Network neighborhood topology as a predictor for fraud and anomaly detection
An improved technique involves generating, from historical transaction data, a relational graph that represents connections between users who initiate transactions and transaction devices used to carry out the transactions. By supplementing traditional relational database models with a tool such as...
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| creator | Gendelev, Anatoly Gorelik, Boris Liptz, Liron Blatt, Marcelo Zaslavsky, Alex |
| description | An improved technique involves generating, from historical transaction data, a relational graph that represents connections between users who initiate transactions and transaction devices used to carry out the transactions. By supplementing traditional relational database models with a tool such as a graph database, a risk analysis server is able to express users and transaction devices as nodes in a graph and the connections between them as edges in the graph. The risk analysis server may then match the topology of the graph in a neighborhood of the user initiating the transaction to a known topology that is linked to an indication of risk. In some arrangements, this topology is an input into a risk model used to compute a risk score for adaptive authentication. |
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| subjects | CALCULATING COMPUTING COUNTING DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES PHYSICS SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR |
| title | Network neighborhood topology as a predictor for fraud and anomaly detection |
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