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: Real-time fraud prevention software-as-a-service (SaaS) products include computer instruction sets to enable a network server to receive medical histories, enrollments, diagnosis, prescription, treatment, follow up, billings, and other data as they occur. The SaaS includes software instruction sets to combine, correlate, categorize, track, normalize, and compare the data sorted by patient, healthcare provider, institution, seasonal, and regional norms. Fraud reveals itself in the ways data points deviate from norms in nonsensical or inexplicable conduct. The individual behaviors of each healthcare provider are independently monitored, characterized, and followed by self-spawning smart agents that can adapt and change their rules as the healthcare providers evolve. Such smart agents will issue flags when their particular surveillance target is acting out of character, outside normal parameters for them. Fraud controls can therefore be much tighter than those that have to accommodate those of a diverse group.

Abstract: A method that protects a network computer system from the malicious acts of hackers and its own system administrators includes a behavior analysis of system administrators within the confines of their particular job tasks. A suite of abnormal behavior detection models analyze each display terminal input entered by recognized system administrators. Any alerts that issue are forwarded to a case management system for investigation. A cadre of trained investigators with case management display terminals attached to control the case management system make decisions based on what is displayed to them and issue actions and reports by inputs to their display terminals. These contribute to the building and updating of a database and updating of the abnormal behavior detection models through adaptive learning. The decisions, actions and reports control the accessibility and authority of each system administrator at their respective display terminals.

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 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 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 network computer system is protected from malicious attacks by its own system administrators by a large number of addressable and assignable smart-agents that are individually allocated to independently follow and represent those system administrators, the jobs those system administrated are assigned to work on, and the system resource tasks that such system administrators can employ in furtherance of the completion of a particular job.

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: 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 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: A method of fraud management follows several steps that each require the involvement and support of financial networks, secure servers, and proprietary databases. A selected variety of fraud classification algorithms are assembled together into a “jury” that includes neural networks, case based reasoning, decision trees, genetic algorithms, fuzzy logic, and rules and constraints. Operating parameters that matter specifically to each are extracted in parallel from the same records of historical transaction data, and that then is used to initialize a general payment fraud model. This is then converted into computer-program executable form for later execution on a third party computer system. These are further integrated by expert programmers and development system with smart agents and associated real-time profiling, recursive profiles, and long-term profiles.

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 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 financial payment authorization data processing system comprises a payment transaction request fraud scoring data structure that suffers occasionally from falsely scoring a legitimate transaction by a cardholder as fraudulent and would otherwise “decline” the transaction request. A so-called “false positive”. The financial payment authorization data processing system further includes a smart agent data structure 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 cardholder who's transaction request is on the table. The computed level of transaction risk that is acceptable is raised in proportion to the cardholder's business value. As a further expedient, such quality cardholders would never be subject to a “declined transaction” if the requested payment transaction was less than some liberal minimum.

Abstract: Independent and addressable smart agent data structures are assignable and discernible in large populations of them. Each provides for the recording and profiling of the corresponding constituent transaction behaviors of identifiable entities decodable in a stream of incoming transaction records. Each smart agent data structure comprises an array of attributes that together render the available details reported in the stream of incoming transaction records.

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: A network computer system is protected from malicious attacks by its own system administrators by a large number of addressable and assignable smart-agents that are individually allocated to independently follow and represent those system administrators, the jobs those system administrated are assigned to work on, and the system resource tasks that such system administrators can employ in furtherance of the completion of a particular job.

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: Real-time fraud prevention software-as-a-service (SaaS) products include computer instruction sets to enable a network server to receive medical histories, enrollments, diagnosis, prescription, treatment, follow up, billings, and other data as they occur. The SaaS includes software instruction sets to combine, correlate, categorize, track, normalize, and compare the data sorted by patient, healthcare provider, institution, seasonal, and regional norms. Fraud reveals itself in the ways data points deviate from norms in nonsensical or inexplicable conduct. The individual behaviors of each healthcare provider are independently monitored, characterized, and followed by self-spawning smart agents that can adapt and change their rules as the healthcare providers evolve. Such smart agents will issue flags when their particular surveillance target is acting out of character, outside normal parameters for them. Fraud controls can therefore be much tighter than those that have to accommodate those of a diverse group.