Self-service terminal
First Claim
1. A method of detecting attempted fraud at a self-service terminal, the method comprising:
- providing a classifier having a plurality of input zones, each input zone being adapted to receive data of a different modality;
mapping inputs from a plurality of sensors in the self-service terminal to respective input zones of the classifier;
for each input zone, independently operating on inputs within that zone to create a weighted set;
combining the weighted sets using a dedicated weighting function for each weighted set to produce a composite set; and
deriving a plurality of class values from the composite set to create a classification result.
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Accused Products
Abstract
A method of detecting attempted fraud at a self-service terminal. The method involves providing a classifier having a plurality of input zones, each input zone being adapted to receive data of a different modality. The classifier may be a statistical model. The method also involves mapping inputs from a plurality of sensors in the self-service terminal to respective input zones of the classifier. For each input zone, the classifier independently operates on inputs within that zone to create a weighted set; combines the weighted sets using a dedicated weighting function for each weighted set to produce a composite set; and derives a plurality of class values from the composite set to create a classification result. The classification result can be used to predict the probability of abnormal operation, which may be indicative of fraud.
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Citations
12 Claims
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1. A method of detecting attempted fraud at a self-service terminal, the method comprising:
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providing a classifier having a plurality of input zones, each input zone being adapted to receive data of a different modality; mapping inputs from a plurality of sensors in the self-service terminal to respective input zones of the classifier; for each input zone, independently operating on inputs within that zone to create a weighted set;
combining the weighted sets using a dedicated weighting function for each weighted set to produce a composite set; andderiving a plurality of class values from the composite set to create a classification result. - View Dependent Claims (2, 3, 4, 5)
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6. A method of detecting attempted fraud at a self-service terminal, the method comprising:
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providing a classifier comprising a statistical model; mapping inputs from sensors in the self-service terminal into the classifier; and operating on the inputs using the statistical model to predict a probability of fraud. - View Dependent Claims (7)
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8. A self-service terminal comprising:
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a plurality of customer interaction devices; and a controller coupled to the customer interaction devices for controlling their operation, the controller including a classifier including a statistical model to predict a probability of fraud based on inputs received from the customer interaction devices. - View Dependent Claims (9)
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10. A method of operating a network of self-service terminals coupled to a management system, where each terminal includes a classifier for predicting fraud based on the operation of that terminal, the method comprising:
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receiving a communication from a self-service terminal indicating that the classifier in that terminal has predicted a probability of fraud; and triggering a dispatch signal to dispatch a customer engineer to the self-service terminal to ascertain if fraud is being perpetrated. - View Dependent Claims (11, 12)
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Specification