ENHANCED FRAUD DETECTION

• US 20150269942A1
• Filed: 03/21/2014
• Published: 09/24/2015
• Est. Priority Date: 03/21/2014
• Status: Active Grant

• Current Assignee: Wells Fargo Bank NA (Wells Fargo & Company)
• Original Assignee: Wells Fargo Bank NA (Wells Fargo & Company)
• Inventors: Jones, Raymond F.
• Granted Patent: US 9,472,194 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 21/32 using biometric data, e.g. fingerprints, iris scans or voiceprints
• CPC Class Codes: G06Q 30/0185 Product, service or business identity fraud
• CPC Class Codes: G10L 17/02 Preprocessing operations, e.g. segment selection; Pattern representation or modelling, e.g. based on linear discriminant analysis [LDA] or principal components; Feature selection or extraction
• CPC Class Codes: G10L 17/04 Training, enrolment or model building
• CPC Class Codes: G10L 17/06 Decision making techniques; Pattern matching strategies
• CPC Class Codes: G10L 17/22 Interactive procedures; Man-machine interfaces
• CPC Class Codes: G10L 17/26 Recognition of special voice characteristics, e.g. for use in lie detectors; Recognition of animal voices
• CPC Class Codes: G10L 21/0208 Noise filtering
• CPC Class Codes: H04M 15/47 Fraud detection or prevention means
• CPC Class Codes: H04M 2203/301 Management of recordings
• CPC Class Codes: H04M 3/42221 Conversation recording systems at the subscriber's set H04M1/656
• CPC Class Codes: H04M 3/5175 Call or contact centers supervision arrangements

Abstract

Embodiments of techniques or systems for fraud detection are provided herein. A communication may be received where the communication includes one or more voice signals from an individual. Frequency responses associated with these voice signals may be determined and analyzed and utilized to determine whether or not potential fraudulent activity is occurring. For example, if a frequency response is greater than a frequency threshold, potential fraudulent activity may be determined. Further, frequency responses may be cross referenced with voice biometrics, voice printing, or fraud pathway detection results. In this way, voice stress or frequency responses may be utilized to build other databases related to other types of fraud detection, thereby enhancing one or more aspects of fraud detection. For example, a database may include a voice library, a pathway library, or a frequency library which include characteristics associated with fraudulent activity, thereby facilitating identification of such activity.

Claims

1. A system for fraud detection, comprising:

a database component comprising a voice library comprising one or more voice samples of individuals deemed to be fraudsters;

a monitoring component;

identifying one or more segments of a communication as a salutation segment, a verification segment, a conversation segment, or a summary segment;

isolating one or more voice signals from one or more of the segments of the communication; and

comparing one or more of the voice signals from one or more of the segments of the communication with one or more of the voice samples of the voice library to generate a voice match determination;

an analysis component analyzing one or more frequency responses of one or more of the voice signals from one or more of the segments of the communication to generate a frequency determination; and

a fraud component identifying potential fraudulent activity based on the voice match determination or the frequency determination;

wherein the monitoring component, the database component, the analysis component, or the fraud component is implemented via a processing unit.

2. The system of claim 1, wherein the fraud component identifies potential fraudulent activity by weighing the voice match determination heavier than the frequency determination.

3. The system of claim 1, wherein the fraud component identifies potential fraudulent activity by considering the voice match determination prior to considering the frequency determination.

4. The system of claim 1, wherein the monitoring component filters noise from one or more of the segments of the communication.

5. The system of claim 1, wherein the analysis component generates the frequency determination based on a comparison between one or more of the frequency responses and a frequency threshold.

6. The system of claim 1, wherein the analysis component generates the frequency determination based on a comparison between one or more of the frequency responses of one or more of the segments of the communication and one or more other frequency responses of one or more other segments of the communication.

7. The system of claim 1, wherein the fraud component provides one or more notifications to one or more parties when potential fraudulent activity is identified.

8. The system of claim 1, wherein the monitoring component isolates one or more of the voice signals for a live communication or a recorded communication.

9. The system of claim 1, wherein one or more of the voice signals is from an individual.

10. The system of claim 1, wherein one or more of the voice signals is from a representative of a call center.

11. A system for fraud detection, comprising:

a database component comprising a pathway library comprising one or more sets of characteristics deemed to be associated with fraudulent communication;

a monitoring component;

identifying one or more segments of a communication as a salutation segment, a verification segment, a conversation segment, or a summary segment; and

isolating one or more voice signals from one or more of the segments of the communication;

a detection component;

determining one or more characteristics associated with the communication; and

comparing one or more of the characteristics associated with the communication with one or more of the sets of characteristics of the pathway library to generate a pathway match determination;

an analysis component analyzing one or more frequency responses of one or more of the voice signals from one or more of the segments of the communication to generate a frequency determination; and

a fraud component identifying potential fraudulent activity based on the pathway match determination or the frequency determination;

wherein the monitoring component, the database component, the detection component, the analysis component, or the fraud component is implemented via a processing unit.

12. The system of claim 11, wherein one or more of the characteristics associated with the communication is indicative of a technology associated with the communication.

13. The system of claim 11, wherein one or more of the characteristics associated with the communication is indicative of one or more artifacts associated with the communication.

14. The system of claim 11, wherein one or more of the characteristics associated with the communication is indicative of noise associated with the communication.

15. The system of claim 11, wherein the communication occurs over a voice over internet protocol (VoIP) channel or a telecommunications channel.

16. The system of claim 11, wherein the analysis component utilizes one or more of the voice signals from the salutation segment or conversation segment as a baseline for generating the frequency determination.

17. A system for fraud detection, comprising:

a database component comprising;

a voice library comprising one or more voice samples of individuals deemed to be fraudsters; and

a pathway library comprising one or more sets of characteristics deemed to be associated with fraudulent communication;

a monitoring component;

identifying one or more segments of a communication as a salutation segment, a verification segment, a conversation segment, or a summary segment;

isolating one or more voice signals from one or more of the segments of the communication; and

comparing one or more of the voice signals from one or more of the segments of the communication with one or more of the voice samples of the voice library to generate a voice match determination;

a detection component;

determining one or more characteristics associated with the communication; and

comparing one or more of the characteristics associated with the communication with one or more of the sets of characteristics of the pathway library to generate a pathway match determination;

an analysis component analyzing one or more frequency responses of one or more of the voice signals from one or more of the segments of the communication to generate a frequency determination; and

a fraud component identifying potential fraudulent activity based on the voice match determination, the pathway match determination, or the frequency determination;

wherein the monitoring component, the database component, the detection component, the analysis component, or the fraud component is implemented via a processing unit.

18. The system of claim 17, wherein the fraud component identifies potential fraudulent activity by weighing the voice match determination heavier than the frequency determination.

19. The system of claim 17, wherein the fraud component identifies potential fraudulent activity by considering the voice match determination prior to considering the frequency determination.

20. The system of claim 17, wherein the monitoring component filters noise from one or more segments of the communication.