FIELD OF THE INVENTION

The invention relates generally to the field of voice biometric security and real-time authentication, and more specifically for determining proactive fraudster exposure (e.g., likelihood a caller is a fraudster) in a customer service channel by clustering potential fraudsters and displaying to a user a ranked list of potential fraudsters to add to a watchlist database.

BACKGROUND OF THE INVENTION

Call centers are increasingly becoming a target for fraudsters via their customer service channels. Call center frauds are one of the leading threats that organizations such as financial institutions face. Fraudsters commonly attempt to retrieve information and/or change information of legitimate customers by, for example, exploiting call center agents by social engineering. For example, fraudsters can conduct an attack on a financial institution by manipulating call center agents to provide them with confidential information of legitimate customers and then use the extracted information to commit another fraud e.g., identity theft. In some scenarios, fraudsters use information from social networks and/or public information to correctly answer knowledge-based questions during a call with an agent.

There can be many types of fraudulent activity. Fraud can be performed via multiple frequent attacks, attempts on a singular legitimate customer account and/or on multiple customer accounts. The attacks can be made via different channels such as mobile application, call-center calls and/or internet, and/or on different lines of business e.g., VIP handling agents. Fraud can be a targeted to a specific individual e.g., a specific customer or on various customers in the call center.

Currently, there are systems to identify fraudsters in high volume (e.g., on the order of millions) call centers. Current systems can identify potential fraudsters and generate lists of potential fraudsters. Current systems with high call volumes can involve a run time complexity that can eventually cause the systems to be unable to run. Therefore, it can be desirable to reduce the run-time complexity of systems and methods that identify fraudsters.

SUMMARY OF THE INVENTION

One advantage of the invention can include a reduction in run-time complexity for systems that detect fraudsters. Other advantages of the invention can include an ability to handle a higher volume of calls, e.g., on the order of hundreds of millions, due to, for example, a reduction of run-time complexity. Other advantages of the invention can include larger more homogeneous potential fraudster clusters.

In one aspect, the invention involves a computerized method for improved fraudster detection in a call center. The method can also involve receiving, via a computing device, a plurality of voiceprints from a plurality of interactions between callers and agents at the call center. The method can also involve determining, via the computer, a subset of the plurality of voiceprints. The method can also involve determining, via the computer, for each of the plurality of voiceprints, a kth order moment score based on the subset of the plurality of voiceprints. The method can also involve sorting, via the computer, the plurality of voiceprints by their corresponding kth order moment score to obtain a sorted list of voiceprints. The method can also involve determining, via the computer, for each of the sorted plurality of voiceprints that are nearby in the sorted list, a clustered voiceprint pair for each pair of nearby voiceprints having a difference between their corresponding kth order moment score within a predetermined tolerance and adding the respective clustered voiceprint pair to a clustered voiceprint pair list. The method can also involve performing, by the computing device, a first search on the clustered voiceprint pair list to obtain a plurality of connected component voiceprints. The method can also involve determining, by the computing device, for each of the plurality of connected component voiceprints substantially in parallel, additional voiceprints that are filtered from the respective connected component voiceprint. The method can also involve performing, by the computing device, a second search on the clustered voiceprint pair list to obtain a final plurality of connected component voiceprints, wherein each of the final plurality of connected components is an output that represents one or more voiceprints for use in fraud detection.

In some embodiments, the kth order moment score is a mean score and a standard deviation score. In some embodiments, the sorting is based on the mean score. In some embodiments, voiceprints that are nearby are voiceprints that are within a fixed window, neighbors, or any combination thereof. In some embodiments, a clustered voiceprint pair has a difference between corresponding mean score within a mean score threshold.

In some embodiments, for each of the clustered voiceprint pairs in the clustered voiceprint pair list, if a difference between the corresponding standard deviation scores are above a predetermined threshold, removing, by the computing device, the respective clustered voiceprint pair.

In some embodiments, the first search or the second search is a breadth-first-search, depth-first-search, or any combination thereof. In some embodiments, the subset of the plurality of voiceprints is based on an input value. In some embodiments, receiving the plurality of voiceprints further comprises recording calls received at the call center between the callers and agents in an audio file.

In another aspect, the invention includes a non-transitory computer program product comprising instructions which, when the program is executed cause the computer to receive a plurality of voiceprints from a plurality of interactions between callers and agents at the call center. The computer can also be caused to determine a subset of the plurality of voiceprints. The computer can also be caused to determine for each of the plurality of voiceprints, a kth order moment score based on the subset of the plurality of voiceprints. The computer can also be caused to sort the plurality of voiceprints by their corresponding kth order moment score to obtain a sorted list of voiceprints. The computer can also be caused to determine for each of the sorted plurality of voiceprints that are nearby in the sorted list, a clustered voiceprint pair for each pair of nearby voiceprints having a difference between their corresponding kth order moment score within a predetermined tolerance and adding the respective clustered voiceprint pair to a clustered voiceprint pair list. The computer can also be caused to perform a first search on the clustered voiceprint pair list to obtain a plurality of connected component voiceprints. The computer can also be caused to determine for each of the plurality of connected component voiceprints substantially in parallel, additional voiceprints that are filtered from the respective connected component voiceprint. The computer can also be caused to perform a second search on the clustered voiceprint pair list to obtain a final plurality of connected component voiceprints, wherein each of the final plurality of connected components is an output that represents one or more voiceprints for use in fraud detection.

In some embodiments, the kth order moment score is a mean score and a standard deviation score. In some embodiments, the sorting is based on the mean score. In some embodiments, voiceprints that are nearby are voiceprints that are within a fixed window, neighbors, or any combination thereof.

In some embodiments, a clustered voiceprint pair has a difference between corresponding mean score within a mean score threshold. In some embodiments, for each of the clustered voiceprint pairs in the clustered voiceprint pair list, if a difference between the corresponding standard deviation scores are above a predetermined threshold, removing, by the computing device, the respective clustered voiceprint pair.

In some embodiments, the first search or the second search is a breadth-first-search, depth-first-search, or any combination thereof. In some embodiments, subset of the plurality of voiceprints is based on an input value. In some embodiments, receiving the plurality of voiceprints further comprises recording calls received at the call center between the callers and agents in an audio file.

