IP Library › Granted Patent US 12711513
Granted Patent B2
US 12711513 · App. 18/751,642 · Granted Aug 18, 2026

Transaction exchange platform with a validation microservice for validating transactions before being processed

Inventors: Nishant Srivastava (Glen Allen, VA); Eric K. Barnum (Midlothian, VA); Suresh Chander Ramaraj (Glen Allen, VA); Eric Smith (Henrico, VA); Earle Michael Lee (Midlothian, VA); Kumari Bhawprita (Glen Allen, VA)
Assignee: Capital One Services, LLC
G06Q20/425G06F11/0745G06F11/0757G06F11/1405G06F11/1474G06F16/2379G06Q20/085G06Q20/401
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Quick Facts
Patent No.
US 12711513
App. No.
18/751,642
Granted
Aug 18, 2026
Kind
B2
Abstract

Aspects described herein may relate to a transaction exchange platform using a streaming data platform (SDP) and microservices to process transactions according to review and approval workflows. The transaction exchange platform may receive transactions from origination sources, which may be added to the SDP as transaction objects. As the transactions are received, the transactions may be analyzed to detect duplicate transactions and/or errors in the transactions. The transaction exchange platform may take steps to remediate transactions that are recognized as duplicates or predicted to generate one or more errors. Similarly, the transaction exchange platform may take steps to remediate transactions that are rejected by a clearinghouse.

Claims (63)

1 . A computer-implemented method comprising:

receiving, by a screening microservice of a transaction exchange platform, a first transaction object corresponding to a first payment transaction, wherein the first transaction object comprises first transaction details;

determining, by the screening microservice and using a predictive model trained to identify one or more errors, whether the first transaction details indicate that processing of the first transaction object is likely to fail;

based on a determination that the first transaction details indicate a first likelihood that processing of the first transaction object is going to fail, determining a corrective action to reduce the first likelihood that processing of the first transaction object is going to fail;

sending, by the screening microservice to a first user device, an indication that processing of the first transaction object is likely to fail and the corrective action;

receiving, by the screening microservice from the first user device, a response indicating acceptance of the corrective action, wherein the corrective action is implemented based on receiving the response;

implementing, by a watchdog microservice of the transaction exchange platform, the corrective action by updating addenda data associated with the first transaction object; and

adding, by the screening microservice, the first transaction object with updated addenda data to a streaming data platform, wherein adding the first transaction object to the streaming data platform comprises setting a current workflow stage of the first transaction object to an initialization stage.

2 . The computer-implemented method of claim 1 , wherein the determination that the first transaction details indicate the first likelihood that processing of the first transaction object is going to fail further comprises:

determining, by the screening microservice, that a first account associated with a payor does not comprise sufficient funds to cover a first transaction associated with the first transaction object, wherein the corrective action comprises transferring funds from a second account to a first account.

3 . The computer-implemented method of claim 1 , wherein the determination that the first transaction details indicate the first likelihood that processing of the first transaction object is going to fail further comprises at least one of:

determining, by the screening microservice, that the first transaction details do not identify an account associated with a payor, wherein the corrective action comprises updating information associated with the account to correctly identify an account associated with the payor;

determining, by the screening microservice, that the first transaction details do not identify an account associated with a payee, wherein the corrective action comprises updating information associated with the account to correctly identify an account associated with the payee; or

determining, by the screening microservice, that the first transaction details do not comport with a first workflow for processing the first transaction object, wherein the corrective action comprises changing a workflow type of the first transaction object to a second workflow.

4 . The computer-implemented method of claim 1 , further comprising:

training the predictive model to identify payment transactions that have a likelihood of failing due to one or more errors, wherein a dataset for training the predictive model comprises:

transaction details associated with a plurality of payment transactions that failed; and

a reason each of the plurality of transactions failed.

5 . The computer-implemented method of claim 1 , wherein the predictive model comprises at least one of: k-means algorithm, affinity propagation algorithm, mean-shift algorithm, spectral clustering algorithm, Ward hierarchical clustering algorithm, agglomerative clustering algorithm, density-based spatial clustering of applications with noise (DBSCAN) algorithm, Gaussian mixtures algorithm, Birch algorithm, or shared nearest neighbors algorithm.

