Systems and methods for optimizing transaction conversion rate using machine learning

1. A method for optimizing transaction authorization conversion rates using machine learning, comprising:

retrieving transaction parameters and authorization results for a plurality of past transactions from a database;

generating, via the machine learning, a transaction success model comprising authorization success factors for each of a plurality of transaction parameters based, at least in part, on processing one or more transactions in the database to tune the transaction success model and pre-validating the transaction success model by determining an improvement in transaction authorization conversion;

automatically calibrating optimization factors of the transaction success model through analysis of the authorization success factors, one or more transaction scenarios, and issuer negative results, the issuer negative results including one or more of an increase in the rate of chargebacks generated by the issuer or an increase in the rate of fraud alerts generated by the issuer;

receiving, at an acquirer processor, a transaction message from a point of sale (POS) device, wherein the POS device includes a memory coupled to a processor to configure the POS device to transmit the transaction message;

determining parameters of the transaction message, applying the transaction success model to the transaction parameters, and generating a modified transaction message by re-formatting, in real-time, the transaction message according to the generated transaction success model and the optimization factors; and

transmitting the modified transaction message to an issuer computer for processing, such that a transaction made corresponding to the modified transaction message is authorized by the issuer computer, wherein the transaction authorization result for the modified transaction message is stored in the database to tune the transaction success model.

2. The method of claim 1 , further comprising:

receiving the transaction authorization result for the transmitted modified transaction message; and

adding the received transaction authorization result and the parameters of the modified transaction message to a database of payment transaction processing results.

3. The method of claim 1 , wherein the modifying one or more parameters of the transaction message further comprises:

executing a machine learning application phase using the one or more parameters of the transaction message and the generated transaction success model; and

modifying each parameter of the transaction message where the machine learning application phase indicates a greater likelihood of authorization of the transaction message.

4. The method of claim 1 , wherein the transaction parameters comprise one or more of a billing address, a card verification value (CVV), a payment processing network, a payment vehicle expiration date, a payment vehicle issuer token, and a merchant classification code (MCC).

5. The method of claim 4 , wherein the authorization success factors include an absence of one or more of the billing address, the CVV, and the payment vehicle expiration date.

6. The method of claim 4 , wherein:

the POS device is associated with a plurality of MCCs, and

the authorization success factors indicate a likelihood of authorization of the transaction message associated with each MCC among the plurality of MCCs.

7. The method of claim 4 , wherein:

a plurality of payment networks are available for transmitting the modified transaction message for processing, and

the authorization success factors indicate a likelihood of authorization of the transaction message associated with each payment network among the plurality of payment networks.

8. The method of claim 4 , wherein the modifying one or more parameters of the transaction message comprises providing the payment vehicle issuer token.

9. A non-transitory computer readable medium storing a program causing a computer to execute a method of optimizing transaction authorization conversion rates using machine learning, the method comprising:

retrieving transaction parameters and authorization results for a plurality of past transactions from a database;

generating, via the machine learning, a transaction success model comprising authorization success factors for each of a plurality of transaction parameters based, at least in part, on processing one or more transactions in the database to tune the transaction success model and pre-validating the transaction success model by determining an improvement in transaction authorization conversion;

automatically calibrating optimization factors of the transaction success model through analysis of the authorization success factors, one or more transaction scenarios, and issuer negative results, the issuer negative results including one or more of an increase in the rate of chargebacks generated by the issuer or an increase in the rate of fraud alerts generated by the issuer;

receiving, at an acquirer processor, a transaction message from a point of sale (POS) device, wherein the POS device includes a memory coupled to a processor to configure the POS device to transmit the transaction message;

determining parameters of the transaction message, applying the transaction success model to the transaction parameters, and generating a modified transaction message by re-formatting, in real-time, the transaction message according to the generated transaction success model and the optimization factors; and

transmitting the modified transaction message to an issuer computer for processing, such that a transaction made corresponding to the modified transaction message is authorized by the issuer computer, wherein the transaction authorization result for the modified transaction message is stored in the database to tune the transaction success model.

10. The non-transitory computer readable medium of claim 9 , wherein the transaction parameters comprise one or more of a billing address, a card verification value (CVV), a payment processing network, a payment vehicle expiration date, a payment vehicle issuer token, and a merchant classification code (MCC).

11. The non-transitory computer readable medium of claim 10 , wherein the authorization success factors include an absence of one or more of the billing address, the CVV, and the payment vehicle expiration date.

12. The non-transitory computer readable medium of claim 10 , wherein:

the POS device is associated with a plurality of MCCs, and

the authorization success factors indicate a likelihood of authorization of the transaction message associated with each MCC among the plurality of MCCs.

13. The non-transitory computer readable medium of claim 10 , wherein:

a plurality of payment networks are available for transmitting the modified transaction message for processing, and

the authorization success factors indicate a likelihood of authorization of the transaction message associated with each payment network among the plurality of payment networks.

14. The non-transitory computer readable medium of claim 10 , wherein the modifying one or more parameters of the transaction message comprises providing the payment vehicle issuer token.

15. A computing system for optimizing transaction authorization conversion rates using machine learning, the computing system comprising a non-transitory computer readable medium having instructions stored thereon which when executed by a processor cause the processor to:

retrieve transaction parameters and authorization results for a plurality of past transactions from a database;

generate, via the machine learning, a transaction success model comprising authorization success factors for each of a plurality of transaction parameters based, at least in part, on processing one or more transactions in the database to tune the transaction success model and pre-validating the transaction success model by determining an improvement in transaction authorization conversion;

automatically calibrate optimization factors of the transaction success model through analysis of the authorization success factors, one or more transaction scenarios, and issuer negative results, the issuer negative results including one or more of an increase in the rate of chargebacks generated by the issuer or an increase in the rate of fraud alerts generated by the issuer;

receive, at an acquirer processor, a transaction message from a point of sale (POS) device, wherein the POS device includes a memory coupled to a processor to configure the POS device to transmit the transaction message;

determine parameters of the transaction message, apply the transaction success model to the transaction parameters, and generate a modified transaction message by re-formatting, in real-time, the transaction according to the generated transaction success model and the optimization factors; and

transmit the modified transaction message to an issuer computer for processing, such that a transaction made corresponding to the modified transaction message is authorized by the issuer computer, wherein the transaction authorization result for the modified transaction message is stored in the database to tune the transaction success model.

16. The system of claim 15 , wherein the transaction parameters comprise one or more of a billing address, a card verification value (CVV), a payment processing network, a payment vehicle expiration date, a payment vehicle issuer token, and a merchant classification code (MCC).

17. The system of claim 16 , wherein the authorization success factors include an absence of one or more of the billing address, the CVV, and the payment vehicle expiration date.

18. The system of claim 16 , wherein:

the POS device is associated with a plurality of MCCs, and

the authorization success factors indicate a likelihood of authorization of the transaction message associated with each MCC among the plurality of MCCs.

19. The system of claim 16 , wherein:

a plurality of payment networks are available for transmitting the modified transaction message for processing, and

the authorization success factors indicate a likelihood of authorization of the transaction message associated with each payment network among the plurality of payment networks.

20. The system of claim 16 , wherein the modifying one or more parameters of the transaction message comprises providing the payment vehicle issuer token.