Systems and methods for transaction acceptance modeling

1 . A system for generating an artificial intelligence model for predicting transaction acceptability, comprising:

a memory for storing organized data comprising merchant and transaction data;

at least one processor communicatively coupled to the memory and configured to:

receive at least one payfile input;

generate artificial intelligence model training data by creating a data package comprising supplier data enriched with card source data and supplier industry data, as well as supplier outreach results data from the at least one payfile input;

set a plurality of model weights based on a plurality of connections between a plurality of hidden layers in the artificial intelligence model;

analyze, via the artificial intelligence model, the artificial intelligence model training data with the plurality of model weights;

adjust the plurality of model weights based on an output of the analysis;

generate a finalized artificial intelligence model based on the output of the analysis and adjusted plurality of model weights;

generate at least one transaction acceptability prediction with the finalized artificial intelligence model; and

update the at least one transaction acceptability prediction without refitting the finalized artificial intelligence model by:

generating a prior transaction acceptability score using the finalized artificial intelligence model,

collecting updated supplier transaction acceptance information, and

combining the prior transaction acceptability score with the updated supplier transaction acceptance information using Bayesian updating to generate an updated transaction acceptability score.

2 . The system of claim 1 , wherein the generated transaction acceptability prediction is updated in real-time based on an updated payfile input.

3 . The system of claim 1 , wherein the transaction acceptability prediction is generated for a supplier without historical transaction data.

4 . The system of claim 1 , wherein the acceptability prediction is generated for an individual transaction.

5 . The system of claim 1 , wherein the at least one payfile input is converted into a standardized format.

6 . The system of claim 1 , wherein the supplier outreach results data comprises results of one or more of (1) a prior attempt to determine a supplier transaction acceptability and (2) a prior payment attempt.

7 . The system of claim 1 , wherein the card source data comprises one or more of (1) records of supplier transactions, (2) records of historical transactions between a supplier and a buyer, and (3) one or more unique values used to categorize the supplier data.

8 . The system of claim 1 , wherein the supplier industry data comprises a North American Industry Classification System code.

9 . A method for generating an artificial intelligence model for predicting transaction acceptability, comprising:

receiving, via a processor, at least one payfile input;

generating, via the processor, artificial intelligence model training data by creating a data package comprising supplier data enriched with card source data and supplier industry data, as well as supplier outreach results data from the at least one payfile input;

setting, via the processor, a plurality of model weights based on a plurality of connections between a plurality of hidden layers in the artificial intelligence model;

analyzing, via the artificial intelligence model, the artificial intelligence model training data with the plurality of model weights;

adjusting, via the processor, the plurality of model weights based on an output of the analysis;

generating, via the processor, a finalized artificial intelligence model based on the output of the analysis and adjusted plurality of model weights;

generating, via the processor, at least one transaction acceptability prediction with the finalized artificial intelligence model; and

updating, via the processor, the at least one transaction acceptability prediction without refitting the finalized artificial intelligence model by:

generating a prior transaction acceptability score using the finalized artificial intelligence model,

collecting updated supplier transaction acceptance information, and

combining the prior transaction acceptability score with the updated supplier transaction acceptance information using Bayesian updating to generate an updated transaction acceptability score.

10 . The method of claim 9 , wherein the generated transaction acceptability prediction is updated in real-time based on an updated payfile input.

11 . The method of claim 9 , wherein the transaction acceptability prediction is generated for a supplier without historical transaction data.

12 . The method of claim 9 , wherein the acceptability prediction is generated for an individual transaction.

13 . The method of claim 9 , wherein the at least one payfile input is converted into a standardized format.

14 . The method of claim 9 , wherein the supplier outreach results data comprises results of one or more of (1) a prior attempt to determine a supplier transaction acceptability and (2) a prior payment attempt.

15 . The method of claim 9 , wherein the card source data comprises one or more of (1) records of supplier transactions, (2) records of historical transactions between a supplier and a buyer, and (3) one or more unique values used to categorize the supplier data.

16 . The method of claim 9 , wherein the supplier industry data comprises a North American Industry Classification System code.

17 . A non-transitory computer-accessible medium having stored thereon computer-executable instructions wherein, when a computer hardware arrangement executes the instructions, the computer hardware arrangement is configured to perform procedures comprising:

receiving, via a processor, at least one payfile input;

generating, via the processor, artificial intelligence model training data by creating a data package comprising supplier data enriched with card source data and supplier industry data, as well as supplier outreach results data from the at least one payfile input;

setting, via the processor, a plurality of model weights based on a plurality of connections between a plurality of hidden layers in the artificial intelligence model;

analyzing, via an artificial intelligence model, the artificial intelligence model training data with the plurality of model weights;

adjusting, via the processor, the plurality of model weights based on an output of the analysis;

generating, via the processor, a finalized artificial intelligence model based on the output of the analysis and adjusted plurality of model weights;

generating, via the processor, at least one transaction acceptability prediction with the finalized artificial intelligence model; and

updating, via the processor, the at least one transaction acceptability prediction without refitting the finalized artificial intelligence model by:

generating a prior transaction acceptability score using the finalized artificial intelligence model,

collecting updated supplier transaction acceptance information, and

combining the prior transaction acceptability score with the updated supplier transaction acceptance information using Bayesian updating to generate an updated transaction acceptability score.

18 . The non-transitory computer-accessible medium of claim 17 , wherein the generated transaction acceptability prediction is updated in real-time based on an updated payfile input.

19 . The non-transitory computer-accessible medium of claim 17 , wherein the transaction acceptability prediction is generated for a supplier without historical transaction data.

20 . The non-transitory computer-accessible medium of claim 17 , wherein the acceptability prediction is generated for an individual transaction.