Abstract: Disclosed is a system for scoring customers of a financial institution based on financial data. The system includes a central database that stores a plurality of modules, a central server that processes the plurality of modules and a display unit that displays the processed plurality of modules. The plurality of modules includes a criteria configuration module, a data module, and a computation module. The criteria configuration module includes a metric module to receive the input parameters required to evaluate the score, and a measurement module for defining transformation criteria to be applied on the data corresponding to the input parameters. The computation module includes a metric evaluation module to compute and applies the transformation criteria to the values of the input parameters, and a scoring module coupled to the metric evaluation module to automatically compute and display the score of the customers based on the values retrieved from the metric evaluation module.

Abstract: A unique user interface for improving machine learning algorithms is described herein. The user interface comprises an icon with multiple visual indicators displaying the machine learning confidence score. When a mouse hovers over the icon, a set of icons are displayed to accept the teaching user's input. In addition, the words that drove the machine learning confidence score are highlighted with formatting so that the teaching user can understand what drove the machine learning confidence score.

Abstract: A novel distributed method for machine learning is described, where the algorithm operates on a plurality of data silos, such that the privacy of the data in each silo is maintained. In some embodiments, the attributes of the data and the features themselves are kept private within the data silos. The method includes a distributed learning algorithm whereby a plurality of data spaces are co-populated with artificial, evenly distributed data, and then the data spaces are carved into smaller portions whereupon the number of real and artificial data points are compared. Through an iterative process, clusters having less than evenly distributed real data are discarded. A plurality of final quality control measurements are used to merge clusters that are too similar to be meaningful. These distributed quality control measures are then combined from each of the data silos to derive an overall quality control metric.

Abstract: Disclosed is a system for scoring customers of a financial institution based on financial data. The system includes a central database that stores a plurality of modules, a central server that processes the plurality of modules and a display unit that displays the processed plurality of modules. The plurality of modules includes a criteria configuration module, a data module, and a computation module. The criteria configuration module includes a metric module to receive the input parameters required to evaluate the score, and a measurement module for defining transformation criteria to be applied on the data corresponding to the input parameters. The computation module includes a metric evaluation module to compute and applies the transformation criteria to the values of the input parameters, and a scoring module coupled to the metric evaluation module to automatically compute and display the score of the customers based on the values retrieved from the metric evaluation module.

Abstract: A method for creating machine learning model performance alerts showing the drifting of functions is described herein. The method starts by creating the initial machine learning model using a training data set. This initial machine learning model is then used in production, and the model is updated to account for the production data. To assure the quality of the updated machine learning model, test data results from the initial machine learning model is compared to the results from the updated machine learning model. Each feature is checked to see if the difference is within a p-value and whether the confidence intervals overlap. If not, an alert is generated to take action on the model.

Abstract: A method and apparatus for detecting suspicious activities surrounding the management of controlled substances in a medical facility is described herein, where the activities that may indicate the diversion of controlled drugs are flagged for further review. The activities are detected by reviewing application layer network packets, related to controlled substances, on the medical facility network, identifying the physical location of the origin of the packet, and processing these packets with a rules engine and machine learning generated rules to make a determination if the circumstances surrounding the packets indicate the diversion of controlled substances.

Abstract: A unique user interface for improving machine learning algorithms is described herein. The user interface comprises an icon with multiple visual indicators displaying the machine learning confidence score. When a mouse hovers over the icon, a set of icons are displayed to accept the teaching user's input. In addition, the words that drove the machine learning confidence score are highlighted with formatting so that the teaching user can understand what drove the machine learning confidence score.

Abstract: A two-step algorithm for conducting near real time fuzzy searches of a target on one or more large data sets is described, where the address and the geolocation are included in the match criteria. This algorithm includes the simplification of the data by removing grammatical constructs to bring the target search term (and the stored database) to their base elements and then perform a Levenshtein comparison to create a subset of the data set that may be a match. Location is determined by a geohash comparison of the latitude and longitude and a Levenshtein comparison of the address text. Then performing a scoring algorithm while comparing the target to the subset of the data set to identify any matches.

