System and method for mobile wallet remittance

1. A method comprising:

receiving, from an electronic device associated with a sender, a request, wherein the request includes a current geographic location obtained from a GPS sensor of the electronic device and transaction information that identifies a recipient;

determining whether the current geographic location of the electronic device is within a pre-designated area;

generating an indication of fraud based on the determination;

transmitting the indication of fraud and the request to a plurality of providers;

receiving information associated with performance of the request from the plurality of providers;

identifying a portion of the plurality of providers based on a comparison of the received information from the plurality of providers and predefined criteria associated with both the sender and the recipient to a threshold;

transmitting, to the electronic device, a list of the portion of the plurality of providers; and

notifying a provider to perform the request, when an indication is received that the provider is selected from the list.

2. The method of claim 1 , further comprising:

negotiating remittance information with the plurality of providers based on the transaction information; and

providing a suggested provider to perform the request, determined from the predefined criteria associated with the sender, the current geographic location of the electronic device, a location of the recipient, the negotiated remittance information, and the transaction information.

3. The method of claim 1 , further comprising authenticating the sender that is associated with the request based on biometric information that is included in the request.

4. The method of claim 1 , further comprising:

generating a fraud score associated with the sender; and

transmitting the fraud score with the indication of fraud to the plurality of providers,

wherein the fraud score is based on a contextual analysis, a deep learning analysis, and a social media analysis.

5. The method of claim 4 , further comprising:

denying the request when the fraud score is above another threshold; and

authenticating the request when the fraud score is below the other threshold.

6. The method of claim 2 , wherein:

the plurality of providers is within a market of providers; and

the request further includes an amount of money to remit to the recipient and a requested delivery speed, and

negotiating the remittance information with the plurality of providers, further comprises receiving from the plurality of providers, a fee associated with the request, an exchange rate associated with the request, and available delivery speeds.

7. The method of claim 1 , further comprising:

performing a deep learning on the sender to identify a potential for fraudulent activity, wherein the deep learning comprises:

analyzing behavior data of the sender, historical data of the sender, social media data of the sender, and contextual data of the sender;

responsive to analyzing the behavior data, the historical data, the social media data, and the contextual data of the sender, generating a financial transaction ontology of the sender to predict a set of financial parameters associated with the request; and

identifying one or more activities of the sender that deviates from the predicted set of financial parameters.

8. The method of claim 1 , further comprising:

predicting a future request, wherein the prediction includes another recipient, an amount of money, and a date of the future request, and

wherein the prediction is based on structured and un-structured data.

9. An electronic device comprising:

a communication interface;

a memory; and

at least one processor coupled to the communication interface and the memory, the at least one processor is configured to:

receive, from a second electronic device that is associated with a sender, a request, wherein the request includes a current geographic location obtained from a GPS sensor of the second electronic device and transaction information that identifies a recipient;

determine whether the current geographic location of the electronic device is within a pre-designated area;

generate an indication of fraud based on the determination;

transmit the indication of fraud and the request to a plurality of providers;

receive information associated with performance of the request from the plurality of providers;

identify a portion of the plurality of providers based on a comparison of the received information from the plurality of providers and predefined criteria associated with both the sender and the recipient to a threshold;

transmitting, to the second electronic device, a list of the portion of the plurality of providers; and

notify a provider to perform the request, when an indication is received that the provider is selected from the list.

10. The electronic device of claim 9 , wherein the at least one processor is further configured to:

negotiate remittance information with the plurality of providers based on the transaction information; and

provide a suggested provider to perform the request, determined from the predefined criteria associated with the sender, the current geographic location of the second electronic device, a location of the recipient, the negotiated remittance information, and the transaction information.

11. The electronic device of claim 9 , wherein the at least one processor is further configured to authenticate the sender that is associated with the request based on biometric information that is included in the request.

12. The electronic device of claim 9 , wherein the at least one processor is further configured to:

generate a fraud score associated with the sender; and

transmit the fraud score with the indication of fraud to the plurality of providers,

wherein the fraud score is based on a contextual analysis, a deep learning analysis, and a social media analysis.

13. The electronic device of claim 10 , wherein:

the plurality of providers is within a market of providers; and

the request further includes an amount of money to remit to the recipient, and a requested delivery speed, and

to negotiate the remittance information with the plurality of providers, the at least one processor is further configured to receive from the plurality of providers, a fee associated with the request, an exchange rate associated with the request, and available delivery speeds.

14. The electronic device of claim 9 , wherein the at least one processor is further configured to:

perform a deep learning on the sender to identify a potential for fraudulent activity, wherein to perform the deep learning the at least one processor is further configured to:

analyze behavior data of the sender, historical data of the sender, social media data of the sender, and contextual data of the sender;

responsive to analyzing the behavior data, the historical data, the social media data, and the contextual data of the sender, generate a financial transaction ontology of the sender to predict a set of financial parameters associated with the request; and

identify one or more activities of the sender that deviates from the predicted set of financial parameters.

15. The electronic device of claim 9 , wherein the at least one processor is further configured to:

predict a future request, wherein the prediction includes another recipient, an amount of money, and a date of the future request, and

wherein the prediction is based on structured and un-structured data.

16. A non-transitory computer readable medium embodying a computer program, the computer program comprising program code that when executed by at least one processor of an electronic device causes the at least one processor to:

receive, from a second electronic device that is associated with a sender, a request, wherein the request includes a current geographic location obtained from a GPS sensor of the second electronic device and transaction information that identifies a recipient;

determine whether the current geographic location of the electronic device is within a pre-designated area;

generate an indication of fraud based on the determination;

transmit the indication of fraud and the request to a plurality of providers;

receive information associated with performance of the request from the plurality of providers;

identify a portion of the plurality of providers based on a comparison of the received information associated from the plurality of providers and predefined criteria associated with both the sender and the recipient to a threshold;

transmitting, to the second electronic device, a list of the portion of the plurality of providers; and

notify a provider to perform the request, when an indication is received that the provider is selected from the list.

17. The non-transitory computer readable medium of claim 16 , further comprising program code that when executed by the at least one processor of the electronic device, causes the at least one processor to:

negotiate remittance information with the plurality of providers based on the transaction information; and

provide a suggested provider to perform the request, determined from the predefined criteria associated with the sender, the current geographic location of the second electronic device, a location of the recipient, the negotiated remittance information, and the transaction information.

18. The non-transitory computer readable medium of claim 16 , further comprising program code that, when executed by the at least one processor of the electronic device, causes the at least one processor to authenticate the sender that is associated with the request based on biometric information that is included in the request.

19. The non-transitory computer readable medium of claim 16 , further comprising program code that when executed by the at least one processor of the electronic device, causes the at least one processor to:

generate a fraud score associated with the sender; and

transmit the fraud score with the indication of fraud to the plurality of providers,

wherein the fraud score is based on a contextual analysis, a deep learning analysis, and a social media analysis.

20. The non-transitory computer readable medium of claim 16 , further comprising program code that when executed by the at least one processor of the electronic device, causes the at least one processor to:

perform a deep learning on the sender to identify a potential for fraudulent activity, wherein to perform the deep learning the program code that when executed by the at least one processor causes, the at least one processor to:

analyze behavior data of the sender, historical data of the sender, social media data of the sender, and contextual data of the sender;

responsive to analyzing the behavior data, the historical data, the social media data, and the contextual data of the sender, generate a financial transaction ontology of the sender to predict a set of financial parameters associated with the request; and

identify one or more activities of the sender that deviates from the predicted set of financial parameters.