Abstract: A system and method for generating ultimate reason codes for computer models is provided. The system for generating ultimate reason codes for computer models comprising a computer system for receiving a data set, and an ultimate reason code generation engine stored on the computer system which, when executed by the computer system, causes the computer system to train a base model with a plurality of reason codes, wherein each reason code includes one or more variables, each of which belongs to only one reason code, train a subsequent model using a subset of the plurality of reason codes, determine whether a high score exists in the base model, determine a scored difference if a high score exists in the base model, and designate a reason code having a largest drop of score as an ultimate reason code.

Abstract: Provided herein are charcoal grills capable of precisely adjusting a heating temperature, the grill having a furnace body, a grilling grate, and a charcoal box, wherein the grilling grate and the charcoal box are installed inside the furnace body, the charcoal box having a charcoal box body, a charcoal box cover, and a charcoal mesh, wherein the charcoal box cover is mounted onto the box body, the charcoal box cover is rotatably connected to the charcoal box body, and the charcoal mesh is fixedly installed inside the charcoal box body.

Abstract: Disclosed embodiments provide systems and methods related to collecting return items using an automated kiosk based on a real time risk decision. The automated kiosk captures return item information representing a return item and transmits the return item information and a request for return risk level relating to the return item to a server operable to execute a machine learning model trained on historical information to determine the risk level. The server determines the risk level based on the received return by using the machine learning model and transmits the determined risk level to the kiosk in real-time. Based on the determined risk level and a return amount associated with the return item, the server may also process a refund in real-time.

Abstract: A system and method for generating ultimate reason codes for computer models is provided. The system for generating ultimate reason codes for computer models comprising a computer system for receiving a data set, and an ultimate reason code generation engine stored on the computer system which, when executed by the computer system, causes the computer system to train a base model with a plurality of reason codes, wherein each reason code includes one or more variables, each of which belongs to only one reason code, train a subsequent model using a subset of the plurality of reason codes, determine whether a high score exists in the base model, determine a scored difference if a high score exists in the base model, and designate a reason code having a largest drop of score as an ultimate reason code.

Abstract: Methods and systems for dynamic IP categorization include receiving electronic requests to access an electronic server; logging a first set of requests occurring during a first period of time and originating from an IP address belonging to a set of IP addresses; assigning the set of IP addresses to a first category according to the first set of requests; logging a second set of requests occurring during a second period of time and originating from the first IP address or a second IP address belonging to the set of IP addresses; determining a second category according to the second set of requests; assigning the set of IP addresses to the second category when the first category and the second category differ; and providing a response to a requesting IP address based on the category associated with the set of IP addresses to which the requesting IP address belongs.

Abstract: Systems and method are provided for fraud detection and user account deduplication. One method includes receiving a request from a user to register a third user account; receiving user information associated with the third user account, wherein the user information comprises a second attribute; at a third time entry, modifying the first data store by: searching the plurality of first entries in the first data store; comparing the second attribute to the first attribute of each first entry; determining that at least one first entry comprises a first attribute that is identical to the second attribute; adding second and third entries, wherein the second entry comprises the first and third user accounts, the second attribute, and the first and third time entries; and the third entry comprises the second and third user accounts, the second attribute, and the second and third time entries.

Abstract: Systems and method are provided for tracking online communities. One method includes at a first time, sorting the plurality of users by: determining that a group of the plurality of users belongs to a community, wherein the community has a community identification of zero; labeling each user in the group with the community identification of zero; labeling each user in the group with an algorithm identification, wherein the algorithm identification is associated with the community; determining that that one user of the group is a core user; and increasing the community identification counter by one; repeat the sorting until each user of the plurality of users is labeled with a community identification and an algorithm identification; and generate a community dynamics analysis based on the sorting of the plurality of users.

Abstract: Systems and method are provided for fraud detection and user account deduplication. One method includes receiving a request from a user to register a third user account; receiving user information associated with the third user account, wherein the user information comprises a second attribute; at a third time entry, modifying the first data store by: searching the plurality of first entries in the first data store; comparing the second attribute to the first attribute of each first entry; determining that at least one first entry comprises a first attribute that is identical to the second attribute; adding second and third entries, wherein the second entry comprises the first and third user accounts, the second attribute, and the first and third time entries; and the third entry comprises the second and third user accounts, the second attribute, and the second and third time entries.

Abstract: A computer-implemented method and system for visualizing card transaction fraud analysis is presented. Transaction data and account data related to one or more payment card accounts is stored in a database. The transaction data includes a fraud score. A computer processor generates one or more of a plurality of visualizations of activity of at least one suspicious account from the one or more payment card accounts for display in a graphical user interface, each of the plurality of visualizations providing at least a graphical representation of the transaction data and which is selectable from a menu provided by the computer processor in the graphical user interface. The visualizations assist in case judgment of the one or more payment cards.

