System and method detecting fraud using machine-learning and recorded voice clips
AssigneeCapital One Services, LLC
InventorsKaitlin Newman, Jeffrey Rule, Rajko Ilincic
Life of this patent
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Abstract
A system and method are disclosed for training a machine-learning model to detect characteristics of fraudulent calls. The machine-learning model is trained using audio clips, voice recognition, call handler feedback and general public knowledge of commercial risks to detect and divert fraudulent calls, thereby alleviating the burdens otherwise placed on call center service representatives.
Claims20
A system for detecting fraudulent calls received from an incoming call network by a call service center comprising a plurality of call handlers comprises:
- a fraud training table comprising at least one fraud training entry, the fraud training entry storing information related to at least one characteristic of a fraudulent caller as indicated by a call handler, the at least one characteristic including an audible characteristic;
- a machine-learning model coupled to receive the at least one fraud training entry, and to use the fraud training entry for training the machine-learning model to provide a trained machine-learning model;
- and
- a call center interface, coupled between the incoming call network and the call handler, to apply the trained machine-learning model to data associated with a call and to selectively route the call received from the incoming call network to the call handler in response to applying the machine-learning model.
A method for detecting fraudulent calls received at a call service center includes the steps of:
- receiving a call at the call service center and selectively forwarding the call to a call handler;
- monitoring characteristics of the call to locate audible characteristics, including background noises, which are identified in a fraud training table as being associated with fraudulent calls by a call handler;
- and
- responsive to the location of an audible characteristic that is identified in the fraud training table, calculating a probability that the call is from a fraudulent caller.
A method for detecting fraudulent calls received at a call service center includes the steps of:
- training a machine-learning model to recognize characteristics, including background noise characteristics, of fraudulent calls to provide a trained machine-learning model, the training performed using a fraud training table, wherein the fraud training table comprises a plurality of entries corresponding to a plurality of characteristics of fraudulent calls;
- receiving a call at the call service center;
- deploying a call agent to handle the call, the call agent comprising a copy of the trained machine-learning model;
- during the call, the call agent analyzing characteristics of the call to identify characteristics of the call that are associated with fraudulent calls utilizing the trained machine-learning model;
- calculating a probability that the call is fraudulent in response to the step of analyzing;
- responsive to the probability, selectively routing the call to a call handler;
- displaying the probability at a graphical user interface (GUI) of the call handler;
- and
- determining, in response to at least one of a call handler input or the probability, that the call is from a fraudulent caller;
- and
- diverting the call.
Dependencies are inferred from the claim text.