Abstract: A method of preventing healthcare fraud-waste-abuse uses artificial intelligence machines to limit financial losses. Healthcare payment request claims are analyzed by predictive models and their behavioral details are compared to running profiles unique to each healthcare provider submitting the claims. A decision results that the instant healthcare payment request claim is or is not fraudulent-wasteful-abusive. If it is, a second analysis of a group behavioral in which the healthcare provider is clustered using unsupervised learning algorithms and compared to a running profile unique to each group of healthcare providers submitting the claims. An overriding decision results if the instant healthcare payment request claim is or is not fraudulent-wasteful-abusive according to group behavior.

Abstract: A method of reducing financial fraud by operating artificial intelligence machines organized into parallel sets of predictive models with each set specially trained with supervised and unsupervised training data filtered for a particular financial channel. Each set integrates several artificial intelligence classifiers like neural networks, case based reasoning, decision trees, genetic algorithms, fuzzy logic, business rules and constraints, smart agents and associated real-time profiling, recursive profiles, and long-term profiles. Suspicious and abnormal activities in any channel communicate across predictive models for all the financial channels through real-time memory storage updates to the smart agent profiles they all share.

Abstract: A merchant data breach process comprises processing daily payment transaction data with a risk and compliance platform to obtain a fraud score for each constituent transaction. Constituent transactions with high risk fraud scores are sorted into a table according to the transaction date, cardholder, and merchant. The table data is scored according to suspected card visits, highly probable visits, and all card visits. The scores are normalized according to merchant size grouping through the use of multipliers. The normalized scores are summed together day-by-day into a final score. A timely warning of an underlying and expanding security rupture caused by a merchant data breach is issued for damage control and law enforcement.

Abstract: A business method to reverse an authorization request denial made according to general guidelines if the particular customer and the particular transaction pass various threshold tests. Alternatively, each customer is assigned different and independent personal thresholds for transaction types, amounts, times, locations, and context. These thresholds are then applied if an instant payment transaction request is about to be declined.

Abstract: A computer-implemented method for real-time transaction fraud vetting, including: receiving a transaction record including real-time transaction data and a key identifying a transacting entity; matching the key to a plurality of profiles of the transacting entity, each of the plurality of profiles including a data attribute; accessing a datapoint for each of the plurality of profiles, each datapoint representing a standard for the corresponding data attribute computed from historical transaction records of the transacting entity; assessing whether deviation of the real-time transaction data from each datapoint exceeds a corresponding threshold; incrementing a transaction risk corresponding to the transaction record for each deviation from one of the datapoints that exceeds the corresponding threshold; outputting a fraud score based at least in part on the transaction risk; and updating at least one datapoint based on the corresponding real-time transaction data to generate an updated datapoint set for the plu

Abstract: A method of improving the training and performance of predictive models. A first method of operating an artificial intelligence machine produces predictive model language documents describing improved predictive models that generate better business decisions from raw data record inputs. A second method of operating an artificial intelligence machine including processors for predictive model algorithms produces and outputs better business decisions from raw data record inputs. Both methods enrich the raw data records their processors are fed by deleting data fields with data values that have little benefit in decision making, and that derive and add new data fields from information sources then available that do benefit in the decision making of the artificial intelligence machine through improved accuracies of prediction.

Abstract: A method of reducing financial fraud by operating artificial intelligence machines organized into parallel sets of predictive models with each set specially trained with supervised and unsupervised training data filtered for a particular financial channel. Each set integrates several artificial intelligence classifiers like neural networks, case based reasoning, decision trees, genetic algorithms, fuzzy logic, business rules and constraints, smart agents and associated real-time profiling, recursive profiles, and long-term profiles. Suspicious and abnormal activities in any channel communicate across predictive models for all the financial channels through real-time memory storage updates to the smart agent profiles they all share.

Abstract: A method of preventing healthcare fraud-waste-abuse uses artificial intelligence machines to limit financial losses. Healthcare payment request claims are analyzed by predictive models and their behavioral details are compared to running profiles unique to each healthcare provider submitting the claims. A decision results that the instant healthcare payment request claim is or is not fraudulent-wasteful-abusive. If it is, a second analysis of a group behavioral in which the healthcare provider is clustered using unsupervised learning algorithms and compared to a running profile unique to each group of healthcare providers submitting the claims. An overriding decision results if the instant healthcare payment request claim is or is not fraudulent-wasteful-abusive according to group behavior.

Abstract: A merchant data breach process comprises processing daily payment transaction data with a risk and compliance platform to obtain a fraud score for each constituent transaction. Constituent transactions with high risk fraud scores are sorted into a table according to the transaction date, cardholder, and merchant. The table data is scored according to suspected card visits, highly probable visits, and all card visits. The scores are normalized according to merchant size grouping through the use of multipliers. The normalized scores are summed together day-by-day into a final score. A timely warning of an underlying and expanding security rupture caused by a merchant data breach is issued for damage control and law enforcement.

Abstract: A computer implemented and electronic process is provided that uses artificial intelligence to detect unauthorized activity by an insider or hacker. Electronic systems that employ artificial intelligence and machine learning to detect unauthorized transaction activity by insiders or hackers for a computer network system are also provided. Hardware required for carrying out the invention typically include a plurality of networked computers. Specialized software and/or firmware is typically needed in connection with the hardware for carrying out the invention.

