Systems and methods for early fraud detection

1. A computer implemented method for routing a call received at a call centre based on one or more characteristics of call data, the method comprising:

receiving or retrieving a first data set associated with a first set of call features relating to an on-going call, the first data set including: data representing a caller identifier associated with the on-going call, a time the on-going call was received at the call centre, and account identifying information associated with an electronic account;

generating, using a machine learning model, a suspiciousness score of the on-going call based on a call history of the caller identifier with the call centre, an attempted access history for the electronic account, and an enumerated time period in which the time the on-going call was received at the call centre, the suspiciousness score indicating a probability of the on-going call being a fraudulent call;

routing the on-going call based on the suspiciousness score;

displaying the first suspiciousness score on a graphical user interface;

receiving or retrieving a second data set associated with the first set or additional call features relating to the on-going call;

updating the suspiciousness score of the on-going call based on the second data set; and

displaying the updated suspiciousness score on the graphical user interface.

2. The method of claim 1 , wherein the first set of call features relating to the on-going call includes one or more historical call features regarding the on-going call.

3. The method of claim 2 , wherein the one or more historical call features include one or more of: a telephone number, a client account number, a number of call times associated with the telephone number in a time period, a number of client account numbers associated with the telephone number in a time period, a number of distinct telephone numbers associated with the client account number, a client segment associated with the client account number, a number of authentication failures associated with the telephone number or the client account number in a time period, a number of hung-ups associated with the telephone number or the client number in a time period, an average past call duration associated with the telephone number or the client number, a number of times the client account number is accessed in a time period, and a validity of the telephone number.

4. The method of claim 3 , wherein the one or more historical call features are retrieved based on a client ID relating to the on-going call.

5. The method of claim 4 , wherein the client ID comprises a telephone number or a client account number.

6. The method of claim 1 , comprising training the machine learning model by:

receiving one or more training data sets of call data having one or more labelled fraudulent calls interspersed within a plurality of non-fraudulent calls; and

using a machine learning device, extracting one or more feature templates from the one or more training data sets representing features indicative of a fraudulent call.

7. The method of claim 1 , wherein the second data set is received or retrieved in real time or near real time and collected on a iterative basis.

8. The method of claim 1 , wherein the first set of call features and the second set of call features contain one or more identical call features relating to the on-going call, and the one or more identical call features are iteratively updated during the on-going call.

9. The method of claim 1 , comprising upon the suspiciousness score breaching a predefined limit, flagging or terminating the on-going call.

10. The method of claim 1 , comprising rendering the suspiciousness score as an interactive interface element on the graphical user interface.

11. The method of claim 1 , wherein the first or second data set includes both voice data and interactive voice response system (IVR) data.

12. The method of claim 1 , wherein routing the on-going call comprises: parking the on-going call in a queue, forwarding the on-going call to a human agent, forwarding the on-going call to a machine; removing the on-going call from a queue; or terminating the on-going call.

13. The method of claim 12 , wherein routing the on-going call based on the suspiciousness score comprises routing the on-going call when the suspiciousness score is above or beneath a pre-determined threshold.

14. The method of claim 13 , comprising routing the on-going call to a human agent when the suspiciousness score is beneath the pre-determined threshold.

15. The method of claim 1 , comprising routing the on-going call based on the updated suspiciousness score.

16. The method of claim 1 , wherein the attempted access history for the electronic account includes data representing attempted access to the electronic account via an internet web server.

17. A system for routing a call based on one or more characteristics of call data, the system comprising a memory device having machine-readable code stored thereon, and a processor configured to, upon executing the machine-readable code:

receive or retrieve a first data set associated with a first set of call features relating to an on-going call, the first data set including: data representing a caller identifier associated with the on-going call, a time the on-going call was received at the call centre, and account identifying information associated with an electronic account;

generate, using a machine learning model, a suspiciousness score of the on-going call based on a call history of the caller identifier with the call centre, an attempted access history for the electronic account, and an enumerated time period in which the time the on-going call was received, the suspiciousness score indicating a probability of the on-going call being a fraudulent call;

route the on-going call based on the suspiciousness score;

render a graphical user interface including the first suspiciousness score;

receive or retrieve a second data set associated with the first set or additional call features relating to the on-going call;

update the suspiciousness score of the on-going call based on the second data set; and

display the updated suspiciousness score on the graphical user interface.

18. The system of claim 17 , wherein the first set of call features relating to the on-going call includes one or more historical call features regarding the on-going call.

19. The system of claim 18 , wherein the one or more historical call features include one or more of: a telephone number, a client account number, a number of call times associated with the telephone number in a time period, a number of client account numbers associated with the telephone number in a time period, a number of distinct telephone numbers associated with the client account number, a client segment associated with the client account number, a number of authentication failures associated with the telephone number or the client account number in a time period, a number of hung-ups associated with the telephone number or the client number in a time period, an average past call duration associated with the telephone number or the client number, a number of times the client account number is accessed in a time period, and a validity of the telephone number.

20. The system of claim 19 , wherein the one or more historical call features are retrieved based on a client ID relating to the on-going call.

21. The system of claim 17 , wherein the processor is configured to train the machine learning model by:

receiving one or more training data sets of call data having one or more labelled fraudulent calls interspersed within a plurality of non-fraudulent calls; and

using a machine learning device, extracting one or more feature templates from the one or more training data sets representing features indicative of a fraudulent call.

22. The system of claim 17 , wherein the second data set is received or retrieved in real time or near real time and collected on a iterative basis.

23. The system of claim 17 , wherein the first set of call features and the second set of call features contain one or more identical call features relating to the on-going call, and the one or more identical call features are iteratively updated during the on-going call.

24. The system of claim 17 , comprising upon the suspiciousness score breaching a predefined limit, flagging or terminating the on-going call.

25. The system of claim 17 , comprising rendering the suspiciousness score as an interactive interface element on the graphical user interface.

26. The system of claim 17 , wherein the first or second data set includes both voice data and interactive voice response system data.

27. The system of claim 17 , wherein routing the on-going call comprises: parking the on-going call in a queue, forwarding the on-going call to a human agent, forwarding the on-going call to a machine; removing the on-going call from a queue; or terminating the on-going call.

28. The system of claim 27 , wherein routing the on-going call based on the suspiciousness score comprises routing the on-going call when the suspiciousness score is above or beneath a pre-determined threshold.

29. The system of claim 28 , wherein the processor is configured to route the on-going call to a human agent when the suspiciousness score is beneath the pre-determined threshold.

30. The system of claim 17 , wherein the processor is configured to route the on-going call based on the updated suspiciousness score.