SYSTEMS AND METHODS FOR OPTIMIZING TRANSACTION CONVERSION RATE USING MACHINE LEARNING
A method for optimizing transaction authorization conversion rates using machine learning includes retrieving payment transaction parameters and authorization results for a plurality of past payment transactions from a database, generating a transaction success model comprising authorization success factors for each of a plurality of payment transaction parameters using a machine learning training phase based on the retrieved payment transaction parameters and authorization results, receiving, at an acquirer processor, a payment transaction from a merchant, modifying one or more parameters of the payment transaction according to the generated transaction success model, and submitting the modified payment transaction to a financial institution for processing.
1 - 20 . (canceled)
21 . A computer-implemented method for optimizing authorization transaction conversion rates, comprising:
receiving a request for authorization of a transaction from a user device;
determining patterns of acceptance or denial of the request based, at least in part, on processing of transaction parameters and authorization results for a plurality of past transactions from a dataset;
inputting the determined patterns into a machine learning model to generate a transaction success model, wherein the transaction success model includes authorization success factors for a plurality of transactions;
applying, by the machine learning model, the transaction success model to the request and re-format one or more parameters associated with the request; and
transmitting the re-formatted request to a service provider for authorization of the transaction.
22 . The computer-implemented method of claim 21 , wherein generating the transaction success model, further comprises:
processing, by the machine learning model, the plurality of past transactions in the dataset to tune the transaction success model;
determining, by the machine learning model, the transaction success model provides an improvement in the transaction conversion rates; and
applying, by the machine learning model, the transaction success model to the parameters of the request based, at least in part, on the determination.
23 . The computer-implemented method of claim 21 , further comprising:
determining an inclusion, an exclusion, or an alteration of the one or more parameters associated with the request results in an improvement or a decrease in rate of transaction authorization.
24 . The computer-implemented method of claim 21 , further comprising:
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.
25 . The computer-implemented method of claim 21 , further comprising:
determining, by the machine learning model, transaction request with tokens, encrypted information, or a combination thereof improves probability of transaction authorization; and
re-formatting, by the machine learning model, the one or more parameters associated with the request to include a token, an encrypted authorization credentials, or a combination thereof.
26 . The computer-implemented method of claim 21 , further comprising:
determining, by the machine learning model, at least one network with a higher probability of transaction authorization; and
selecting, by the machine learning model, the at least one network for transmitting the re-formatted request to the service provider for transaction authorization.
27 . The computer-implemented method of claim 21 , further comprising:
receiving a transaction authorization result for the re-formatted request from the service provider; and
adding the transaction authorization result and the parameters of the re-formatted request to the dataset, wherein the transaction authorization result and the parameters are utilized for subsequent transactions.
28 . The computer-implemented method of claim 21 , wherein the one or more parameters associated with the request are re-formatted in batches, in a queue of the requests, in real-time, or asynchronously.
29 . The computer-implemented method of claim 21 , wherein the dataset includes an authorization result that indicates a specific combination of the transaction parameters that resulted in authorization of the request and a reason response code.
30 . The computer-implemented method of claim 21 , wherein the transaction parameters include 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).
31 . A system for optimizing authorization transaction conversion rates, comprising:
one or more processors; and
at least one non-transitory computer readable medium storing instructions which, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
receiving a request for authorization of a transaction from a user device;
determining patterns of acceptance or denial of the request based, at least in part, on processing of transaction parameters and authorization results for a plurality of past transactions from a dataset;
inputting the determined patterns into a machine learning model to generate a transaction success model, wherein the transaction success model includes authorization success factors for a plurality of transactions;
applying, by the machine learning model, the transaction success model to the request and re-format one or more parameters associated with the request; and
transmitting the re-formatted request to a service provider for authorization of the transaction.
32 . The system of claim 31 , wherein generating the transaction success model, further comprises:
processing, by the machine learning model, the plurality of past transactions in the dataset to tune the transaction success model;
determining, by the machine learning model, the transaction success model provides an improvement in the transaction conversion rates; and
applying, by the machine learning model, the transaction success model to the parameters of the request based, at least in part, on the determination.
33 . The system of claim 31 , further comprising:
determining an inclusion, an exclusion, or an alteration of the one or more parameters associated with the request results in an improvement or a decrease in rate of transaction authorization.
34 . The system of claim 31 , further comprising:
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.
35 . The system of claim 31 , further comprising:
determining, by the machine learning model, transaction request with tokens, encrypted information, or a combination thereof improves probability of transaction authorization; and
re-formatting, by the machine learning model, the one or more parameters associated with the request to include a token, an encrypted authorization credentials, or a combination thereof.
36 . The system of claim 31 , further comprising:
determining, by the machine learning model, at least one network with a higher probability of transaction authorization; and
selecting, by the machine learning model, the at least one network for transmitting the re-formatted request to the service provider for transaction authorization.
37 . The system of claim 31 , further comprising:
receiving a transaction authorization result for the re-formatted request from the service provider; and
adding the transaction authorization result and the parameters of the re-formatted request to the dataset, wherein the transaction authorization result and the parameters are utilized for subsequent transactions.
38 . A non-transitory computer readable medium for optimizing authorization transaction conversion rates, the non-transitory computer readable medium storing instructions which, when executed by one or more processors, cause the one or more processors to perform operations comprising:
receiving a request for authorization of a transaction from a user device;
determining patterns of acceptance or denial of the request based, at least in part, on processing of transaction parameters and authorization results for a plurality of past transactions from a dataset;
inputting the determined patterns into a machine learning model to generate a transaction success model, wherein the transaction success model includes authorization success factors for a plurality of transactions;
applying, by the machine learning model, the transaction success model to the request and re-format one or more parameters associated with the request; and
transmitting the re-formatted request to a service provider for authorization of the transaction.
39 . The non-transitory computer readable medium of claim 38 , wherein generating the transaction success model, further comprises:
processing, by the machine learning model, the plurality of past transactions in the dataset to tune the transaction success model;
determining, by the machine learning model, the transaction success model provides an improvement in the transaction conversion rates; and
applying, by the machine learning model, the transaction success model to the parameters of the request based, at least in part, on the determination.
40 . The non-transitory computer readable medium of claim 38 , further comprising:
determining an inclusion, an exclusion, or an alteration of the one or more parameters associated with the request results in an improvement or a decrease in rate of transaction authorization.