for each of the aspects represents an importance of each of the aspects, and a presentation module configured to generate the GUI.