Abstract: Systems and method are provided for fraud detection and user account deduplication. One method includes receiving a request from a user to register a third user account; receiving user information associated with the third user account, wherein the user information comprises a second attribute; at a third time entry, modifying the first data store by: searching the plurality of first entries in the first data store; comparing the second attribute to the first attribute of each first entry; determining that at least one first entry comprises a first attribute that is identical to the second attribute; adding second and third entries, wherein the second entry comprises the first and third user accounts, the second attribute, and the first and third time entries; and the third entry comprises the second and third user accounts, the second attribute, and the second and third time entries.

Abstract: Methods and systems for dynamic IP categorization include receiving electronic requests to access an electronic server; logging a first set of requests occurring during a first period of time and originating from an IP address belonging to a set of IP addresses; assigning the set of IP addresses to a first category according to the first set of requests; logging a second set of requests occurring during a second period of time and originating from the first IP address or a second IP address belonging to the set of IP addresses; determining a second category according to the second set of requests; assigning the set of IP addresses to the second category when the first category and the second category differ; and providing a response to a requesting IP address based on the category associated with the set of IP addresses to which the requesting IP address belongs.

Abstract: Methods and systems for dynamic IP categorization include receiving electronic requests to access an electronic server; logging a first set of requests occurring during a first period of time and originating from an IP address belonging to a set of IP addresses; assigning the set of IP addresses to a first category according to the first set of requests; logging a second set of requests occurring during a second period of time and originating from the first IP address or a second IP address belonging to the set of IP addresses; determining a second category according to the second set of requests; assigning the set of IP addresses to the second category when the first category and the second category differ; and providing a response to a requesting IP address based on the category associated with the set of IP addresses to which the requesting IP address belongs.

Abstract: Systems and method are provided for tracking online communities. One method includes at a first time, sorting the plurality of users by: determining that a group of the plurality of users belongs to a community, wherein the community has a community identification of zero; labeling each user in the group with the community identification of zero; labeling each user in the group with an algorithm identification, wherein the algorithm identification is associated with the community; determining that that one user of the group is a core user; and increasing the community identification counter by one; repeat the sorting until each user of the plurality of users is labeled with a community identification and an algorithm identification; and generate a community dynamics analysis based on the sorting of the plurality of users.

Abstract: Systems and method are provided for fraud detection and user account deduplication. One method includes receiving a request from a user to register a third user account; receiving user information associated with the third user account, wherein the user information comprises a second attribute; at a third time entry, modifying the first data store by: searching the plurality of first entries in the first data store; comparing the second attribute to the first attribute of each first entry; determining that at least one first entry comprises a first attribute that is identical to the second attribute; adding second and third entries, wherein the second entry comprises the first and third user accounts, the second attribute, and the first and third time entries; and the third entry comprises the second and third user accounts, the second attribute, and the second and third time entries.

Abstract: Disclosed embodiments provide systems and methods related to collecting return items using an automated kiosk based on a real time risk decision. The automated kiosk captures return item information representing a return item and transmits the return item information and a request for return risk level relating to the return item to a server operable to execute a machine learning model trained on historical information to determine the risk level. The server determines the risk level based on the received return by using the machine learning model and transmits the determined risk level to the kiosk in real-time. Based on the determined risk level and a return amount associated with the return item, the server may also process a refund in real-time.

Abstract: Disclosed embodiments provide systems and methods related to collecting return items using an automated kiosk based on a real time risk decision. The automated kiosk captures return item information representing a return item and transmits the return item information and a request for return risk level relating to the return item to a server operable to execute a machine learning model trained on historical information to determine the risk level. The server determines the risk level based on the received return by using the machine learning model and transmits the determined risk level to the kiosk in real-time. Based on the determined risk level and a return amount associated with the return item, the server may also process a refund in real-time.

Abstract: A system for generating alerts including processors and storage devices. The instructions configure the one or more processors to perform operations, which include receiving an event from a data stream, extracting keys from the event, associating the event with at least one account based on the extracted keys, identifying a state variable associated with the at least one account, updating the state variable by accumulating the event in the state variable, registering a time stamp for the event in the state variable, and retiring expired events from the state variable, The operations may also include determining whether the state variable is above a threshold level and generating an alert for the account when the state variable is above the threshold level.

Abstract: Disclosed embodiments provide systems and methods related to collecting return items using an automated kiosk based on a real time risk decision. The automated kiosk captures return item information representing a return item and transmits the return item information and a request for return risk level relating to the return item to a server operable to execute a machine learning model trained on historical information to determine the risk level. The server determines the risk level based on the received return by using the machine learning model and transmits the determined risk level to the kiosk in real-time. Based on the determined risk level and a return amount associated with the return item, the server may also process a refund in real-time.

Abstract: A system for generating alerts including processors and storage devices. The instructions configure the one or more processors to perform operations, which include receiving an event from a data stream, extracting keys from the event, associating the event with at least one account based on the extracted keys, identifying a state variable associated with the at least one account, updating the state variable by accumulating the event in the state variable, registering a time stamp for the event in the state variable, and retiring expired events from the state variable. The operations may also include determining whether the state variable is above a threshold level and generating an alert for the account when the state variable is above the threshold level.