Abstract: The disclosure relates to methods and systems of generating geodata match classifications based on an ensemble of weak supervision-based match labels and geo-similarity models. The system may generate at least two match classifiers that are ensembled together. The match classifiers may include a location name classifier and a geo-similarity classifier. The location name classifier may generate a match classification that is based on a similarity of characters of the location names. The geo-similarity classifier may generate a match classification that is based on a proximity of the geolocations defined by the location names. The match classifications may be aggregated together to generate a geodata match classification.

Abstract: The disclosure relates to methods and systems of joining data structures based on a composite similarity score (CSS). For example, a computer system may use a plurality of similarity models to generate respective similarity scores. Each similarity score may be a metric that indicates a confidence that a first data value of a first data record is similar to a second data value of a second data record. The computer system may generate the CSS based on the plurality of similarity sub-scores. The CSS may indicate a confidence that the records being compared are similar. Thus, the CSS may be used to detect similar data records across different data structures. The disclosure also relates to a string similarity model that detects similarity among strings without respect to an order of words in each string and in a way that tolerates errors or omissions in one or both strings.

Abstract: In one embodiment, the present disclosure includes a mobile computing device. The mobile computing device includes a communication interface, one or more sensors, a memory, and an electronic processor. The electronic processor is configured to detect a remuneration trigger event, retrieve sensor data from a sensor data repository in response to detecting the remuneration trigger event, determine whether a user of the mobile computing device intended to perform a remuneration action by applying a user intention model to the sensor data, generate remuneration credentials in response to determining that the user of the mobile computing device intended to perform the remuneration action, and control the communication interface to transmit the remuneration credentials to the terminal device to complete the remuneration action.

Abstract: Systems and methods are provided for performing anomaly detection. One example method relates to transaction data including fraud scores output by a fraud score model generated by a machine learning system. The method includes determining, by a computing device, divergence values for multiple segments of payment accounts between baseline distributions of fraud scores and current distributions of fraud scores for the segments and detecting, by the computing device, at least one of the divergence values for at least one of the multiple segments as an anomaly. The method also includes categorizing, by the computing device, the detected anomaly into one of multiple categories, whereby the one of the multiple categories is indicative of a type of issue associated with the detected anomaly.

Abstract: Systems and methods are provided for evaluating aggregate merchant sets, which are often generated by a payment network. One exemplary method includes accessing, by a computing device, a monitor score and a volatility score for an aggregate merchant set representative of multiple merchants having at least one disparate parameter in a transaction data structure, fuzzy sets for the monitor score and the volatility score comprising linguistic values, and inference rules that use the linguistic values in logical operations. The method also generally includes determining degrees of membership of the monitor score and volatility score to the associated fuzzy sets and generating an evaluation index based on the inference rules and the degrees of membership to the fuzzy sets, thereby providing an indication of a propriety of the aggregation of said multiple merchants.

Abstract: Systems and methods are provided for scoring support messages from users indicative of the likelihood of escalation of the messages, upon which the messages may be prioritized. One exemplary method includes receiving, from a user, a support message related to a payment service provider and generating an escalation score for the support message based on a temporal factor associated with a duration associated with the support message, a source factor for the support message, and a text content factor of the support message. The exemplary method further includes identifying a likelihood of escalation of the support message based on the escalation score, whereby a support representative assigned to the support message is able to prioritize the support message over at least one other support message based on the likelihood of escalation for the support message.

Abstract: A capacity analysis computing device for determining a restaurant capacity level is provided. The capacity analysis computing device is configured to store historical transaction data for a restaurant for a period of time, and analyze the historical transaction data to identify a maximum restaurant capacity for the restaurant over the period of time. The capacity analysis computing device is also configured to receive, from a user computing, a selected time interval for which a current capacity level is to be determined. The capacity analysis computing device is further configured to identify a similar historical time interval to the selected time interval, and determine a historical capacity level for the restaurant during the similar historical time interval. The capacity analysis computing device is further configured to assign the historical capacity level as the current capacity level for the restaurant, and display the current capacity level on the user computing device.

