Fraud detection database

• US 10,043,190 B1
• Filed: 02/13/2017
• Issued: 08/07/2018
• 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.
• Primary Examiner(s): ALBERTALLI, BRIAN LOUIS
• Application Number:
  US15/431,280
• Time in Patent Office: 540 Days
• 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; and

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

a monitoring component receiving a communication, wherein the communication comprises one or more voice signals of an individual making one or more requests associated with an account;

a detection component determining one or more characteristics associated with the communication;

a fraud component updating the voice library or the pathway library based on activity deemed to be fraudulent associated with the account;

an analysis component determining one or more frequency responses of one or more of the voice signals of the communication

wherein the monitoring component is operative to generate a voice match determination based on a comparison between one or more of the voice signals of the communication and one or more voice signals or voice samples from the voice library,

wherein the fraud component is operative to identify potential fraudulent activity by weighing the voice match determination more heavily than the frequency determination,

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

2. The system of claim 1, comprising an analysis component determining one or more frequency responses of one or more of the voice signals of the communication.

3. The system of claim 2, wherein the fraud component updates a frequency library with one or more of the frequency responses associated with the individual.

4. The system of claim 1, wherein the fraud component deems activity associated with the account to be fraudulent based on a comparison between one or more of the voice signals and one or more of the voice samples of the voice library.

5. The system of claim 1, wherein the fraud component deems activity associated with the account to be fraudulent based on a comparison between one or more of the characteristics associated with the communication and one or more sets of the characteristics of the pathway library.

6. The system of claim 1, wherein the activity deemed to be fraudulent for the account is determined at a time after the communication has occurred.

7. The system of claim 1, wherein the activity deemed to be fraudulent is discovered manually.

8. A method for fraud detection, comprising:

storing;

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

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

a frequency library which includes characteristics associated with account owners,

receiving a communication, wherein the communication comprises one or more voice signals of an individual making one or more requests associated with an account;

determining one or more characteristics associated with the communication; and

updating the voice library or the pathway library based on activity deemed to be fraudulent associated with the account;

determining one or more frequency responses of one or more voice signals of the communication;

generating a voice match determination based on a comparison between one or more of the voice signals of the communication and one or more voice signals or voice samples from the voice library;

identifying potential fraudulent activity by weighing the voice match determination more heavily than the frequency determination;

wherein the frequency library is utilized to compile patterns associated with account owners,

wherein the storing, the receiving, the determining, the generating, the identifying or the updating is implemented via a processing unit.

9. The method of claim 8, comprising updating a frequency library with one or more of the frequency responses associated with the individual.

10. The method of claim 8, comprising deeming activity associated with the account to be fraudulent based on a match between one or more of the voice signals and one or more of the voice samples of the voice library.

11. The method of claim 8, comprising deeming activity associated with the account to be fraudulent based on a match between one or more of the characteristics associated with the communication and one or more sets of the characteristics of the pathway library.

12. 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 receiving a communication, wherein the communication comprises one or more voice signals of an individual making one or more requests associated with an account, wherein the communication is recorded, wherein the monitoring component is operative to generate a voice match determination based on a comparison between one or more of the voice signals of the communication and one or more voice signals or voice samples from the voice library;

a detection component determining one or more characteristics associated with the communication, utilizing one or more characteristics associated with the communication to generate reverse lookup data or reverse path data for the communication in order to identify information indicative of the technology, artifacts, or noise associated with the communication;

an analysis component determining one or more frequency responses of one or more of the voice signals of the communication; and

a fraud component updating the voice library or the pathway library based on activity deemed to be fraudulent associated with the account, wherein the fraud component is operative to identify potential fraudulent activity by weighing the voice match determination more heavily than the frequency determination,

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

13. The system of claim 12, wherein the fraud component updates the voice library with one or more of the voice signals based on a match between one or more of the characteristics associated with the communication and one or more sets of the characteristics of the pathway library.

14. The system of claim 12, wherein the fraud component updates the voice library with one or more of the voice signals based on one or more frequency responses of one or more of the voice signals of the communication.

15. The system of claim 12, wherein the fraud component updates the pathway library with one or more characteristics of the communication based on a match between one or more of the voice signals and one or more of the voice samples of the voice library.

16. The system of claim 12, wherein the fraud component updates the pathway library with one or more characteristics of the communication based on one or more frequency responses of one or more of the voice signals of the communication.

17. The system of claim 12, wherein the fraud component updates a frequency library with one or more of the frequency responses associated with the individual.

18. The system of claim 12, monitoring component filters noise from one or more segments of the communication.