49 results about "Fraudulent Data" patented technology

A call processing method is disclosed. A first station initiates a call to a second station. Call information associated with the initiated call is evaluated by comparing the call information with information in a fraud database. Completion of the call is prevented if the evaluation results in a finding of fraud. If the evaluation results in no finding of fraud, the call is completed to the second station via a routing path obtained from a routing database. If the call being completed is deemed to be a call of interest, the routing path obtained from the routing database for completing the call is stored.

Systems, methods, and media for analyzing fraud patterns and creating fraud behavioral models are provided herein. In some embodiments, methods for analyzing call data associated with fraudsters may include executing instructions stored in memory to compare the call data to a corpus of fraud data to determine one or more unique fraudsters associated with the call data, associate the call data with one or more unique fraudsters based upon the comparison, generate one or more voiceprints for each of the one or more identified unique fraudsters from the call data, and store the one or more voiceprints in a database.

An electronic system is provided to confirm the identity of an individual or customer. The confirmation process permits businesses and other entities to access a preapproved list of authorize customers for purposes of preventing identity theft or fraud. Biometric data obtained from the person of the customer is compared against an authorized version of customer biometric data for this purpose. Even if an individual customer is not an authorized customer, the biometric data may be compared to a known fraud database to see if this individual has perpetrated past instances of fraud.

Systems, methods, and media for analyzing fraud patterns and creating fraud behavioral models are provided herein. In some embodiments, methods for analyzing call data associated with fraudsters may include executing instructions stored in memory to compare the call data to a corpus of fraud data to determine one or more unique fraudsters associated with the call data, associate the call data with one or more unique fraudsters based upon the comparison, generate one or more voiceprints for each of the one or more identified unique fraudsters from the call data, and store the one or more voiceprints in a database.

Systems, methods, and other embodiments associated with automated secondary linking for fraud detection systems. According to one embodiment, a system includes a parsing logic configured to receive an incoming fraud report based on a first fraud detection strategy. The parse logic is also configured to parse the incoming fraud report into fraud data. The system further includes a search logic configured to search a set of fraud reports to identify instances of the fraud data. The system also includes an alert logic configured to generate an alert in response to an instance of the fraud data being identified in the set of fraud reports.

A method of determining whether a page is a fraudulent page comprising the steps of: extracting a plurality of tokens from the page, (403) for each token, calculating a token probability being the probability of the token being in a fraudulent page (404), using the calculated token probabilities, calculating a page probability being the probability of the page being a fraudulent page (405), wherein the token probability of a token being in a fraudulent page is calculated based on a number of fraudulent pages and a number of non-fraudulent pages which contain the token from a training corpus of fraudulent pages and non-fraudulent pages.

The invention discloses a trusted electronic transaction release mechanism based on a generative adversarial network. The method is characterized by comprising, the transaction distribution of normalusers is learnt by using the generative adversarial network by using a large amount of non-fraudulent data based on a reverse idea; the obtained discrimination model can better identify non-fraud data, so that a release mechanism can be adopted for credible electronic transactions, and the remaining data which cannot be discriminated are delivered to the prediction model for prediction, so that alarge amount of time can be saved.

A computing device for detecting fraudulent network events in a payment card transaction network is provided. The computing device includes a processor and a display device. The computing device is programmed to receive a first plurality of fraud data elements associated with a plurality of payment card transactions from a fraud detection system. The computing device is also programmed to generate a first graph from the first plurality of fraud data elements. The computing device is further programmed to receive a first plurality of data elements associated with the plurality of payment card transactions from a second system. The computing device is also programmed to generate a second graph from the first plurality of data elements and display both of the first graph and the second graph simultaneously to a user on the display device, such that the user to detect fraudulent events in the payment card transaction network.

A fraud detection method for use in an in-vehicle network system including a plurality of electronic control units that communicate with one another via a bus in accordance with Controller Area Network (CAN) protocol is provided. The method includes receiving at least one data frame sent to the bus, verifying a specific identifier in the received data frame only if the received data frame does not follow a predetermined rule regarding a transmission period and a state of a vehicle having the in-vehicle network system mounted therein is a predetermined state, detecting the received data frame as an authenticated data frame if the verification is successful, and detecting the received data frame as a fraudulent data frame if the verification fails.

The invention comprises a method and system of detecting and identifying fraud arising from a healthcare claim. The system includes a storage means for storing a data base containing source data related to a healthcare claim. A memory means is used for storing a set of user-defined rules for detecting and identifying fraud. A processing means is coupled to the storage means for comparing the source data to the set of rules in the memory means. If the source data violates the set of rules, the relevant portion of the source data is identified and flagged as fraudulent data. The flagged data is then forwarded to a special investigator for a comprehensive analysis. The fraudulent data is transformed to graphs and charts to illustrate patterns so that the fraud is easily detected and identified.

The embodiment of the invention discloses a malicious group determination method and device, electronic equipment and a storage medium. The method comprises the steps of determining a knowledge graphaccording to a relationship between initial entities in historical fraud data; determining the similarity degree between different initial entities in the knowledge graph, and selecting candidate entities from the initial entities according to the similarity degree; according to the candidate entities, dividing the knowledge graph determined by the candidate entities into candidate groups; and determining whether the candidate group is a malicious group or not according to the score of the candidate entity in the candidate group. By running the technical scheme provided by the embodiment of the invention, the problems that a single-dimensional risk control strategy is difficult to acquire malicious gang behaviors in multiple directions, the occurrence time of the malicious gang behaviors is short, and malicious gangs are difficult to effectively discover and deploy the strategy in a short time can be solved, and the risk control efficiency is improved. The effect of improving the efficiency and accuracy of malicious group determination is achieved.

A fraud monitoring procedure in a management network is provided. A fraud monitoring program uses meter data, configuration data, and account information of the network to detect fraudulent activitiesin the network, such as fraudulent advertisements or other types of fraudulent data traffic, including fraudulent responses (such as fraudulent clicks) to advertisers. A fraud monitor receives configuration data and identification data of the network physical resources. The fraud monitor receives instrument data of data packet flow in the network. The fraud monitoring program receives account information of a network user. The fraud monitor analyzes the instrumentation data to detect the violation of a fraud detection policy that prevents malicious or fraudulent online advertising campaign based on the configuration data, the identification data, or the account information.