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Fraud detection and security system for financial institutions

InactiveUS20030182214A1Limit losses to individual FIsAvoid checkingFinancePayment circuitsSocial Security numberCheque
A fraud detection and security system for financial institutions utilizes a secure database of information relating to financial transactions of non-account holders who present checks and other instruments for payment. The system collects and tracks the frequency of particular aspects of the subject's behavior, and flags deviations from such norms for the purpose of indicating that fraudulent or criminal behavior may be occurring. At such time, the teller or other employee with whom the subject is dealing may stop the transaction, to the benefit of the financial institution and account holder. The system also allows for law enforcement to detect related transactions, or a string of criminal activity from the same perpetrator. In the preferred mode, the system includes a teller collecting information from the non-account holder, including name, date of birth, address, gender, driver's license number, social security number, and / or telephone number. At the time of the transaction, such data is submitted to the system database and the database returns a response code based upon criteria established by the financial institution's desired security measures, accomplished by installation of new software or by integration of a custom program. The system alerts tellers to suspicious activity, such as when a particular account is accessed more than once in a day, or when the same non-account holder presents items for payment at multiple branches of a banking institution in a short period of time.
Owner:TAYLOR MICHAEL K

Port ore stockyard identification method based on remote sensing screen classification

The invention discloses a port ore stockyard identification method based on remote sensing screen classification. The method comprises the following steps of firstly, performing texture characteristicidentification on a random area of a remote sensing image, and extracting a port coast edge line; secondly, performing classification based on CA change through different scene picture characteristicdifferences; based on accurate identification to an ore stockyard spatial relation characteristic, extracting an association relation of the ore stockyard, and forming a characteristic point set by the identified to-be-analyzed ore stockyard; finally, establishing a classifier through regular logic regression, and performing secondary optimization through a classifier for obtaining a constrainedcondition; combining a theme probability characteristic and performing scene classification by means of a vector classifier for identifying the ore stockyard; extracting an original attribute througha texture characteristic, eliminating impurity data, and obtaining a scene picture characteristic after layering capability obtainment through CA conversion. The port ore stockyard identification method has relatively high pertinence and can calibrate data in real time, thereby performing data updating identification according to a field condition and realizing high accuracy.
Owner:TRANSPORT PLANNING & RES INST MINIST OF TRANSPORT
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