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 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.

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: An Internet-of-things (IoT) mechanizes, computerizes, automates, instruments, includes, and connects a broadly dispersed and extensively diverse universe of unrelated “things” to the Internet, e.g., credit cards, home appliances, industrial machinery, airplanes, cars, municipal water pumps, mobile devices, rain gauges, etc. Each thing is assigned a resident local “smart agent”. Or an entity, manifesting remotely only as transaction records and reports, is assigned a virtual smart agent in a network server. These data structures follow, track, record, chart, monitor, characterize, describe, render, and otherwise provide a label and handle on independent things and entities.

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 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 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 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: An artificial-intelligence based, authorization data processing system and method that use networked electronic computers are provided. Included are one or more smart agent data structures tasked to individually follow past transaction data and behaviors, and to provide its artificial intelligence observations on the magnitude, type, and quality of payment card revenues and business routinely engaged in by the individual, e.g., a cardholder whose transaction request is pending. The computed level of acceptable transaction risk is raised in proportion to the cardholder's business value. As a further expedient, such quality cardholders may never be subject to a “declined transaction” if the requested payment transaction were less than some liberal minimum to meet an appropriate threshold level.

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 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: An Internet-of-things (IoT) mechanizes, computerizes, automates, instruments, includes, and connects a broadly dispersed and extensively diverse universe of unrelated “things” to the Internet, e.g., credit cards, home appliances, industrial machinery, airplanes, cars, municipal water pumps, mobile devices, rain gauges, etc. Each thing is assigned a resident local “smart agent”. Or an entity, manifesting remotely only as transaction records and reports, is assigned a virtual smart agent in a network server. These data structures follow, track, record, chart, monitor, characterize, describe, render, and otherwise provide a label and handle on independent things and entities.

Abstract: Provided is an artificial-intelligence based, electronic computer implemented system for generating an alert to a likelihood of money laundering activity within a financial environment, comprising at least one computer that includes both hardware and software components. The components form: a smart agent generating means for generating a smart agent for each entity capable of acting by itself or in concert with another in furtherance of money laundering activity; an updating means for updating each smart agent with transaction based data (financial and/or non-financial) associated therewith so that each smart agent models an individual entity behavior profile; a supervised learning model for a first-pass detection of potential money laundering activity; an unsupervised learning model for reducing false positive and enhancing detection of potential money laundering activity; and an alerting means for generating an alert to a likelihood of money laundering activity within the financial environment.

Abstract: Provided are artificial-intelligence based, electronic computer implemented processes and systems of analyzing credit risk and/or predicting default to an institution by an entity having been issued a plurality of credentials by individuals of the entity. The processes and systems involve: providing a smart agent for each credential; updating each smart agent with data of its credential so that so that each smart agent models an individual behavior profile; computing timely fields for the entity on spent using the credentials, amounts paid to the institution, and amounts due to the institution, thereby providing an timely assessment of overall entity demographic and financial situation; using the assessment to assign to the entity a risk level of delinquency; and predicting a likelihood of upcoming delinquency or default by the entity to the institution based on the individual behavior profiles modeled by the smart agents and on the risk level.

Abstract: An artificial intelligence fraud management solution comprises a development system to generate a population of virtual smart agents corresponding to every cardholder, merchant, and device ID that hinted at during modeling and training. Each smart agent is nothing more than a pigeonhole and summation of various aspects of every transaction in a real-time profile of less than ninety days and a long-term profile of transactions older than ninety days. Actors and entities are built of no more than the attributes the express in each transaction. In fact, smart agents themselves take no action on their own and are not capable of gesticulations. They are merely attributes, descriptors, what can be seen on the surface.

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 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: An artificial intelligence system comprises a computer network server connected to receive and analyze millions of simultaneous text and/or voice messages written by humans to be read and understood by humans. Key, or otherwise important words in sentences are recognized and arrayed. Each such word is contributed to a qualia generator that spawns the word into its possible contexts, themes, or other reasonable ambiguities that can exist at the level of sentences, paragraphs, and missives. A thesaurus-like table is employed to expand each word into a spread of discrete definitions. Several such spreads are used as templates on the others to find petals that exhibit a convergence of meaning. Once the context of a whole missive has been predicted, each paragraph is deconstructed into sub-contexts that are appropriate within the overall theme. Particular contexts identified are then useful to trigger an actionable output.

Abstract: An artificial intelligence fraud management system comprises a real-time analytics process for analyzing the behavior of a user from the transaction events they generate over a network. An initial population of smart agent profiles is stored in a computer file system and more smart agent profiles are added as required as transaction data is input. Transactions in particular merchant category codes (MCC) are likely to be followed by predictable related transactions. A forecast of those likely future transactions is calculated and used to desensitize corresponding smart agent profile datapoints. Fewer false positives are produced and overall fraud management performance is improved.

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.