Abstract: A system and computer-implemented method for analyzing chip card transactions to identify defective chip cards and/or defective chip readers in need of replacement. Constraints are established to define a subset of card transactions. From a full set of card transactions the subset is identified consisting of each card transaction falling within the constraints and occurring at a merchant having a chip reader. From this subset the unique chip readers are identified, and for each unique chip reader a percentage of fallback transactions is calculated. The percentage of fallback transactions is compared to a maximum value, and if the percentage of fallback transactions exceeds the maximum value, the chip reader is identified as defective. Each defective chip reader is reported to the merchant, along with at least a recommendation to replace the defective chip reader. A similar process may be used to identify defective chip cards.

Abstract: Systems and methods are provided for processing messages from users, via interactive interfaces, requesting support relating to features of applications available to the users. In connection therewith, the systems and methods normalize and score the user messages, and use the scores, in combination with scores for historical messages or scores for crowdsourced messages, to identify appropriate response messages for transmittal to the users. In various aspects, the crowdsourced solutions can be voted by both users and domain experts, and also verified by the domain experts. If a solution is verified it will be flagged and it can be included into a reference solution database as a standard response. However, when the scores assigned to the messages do not produce suitable results, a request for additional information relating to the issue described by the user is transmitted via the interactive interface.

Abstract: A system and computer-implemented method for analyzing chip card transactions to identify defective chip cards and/or defective chip readers in need of replacement. Constraints are established to define a subset of card transactions. From a full set of card transactions the subset is identified consisting of each card transaction falling within the constraints and occurring at a merchant having a chip reader. From this subset the unique chip cards are identified, and for each unique chip card a percentage of fallback transactions is calculated. The percentage of fallback transactions is compared to a maximum value, and if the percentage of fallback transactions exceeds the maximum value, the chip card is identified as defective. Each defective chip card is reported to the card issuer, along with at least a recommendation to replace the defective chip card. A similar process may be used to identify defective chip readers of particular merchants.

Abstract: Systems and methods are provided for verifying and/or confirming merchant data for multiple different transactions. One method generally includes accessing merchant data for a merchant where the merchant data includes a master merchant identifier for the merchant, and querying, by a computing device, multiple users as to which of a list of merchant data entries, for multiple different transactions and including different merchant identifiers, corresponds to the master merchant identifier for the merchant. The users are associated with a location within a predefined distance of the confirmed merchant and/or have performed one or more purchase transactions at the merchant. The method also generally includes receiving, at the computing device, a response from at least one of the users, in which the at least one of the users selects a data entry from the list, and determining whether to proceed in querying additional users.

Abstract: Systems and methods are provided for performing anomaly detection. One exemplary method relates to transaction data including fraud scores output by a fraud score model generated by a machine learning system. The method includes accessing fraud scores for a segment of payment accounts for a target interval and for a series of similar intervals, generating a baseline distribution and a current distribution based on the fraud scores. A divergence value is then determined based on the baseline distribution and the current distribution. An activeness of the segment of payment accounts is also determined, and the operations are repeated for one or more other segments of payment accounts. The method further includes clustering the multiple divergence pairs and designated one or more of the multiple divergence pairs as abnormal based on the clustered divergence pairs.

Abstract: Systems and methods are provided for use in performing data quality checks on input variables to machine learning systems. One exemplary method includes calculating a first moment associated with a long term variable (LTV), based on the value of the LTV and historical values of the LTV over a defined interval; and calculating a second moment associated with the LTV, based on the value of the LTV and the historical values of the LTV over the defined interval. The first moment and the second moment provide a moment pair. An isolation forest analysis is performed based on the moment pairs. And, a flag is generated for the LTV, when a check value of the LTV is different than the value of the LTV, and/or when the isolation forest analysis indicates the calculated moment pair is an anomaly.

Abstract: Systems and methods are provided for evaluating aggregate merchant sets, which are often generated by a payment network. One exemplary method includes accessing, by a computing device, a monitor score and a volatility score for an aggregate merchant set representative of multiple merchants having at least one disparate parameter in a transaction data structure, fuzzy sets for the monitor score and the volatility score comprising linguistic values, and inference rules that use the linguistic values in logical operations. The method also generally includes determining degrees of membership of the monitor score and volatility score to the associated fuzzy sets and generating an evaluation index based on the inference rules and the degrees of membership to the fuzzy sets, thereby providing an indication of a propriety of the aggregation of said multiple merchants.