Abstract: A two-step algorithm for conducting near real-time fuzzy searches of a target on one or more large data sets is described. This algorithm includes the simplification of the data by removing grammatical constructs to bring the target search term (and the stored database) to their base elements and then perform a Levenstein comparison to create a subset of the data set that may be a match. Then performing a scoring algorithm while comparing the target to the subset of the data set to identify any matches.

Abstract: Disclosed is a system for facilitating financial planning of an objective. The system includes a storage unit for storing computer program instructions, a display unit for displaying processed computer program instructions, a processing unit is coupled to the storage unit and the display unit for processing the computer program instructions. The computer program instructions includes a data input computer program instruction, a data category display computer program instruction, a threshold computer program instruction, a slide computer program instruction and a heat map computer program instruction. The heat map computer program instruction with a color computer program instruction to display the degree of difference with the intensity of the color. A red color to display the degree of difference for a negative value and a green color to display the degree of difference for a positive value.

Abstract: A two-step algorithm for conducting near real time fuzzy searches of a target on one or more large data sets is described, where the address and the geolocation are included in the match criteria. This algorithm includes the simplification of the data by removing grammatical constructs to bring the target search term (and the stored database) to their base elements and then perform a Levenshtein comparison to create a subset of the data set that may be a match. Location is determined by a geohash comparison of the latitude and longitude and a Levenshtein comparison of the address text. Then performing a scoring algorithm while comparing the target to the subset of the data set to identify any matches.

Abstract: A two-step algorithm for conducting near real-time fuzzy searches of a target on one or more large data sets is described. This algorithm includes the simplification of the data by removing grammatical constructs to bring the target search term (and the stored database) to their base elements and then perform a Levenstein comparison to create a subset of the data set that may be a match. Then performing a scoring algorithm while comparing the target to the subset of the data set to identify any matches.

Abstract: A computer-implemented method for providing tenant aware, variable length, deduplication of data stored on a non-transitory computer readable storage medium. The method is performed at least in part by circuitry and the data comprises a plurality of data items. Each of the plurality of data items is associated with a particular tenant of a group of tenants that store data on the storage medium.

Abstract: Disclosed are a system and a method for communicating with a financial institution over a communication network to manage payments of payee initiated by a sender. The system includes a database for storing a plurality of modules, a central server for monitoring and updating the plurality of modules, a processing unit that processes the plurality of modules. The plurality of modules includes a notification module, a payee module, a dashboard module, a refunds module, a payment process module, and a remittance summary module. The notification module allows the sender to send a payment summary to each payee. The payee module allows the payee to upload the payee's details, to select a mode of payment to receive the payment. The payment process module processes the payment as per the mode selected by the payee in the payee module, further processes the payment via cheque mode if the payee fails to update the mode of payment in a pre-defined duration.

Abstract: A computer-implemented method for matching user inputted text to stored text. The user inputted text is compared to each of the text strings stored in a database using a string similarity score determined using a Levenshtein distance algorithm, the n-gram or trigram methods, the Jaro-Winkler algorithm, the Cosine similarity algorithm, the Hamming distance algorithm, the Damerau-Levenshtein distance algorithm, or similar. For each comparison, the string similarity score is analyzed to determine exact matches, non-matches, and probable matches. Probable matches are further analyzed using a keyboard distance algorithm to differentiate between matches and non-matches.

Abstract: A method and apparatus for detecting suspicious activities surrounding the management of controlled substances in a medical facility is described herein, where the activities that may indicate the diversion of controlled drugs are flagged for further review. The activities are detected by reviewing application layer network packets, related to controlled substances, on the medical facility network, identifying the physical location of the origin of the packet, and processing these packets with a rules engine and machine learning generated rules to make a determination if the circumstances surrounding the packets indicate the diversion of controlled substances.

Abstract: A two-step algorithm for conducting near real-time fuzzy searches of a target on one or more large data sets is described. This algorithm includes the simplification of the data by removing grammatical constructs to bring the target search term (and the stored database) to their base elements and then perform a Levenstein comparison to create a subset of the data set that may be a match. Then performing a scoring algorithm while comparing the target to the subset of the data set to identify any matches.