Abstract: A method for operating digital electronic appliance that empanels several different artificial intelligence (AI) classification technologies into a “jury” uses combinational digital logic to render “verdicts” about the need for service and impending equipment failures of the machines they monitor. Networks can be used to forward signals from remote locations to a centralized appliance that may be plugged as a module into a server. The appliance outputs can also be communicated over networks to servers that will muster appropriate maintenance personnel who are forewarned as to the nature of the trouble.

Abstract: A computer-implemented method for real-time transaction fraud vetting, including: receiving a transaction record including real-time transaction data and a key identifying a transacting entity; matching the key to a plurality of profiles of the transacting entity, each of the plurality of profiles including a data attribute; accessing a datapoint for each of the plurality of profiles, each datapoint representing a standard for the corresponding data attribute computed from historical transaction records of the transacting entity; assessing whether deviation of the real-time transaction data from each datapoint exceeds a corresponding threshold; incrementing a transaction risk corresponding to the transaction record for each deviation from one of the datapoints that exceeds the corresponding threshold; outputting a fraud score based at least in part on the transaction risk; and updating at least one datapoint based on the corresponding real-time transaction data to generate an updated datapoint set for the plu

Abstract: An automated method of predictive model development first cleans up raw supervised and unsupervised training data with a step that uses an algorithm to make every field of every record consistent, cohesive, and productive. Then the resulting flat data is given texture in a next step by a data enrichment algorithm that culls fields that do not contribute to predictive model building and that adds new fields computed from data combinations that are tested to add value to later steps that build different types of predictive models. Another late step for building smart-agents and their entity profiles uses another algorithm that benefits greatly from the cleaned and highly enriched training data. The predictive models and smart-agents and their entity profiles are then rendered as deliverable predictive model markup language documents in a final step executed by a specialized algorithm.

Abstract: A method for minimizing merchant data breach damage depends on computers and financial networks to carry out its steps. Every payment card transaction witnessed each day by a network server is assessed by a “jury” of fraud classification algorithms and assigned a fraud-risk-verdict. Those payment transactions receiving a high-risk-fraud verdict are retained and sorted into a table according to transaction date, cardholder, and merchant. The raw verdicts are normalized and standardized according to merchant size groups, e.g., to even the comparisons that will be made. A daily tally is made for each merchant of the number of suspected-card-visits, the number of highly-probable-card-visits, and the number of total-card-visits. A merchant data-breach alert is issued if a final score and sum of the normalized verdicts exceeds a threshold.

Abstract: A method of improving the training and performance of predictive models. A first method of operating an artificial intelligence machine produces predictive model language documents describing improved predictive models that generate better business decisions from raw data record inputs. A second method of operating an artificial intelligence machine including processors for predictive model algorithms produces and outputs better business decisions from raw data record inputs. Both methods enrich the raw data records their processors are fed by deleting data fields with data values that have little benefit in decision making, and that derive and add new data fields from information sources then available that do benefit in the decision making of the artificial intelligence machine through improved accuracies of prediction.

Abstract: A business method to reverse an authorization request denial made according to general guidelines if the particular customer and the particular transaction pass various threshold tests. Alternatively, each customer is assigned different and independent personal thresholds for transaction types, amounts, times, locations, and context. These thresholds are then applied if an instant payment transaction request is about to be declined.

Abstract: A real-time fraud prevention system enables merchants and commercial organizations on-line to assess and protect themselves from high-risk users. A centralized database is configured to build and store dossiers of user devices and behaviors collected from subscriber websites in real-time. Real, low-risk users have webpage click navigation behaviors that are assumed to be very different than those of fraudsters. Individual user devices are distinguished from others by hundreds of points of user-device configuration data each independently maintains. A client agent provokes user devices to volunteer configuration data when a user visits respective webpages at independent websites. A collection of comprehensive dossiers of user devices is organized by their identifying information, and used calculating a fraud score in real-time. Each corresponding website is thereby assisted in deciding whether to allow a proposed transaction to be concluded with the particular user and their device.

Abstract: An automated method of predictive model development first cleans up raw supervised and unsupervised training data with a step that uses an algorithm to make every field of every record consistent, cohesive, and productive. Then the resulting flat data is given texture in a next step by a data enrichment algorithm that culls fields that do not contribute to predictive model building and that adds new fields computed from data combinations that are tested to add value to later steps that build different types of predictive models. Another late step for building smart-agents and their entity profiles uses another algorithm that benefits greatly from the cleaned and highly enriched training data. The predictive models and smart-agents and their entity profiles are then rendered as deliverable predictive model markup language documents in a final step executed by a specialized algorithm.

Abstract: A method that improves the training of predictive models. Better trained predictive models make better predictions, and can classify transactions with reduced levels of false positives and false negative. Included is an apparatus for executing a data clean-up algorithm that harmonizes a wide range of real world supervised and unsupervised training data into a single, error-free, uniformly formatted record file that has every field coherent and well populated with information.

Abstract: A real-time fraud prevention system enables merchants and commercial organizations on-line to assess and protect themselves from high-risk users. A centralized database is configured to build and store dossiers of user devices and behaviors collected from subscriber websites in real-time. Real, low-risk users have webpage click navigation behaviors that are assumed to be very different than those of fraudsters. Individual user devices are distinguished from others by hundreds of points of user-device configuration data each independently maintains. A client agent provokes user devices to volunteer configuration data when a user visits respective webpages at independent websites. A collection of comprehensive dossiers of user devices is organized by their identifying information, and used calculating a fraud score in real-time. Each corresponding website is thereby assisted in deciding whether to allow a proposed transaction to be concluded with the particular user and their device.