SYSTEMS, METHODS, AND MEDIA FOR DETERMINING FRAUD RISK FROM AUDIO SIGNALS

1 . A method comprising:

receiving an audio signal and an associated audio signal identifier;

receiving a fraud event identifier associated with a fraud event;

determining a speaker model based on the received audio signal, using a server system;

determining a channel model based on a path of the received audio signal, using the server system;

updating a fraudster channel database to include the determined channel model based on a comparison of the audio signal identifier and the fraud event identifier; and

updating a fraudster voice database to include the determined speaker model based on a comparison of the audio signal identifier and the fraud event identifier.

2 . The method of claim 1 , wherein the audio signal is received without regard to fraud activities.

3 . The method of claim 1 , wherein the channel model represents distortion of a source of the received audio signal along the path of the received audio signal.

4 . The method of claim 1 , further comprising:

receiving a candidate audio sample;

determining a channel match score based on a match between candidate audio sample and the determined channel model in the fraudster channel database;

determining a voice match score based on a match between candidate audio sample and the determined speaker model in the fraudster voice database; and

determining an audio sample risk score based on the channel match score and the voice match score.

5 . The method of claim 4 , further comprising selecting the fraudster voice database based on a match between candidate audio sample and the determined channel model in the fraudster channel database.

6 . The method of claim 4 , wherein the channel match score represents a match between the path of the received audio signal and a path traversed by the received candidate audio sample.

7 . The method of claim 6 , further comprising selecting the fraudster voice database based on the channel match score.

8 . The method of claim 1 , wherein updating a fraudster voice database further comprises comparing a time stamp of the fraud event and a time stamp of the received audio signal.

9 . The method of claim 1 , wherein the fraudster voice database and the fraudster channel database are included in a fraudster database.

10 . A method for screening an audio sample, the method comprising:

maintaining a set of channel models in a server system, each channel model belonging to a disqualified candidate and representing a path of an audio signal associated with an identifier that has been matched to information associated with an instance of fraud;

receiving a screening request, the screening request comprising an audio sample for a candidate;

comparing the audio sample with the channel models in the set of channel models in the server system; and

generating a channel match score based on at least a partial match between the audio sample and a channel model in the set of channel models.

11 . The method of claim 10 , further comprising:

maintaining a set of speaker models in the server system, each speaker model belonging to a disqualified candidate and based on a speaker model associated with an identifier that has been matched to information associated with an instance of fraud; and

generating a voice match score on at least partial match between the audio sample and a speaker model in the set of speaker models.

12 . The method of claim 11 , further comprising generating a risk score based on the voice match score and the channel match score, and providing the risk score to a third party.

13 . The method of claim 11 , further comprising selecting the maintained set of speaker models belonging to disqualified candidates based on the channel score.

14 . A system for analyzing audio, comprising:

a memory for storing executable instructions for analyzing audio;

a processor for executing the instructions;

a communications module stored in memory and executable by the processor to receive an audio signal and an associated audio signal identifier, and to receive a fraud event identifier associated with a fraud event;

an audio analysis module stored in memory and executable by the processor to extract signatures from the received audio signal; and

an enrollment module stored in memory and executable by the processor to compare the audio signal identifier and the fraud event identifier and based on the comparison to store in a fraudster database any of a channel model extracted from the audio signal using the analysis module, a speaker model extracted from the audio signal using the analysis module, or combinations thereof.

15 . The system of claim 14 , wherein the channel model represents a distortion of a signal originating from a source of the received audio signal, the distortion occurring along a path of the audio signal between the source and the communications module.

16 . The system of claim 14 , wherein the enrollment module is further executable by the processor to compare the audio signal identifier and a fraud event identifier, and based on the comparison store in a whitelist database a channel model extracted from the audio signal using the analysis module.

17 . The system of claim 14 , further comprising:

a scoring module stored in memory and executable by the processor to:

receive a candidate audio sample;

determine a channel match score based on an at least partial match between candidate audio sample and the extracted channel model in the fraudster database;

determine a voice match score based on an at least partial match between candidate audio sample and the extracted speaker model in the fraudster database; and

determine a fraud score based on the channel match score and the voice match score.

18 . The system of claim 17 , wherein the channel match score represents an at least partial match of a path between a source of the received audio signal and the communications module, and a path traversed by the received candidate audio.

19 . The system of claim 17 , wherein the scoring module is further executable to select a subset of the fraudster database for comparison of the candidate audio and speaker models in the fraudster database based on the channel match score.

20 . The system of claim 17 , wherein the scoring module stored in memory is further executable by the processor to determine the fraud score based on a whitelist risk score.

21 . The system of claim 20 , wherein the scoring module selects a whitelist database based on the channel match score for comparison of the candidate audio and speaker models in the whitelist database.