62 results about "Credit card fraud" patented technology

A multi-level anomaly detection method based on exponential smoothing, sliding window distribution statistics and an integrated learning model comprises the following steps of a statistic detection stage, an integrated learning training stage and an integrated learning classification stage, wherein in the statistic detection stage, a, a key feature set is determined according to the application scene; b, for discrete characteristics, a model is built through a sliding window distribution histogram, and a model is built through exponential smoothing for continuous characteristics; c, the observation features of all key features are input periodically; d, the process is ended. In the integrated learning training stage, a, a training data set is formed by marked normal and abnormal examples; b, a random forest classification model is trained. The method provides a general framework for anomaly detection problems comprising time sequence characteristics and complex behavior patterns and is suitable for online permanent detection, the random forest model is used in the integrated learning stage to achieve the advantages of parallelization and high generalization ability, and the method can be applied to multiple scenes like business violation detection in the telecom industry, credit card fraud detection in the financial industry and network attack detection.

A fraud score calculating program primarily for use in determining the possibility of credit card fraud can calculate a score reflecting the reliability of a model created based on Bayesian theory. Various factors are extracted from authorization data, and cases are selected for various combinations of the factors. A fraud score is calculated for each case, and a result of score calculation is specified using at least one of the calculated scores. The result of score calculation may be an average of a plurality of the calculated scores. When each score reflects reliability, the result of score calculation may be the largest of the calculated scores.

A mobile computing device is adapted to transmit to a scoring server URLs of websites browsed using the device. The scoring server can compare these URLs against a merchant URL obtained within a preselected time period from transaction data resulting from a transaction involving a payment product of the device user. A score can be calculated based on the similarity between each URL obtained from the device and the URL from the transaction data. The score represents the likelihood that a website browsed using the device and, as a result, the transaction, is fraudulent. The browsed URLs can also be scored against a database of known fraudulent websites. A notification concerning the legitimacy of the transaction based on the score can be generated and sent to the mobile device in real-time. On receiving the notification, the device can be used to either accept or decline the transaction in real-time.

A trigger card and system for preventing credit and debit card fraud is disclosed. The trigger card is adapted to be indistinguishable from typical credit cards and other cards issued by financial institutions. The system is adapted to receive signals when a standard card reader reads the trigger card. The system is further adapted to initiate an automated procedure to reduce fraud upon receipt of the signal indicating that the trigger card has been read by the card reader.

A number of novel watermarking applications, and improvements to watermarking methods, are disclosed. Included are techniques for encoding printed circuit boards and street signs with watermarks, deterring credit card fraud and controlling software licensing using watermarks, registering collectibles via watermarks, encoding the margins of printed pages with watermarks, and using watermarks to convey extra information in video by which fidelity of the rendered video may be improved. One particular arrangement is a method in which a PDA, wristwatch, or other portable device with a display screen presents a pattern that includes a machine-readable identifier. This image is sensed by a separate device, such as a webcam or a camera-equipped cell phone. The sensing device can then take an action based on the identifier. In some arrangements, the action is personalized to the user.

The invention provides a credit card fraud detection method and system based on undersampling, a medium and equipment. The method comprises: fitting majority class samples of a training set in a dataset by using a Gaussian mixture model; predicting probability density values of minority class samples in the training set by using the fitted Gaussian mixture model, and selecting a maximum value inthe probability density values as a cross edge of the two classes of samples; taking the cross edge as a center, extending upwards and downwards from the cross edge to set a sampling upper bound and asampling lower bound so as to carry out undersampling to obtain an undersampling data set, and combining the undersampling data set with the minority class sample set to form an equalization trainingset; training a machine learning classifier according to the balanced training set; and detecting a credit card transaction data set by using the trained machine learning classifier. The Gaussian mixture model is used for grabbing the samples with the two types of samples distributed at the crossed edges, more useful information is provided for recognition of the two types of samples, and the recognition accuracy of the classifier in the field of credit card fraud detection is improved.

A mobile computing device is adapted to transmit to a scoring server URLs of websites browsed using the device. The scoring server can compare these URLs against a merchant URL obtained within a preselected time period from transaction data resulting from a transaction involving a payment product of the device user. A score can be calculated based on the similarity between each URL obtained from the device and the URL from the transaction data. The score represents the likelihood that a website browsed using the device and, as a result, the transaction, is fraudulent. The browsed URLs can also be scored against a database of known fraudulent websites. A notification concerning the legitimacy of the transaction based on the score can be generated and sent to the mobile device in real-time. On receiving the notification, the device can be used to either accept or decline the transaction in real-time.

The invention belongs to the technical field of face recognition and especially relates to a novel precise face recognition system. The system includes a high definition camera, an infrared human bodysensor, an auxiliary light source, a control chip, a Gabor filter, an image normalizer, an ARM processor, a database and a display device. An expression action recognition filtering and comparison DSP processor is also connected with an expression action recognition buffer module. Through setting of the expression action recognition filtering and comparison DSP processor and the expression actionrecognition buffer module, a series of expressions or actions for identity verification of a person can be taken as identify verification information, so that credit card fraud when the precision face recognition system is applied to payment can be prevented and a problem of face recognition of twins can be solved easily.