Call classification through analysis of DTMF events
Systems, methods, and computer-readable media for call classification and for training a model for call classification, an example method comprising: receiving DTMF information from a plurality of calls; determining, for each of the calls, a feature vector including statistics based on DTMF information such as DTMF residual signal comprising channel noise and additive noise; training a model for classification; comparing a new call feature vector to the model; predicting a device type and geographic location based on the comparison of the new call feature vector to the model; classifying the call as spoofed or genuine; and authenticating a call or altering an IVR call flow.
1 . A computer-implemented method comprising:
obtaining, by a computer, via a remote computer dual-tone multi frequency (DTMF) information associated with a new phone call;
generating, by the computer, a new call feature vector for the new phone call using the DTMF information of the new phone call;
determining, by the computer, a fraud probability that the new phone call is fraudulent or genuine, based upon comparing the new call feature vector against one or more prior call feature vectors for one or more prior phone calls, each prior call feature vector generated based upon the DTMF information of a prior phone call corresponding to the prior call feature vector; and
generating, by the computer, a user interface comprising an indicator of the fraud probability for the new phone call.
2 . The method according to claim 1 , further comprising providing, by the computer, the user interface having the fraud probability for display at the remote computer.
3 . The method according to claim 1 , further comprising, for each prior phone call of the one or more prior phone calls, generating, by the computer, the prior call feature vector using the DTMF information for the prior phone call.
4 . The method according to claim 1 , wherein each prior call feature vector is associated with a corresponding label indicating one or more characteristics of the prior phone call corresponding to the prior call feature vector,
the method further comprising training, by the computer, a classifier model according to each of the prior call feature vectors and the associated label for each of the prior phone calls.
5 . The method according to claim 4 , further comprising generating, by the computer, one or more fingerprint models associated with a known caller by executing the classifier model on a set of prior call feature vectors generated using the DTMF information for one or more of prior phone calls associated with the known caller.
6 . The method according to claim 4 , further comprising classifying, by the computer, the new phone call as fraudulent or genuine by applying the classifier model on the new call feature vector generated for the new phone call.
7 . The method according to claim 1 , further comprising generating, by the computer, ideal DTMF information corresponding to the DTMF information received during an Interactive Voice Response (IVR) session of the new phone call,
wherein the computer generates the new call feature vector for the new phone call based upon one or more differences between the ideal DTMF information and the DTMF information of the new phone call.
8 . The method according to claim 7 , further comprising estimating, by the computer, additive noise in the DTMF information of the new phone call based upon the difference between the ideal DTMF information and the DTMF information of the new phone call.
9 . The method according to claim 7 , further comprising estimating, by the computer, channel noise in the DTMF information of the new phone call based upon the difference between the ideal DTMF information and the DTMF information of the new phone call.
10 . The method according to claim 7 , further comprising, for each prior phone call:
generating, by the computer, prior ideal DTMF information corresponding to the prior DTMF information received during a prior IVR session of the prior phone call; and
generating, by the computer, the prior call feature vector for the prior phone call based upon one or more differences between the prior ideal DTMF information and the prior DTMF information of the prior phone call.
11 . A system comprising:
one or more network interfaces configured to receive dual-tone multifrequency (DTMF) information for a plurality of calls associated with a plurality of phone numbers; and
a processor configured to:
obtain via a remote computer the DTMF information associated with a new phone call;
generate a new call feature vector for the new phone call using the DTMF information of the new phone call;
determine a fraud probability that the new phone call is fraudulent or genuine, based upon comparing the new call feature vector against one or more prior call feature vectors for one or more prior phone calls, each prior call feature vector generated based upon the DTMF information of a prior phone call corresponding to the prior call feature vector; and
generate a user interface comprising an indicator of the fraud probability for the new phone call.
12 . The system according to claim 11 , wherein the processor is further configured to provide the user interface having the fraud probability for display at the remote computer.
13 . The system according to claim 11 , wherein the processor is further configured to, for each prior phone call of the one or more prior phone calls, generate the prior call feature vector using the DTMF information for the prior phone call.
14 . The system according to claim 11 , wherein each prior call feature vector is associated with a corresponding label indicating one or more characteristics of the prior phone call corresponding to the prior call feature vector, and
wherein the processor is further configured to train a classifier model according to each of the prior call feature vectors and the associated label for each of the prior phone calls.
15 . The system according to claim 14 , wherein the processor is further configured to generate one or more fingerprint models associated with a known caller by executing the classifier model on a set of prior call feature vectors generated using the DTMF information for one or more of prior phone calls associated with the known caller.
16 . The system according to claim 14 , wherein the processor is further configured to classify the new phone call as fraudulent or genuine by applying the classifier model on the new call feature vector generated for the new phone call.
17 . The system according to claim 11 , wherein the processor is further configured to generate ideal DTMF information corresponding to the DTMF information received during an Interactive Voice Response (IVR) session of the new phone call, and
wherein the computer generates the new call feature vector for the new phone call based upon one or more differences between the ideal DTMF information and the DTMF information of the new phone call.
18 . The system according to claim 17 , wherein the processor is further configured to estimate additive noise in the DTMF information of the new phone call based upon the difference between the ideal DTMF information and the DTMF information of the new phone call.
19 . The system according to claim 17 , wherein the processor is further configured to estimate channel noise in the DTMF information of the new phone call based upon the difference between the ideal DTMF information and the DTMF information of the new phone call.
20 . The system according to claim 17 , wherein the processor is further configured to, for each prior phone call:
generate prior ideal DTMF information corresponding to the prior DTMF information received during a prior IVR session of the prior phone call; and
generate the prior call feature vector for the prior phone call based upon one or more differences between the prior ideal DTMF information and the prior DTMF information of the prior phone call.