Abstract: Systems and methods are provided for evaluating aggregate merchant sets, which are often generated by a payment network. One exemplary method includes accessing, by a computing device, a monitor score and a volatility score for an aggregate merchant set representative of multiple merchants having at least one disparate parameter in a transaction data structure, fuzzy sets for the monitor score and the volatility score comprising linguistic values, and inference rules that use the linguistic values in logical operations. The method also generally includes determining degrees of membership of the monitor score and volatility score to the associated fuzzy sets and generating an evaluation index based on the inference rules and the degrees of membership to the fuzzy sets, thereby providing an indication of a propriety of the aggregation of said multiple merchants.

Abstract: Systems and methods are provided for verifying and/or confirming merchant data for multiple different transactions. One method generally includes accessing merchant data for a merchant where the merchant data includes a master merchant identifier for the merchant, and querying, by a computing device, multiple users as to which of a list of merchant data entries, for multiple different transactions and including different merchant identifiers, corresponds to the master merchant identifier for the merchant. The users are associated with a location within a predefined distance of the confirmed merchant and/or have performed one or more purchase transactions at the merchant. The method also generally includes receiving, at the computing device, a response from at least one of the users, in which the at least one of the users selects a data entry from the list, and determining whether to proceed in querying additional users.

Abstract: A system and computer-implemented method for analyzing chip card transactions to identify defective chip cards and/or defective chip readers in need of replacement. Constraints are established to define a subset of card transactions. From a full set of card transactions the subset is identified consisting of each card transaction falling within the constraints and occurring at a merchant having a chip reader. From this subset the unique chip readers are identified, and for each unique chip reader a percentage of fallback transactions is calculated. The percentage of fallback transactions is compared to a maximum value, and if the percentage of fallback transactions exceeds the maximum value, the chip reader is identified as defective. Each defective chip reader is reported to the merchant, along with at least a recommendation to replace the defective chip reader. A similar process may be used to identify defective chip cards.

Abstract: A system and computer-implemented method for analyzing chip card transactions to identify defective chip cards and/or defective chip readers in need of replacement. Constraints are established to define a subset of card transactions. From a full set of card transactions the subset is identified consisting of each card transaction falling within the constraints and occurring at a merchant having a chip reader. From this subset the unique chip cards are identified, and for each unique chip card a percentage of fallback transactions is calculated. The percentage of fallback transactions is compared to a maximum value, and if the percentage of fallback transactions exceeds the maximum value, the chip card is identified as defective. Each defective chip card is reported to the card issuer, along with at least a recommendation to replace the defective chip card. A similar process may be used to identify defective chip readers of particular merchants.

Abstract: Systems and methods are provided for verifying and/or confirming merchant data for multiple different transactions. One method generally includes accessing merchant data for a merchant where the merchant data includes a master merchant identifier for the merchant, and querying, by a computing device, multiple users as to which of a list of merchant data entries, for multiple different transactions and including different merchant identifiers, corresponds to the master merchant identifier for the merchant. The users are associated with a location within a predefined distance of the confirmed merchant and/or have performed one or more purchase transactions at the merchant. The method also generally includes receiving, at the computing device, a response from at least one of the users, in which the at least one of the users selects a data entry from the list, and determining whether to proceed in querying additional users.

Abstract: Systems and methods are provided for scoring support messages from users indicative of the likelihood of escalation of the messages, upon which the messages may be prioritized. One exemplary method includes receiving, from a user, a support message related to a payment service provider and generating an escalation score for the support message based on a temporal factor associated with a duration associated with the support message, a source factor for the support message, and a text content factor of the support message. The exemplary method further includes identifying a likelihood of escalation of the support message based on the escalation score, whereby a support representative assigned to the support message is able to prioritize the support message over at least one other support message based on the likelihood of escalation for the support message.