6 . A transaction exchange platform comprising:

a screening microservice configured to analyze transaction objects received by the transaction exchange platform;

a watchdog microservice configured to track transaction objects being processed by the transaction exchange platform;

at least one processor; and

memory storing instructions that, when executed by the at least one processor, cause the transaction exchange platform to:

receive, by the screening microservice, a first transaction object corresponding to a first payment transaction, wherein the first transaction object comprises first transaction details;

determine, by the screening microservice and using a predictive model trained to identify one or more errors, whether the first transaction details indicate that processing of the first transaction object is likely to fail;

based on a determination that the first transaction details indicate a first likelihood that processing of the first transaction object is going to fail, determine a corrective action to reduce the first likelihood that processing of the first transaction object is going to fail;

send, by the screening microservice to a first user device, an indication that processing of the first transaction object is likely to fail and the corrective action;

receive, by the screening microservice from the first user device, a response indicating acceptance of the corrective action, wherein the corrective action is implemented based on receiving the response

implement, by the watchdog microservice, the corrective action by updating addenda data associated with the first transaction object; and

add, by the screening microservice, the first transaction object with updated addenda data to a streaming data platform, wherein adding the first transaction object to the streaming data platform comprises setting a current workflow stage of the first transaction object to an initialization stage.

7 . The transaction exchange platform of claim 6 , wherein the instructions for determining that the first transaction details indicate the first likelihood that processing of the first transaction object is going to fail cause the transaction exchange platform to:

determine, by the screening microservice, that a first account associated with a payor does not comprise sufficient funds to cover a first transaction associated with the first transaction object, wherein the corrective action comprises transferring funds from a second account to a first account.

8 . The transaction exchange platform of claim 6 , wherein the instructions for determining that the first transaction details indicate the first likelihood that processing of the first transaction object is going to fail cause the transaction exchange platform to:

determine, by the screening microservice, that the first transaction details do not identify an account associated with a payor, wherein the corrective action comprises updating information associated with the account to correctly identify an account associated with the payor.

9 . The transaction exchange platform of claim 6 , wherein the instructions for determining that the first transaction details indicate the first likelihood that processing of the first transaction object is going to fail cause the transaction exchange platform to:

determine, by the screening microservice, that the first transaction details do not identify an account associated with a payee, wherein the corrective action comprises updating information associated with the account to correctly identify an account associated with the payee.

10 . The transaction exchange platform of claim 6 , wherein the instructions for determining that the first transaction details indicate the first likelihood that processing of the first transaction object is going to fail cause the transaction exchange platform to:

determine, by the screening microservice, that the first transaction details do not comport with a first workflow for processing the first transaction object, wherein the corrective action comprises changing a workflow type of the first transaction object to a second workflow.

11 . The transaction exchange platform of claim 6 , wherein the instructions, when executed by the at least one processor, cause a computing device to:

train the predictive model to identify payment transactions that have a likelihood of failing due to one or more errors, wherein a dataset for training the predictive model comprises:

transaction details associated with a plurality of payment transactions that failed; and

a reason each of the plurality of transactions failed.

12 . One or more non-transitory computer readable media comprising instructions that, when executed, cause a computing device to:

receive, by a screening microservice of a transaction exchange platform, a first transaction object corresponding to a first payment transaction, wherein the first transaction object comprises first transaction details;

determine, by the screening microservice and using a predictive model trained to identify one or more errors, whether the first transaction details indicate that processing of the first transaction object is likely to fail;

based on a determination that the first transaction details indicate a first likelihood that processing of the first transaction object is going to fail, determine a corrective action to reduce the first likelihood that processing of the first transaction object is going to fail;

send, by the screening microservice to a first user device, an indication that processing of the first transaction object is likely to fail and the corrective action;

receive, by the screening microservice from the first user device, a response indicating acceptance of the corrective action, wherein the corrective action is implemented based on receiving the response;

implementing, by a watchdog microservice of the transaction exchange platform, the corrective action by updating addenda data associated with the first transaction object; and

add, by the screening microservice, the first transaction object with updated addenda data to a streaming data platform, wherein adding the first transaction object to the streaming data platform comprises setting a current workflow stage of the first transaction object to an initialization stage.

13 . The one or more non-transitory computer readable media of claim 12 , wherein the instructions for determining that the first transaction details indicate the first likelihood that processing of the first transaction object is going to fail cause the computing device to:

determine, by the screening microservice, that a first account associated with a payor does not comprise sufficient funds to cover a first transaction associated with the first transaction object, wherein the corrective action comprises transferring funds from a second account to a first account.

14 . The one or more non-transitory computer readable media of claim 12 , wherein the instructions for determining that the first transaction details indicate the first likelihood that processing of the first transaction object is going to fail cause the computing device to:

determine, by the screening microservice, that the first transaction details do not identify an account associated with a payor, wherein the corrective action comprises updating information associated with the account to correctly identify an account associated with the payor.

15 . The one or more non-transitory computer readable media of claim 12 , wherein the instructions for determining that the first transaction details indicate the first likelihood that processing of the first transaction object is going to fail cause the computing device to:

determine, by the screening microservice, that the first transaction details do not identify an account associated with a payee, wherein the corrective action comprises updating information associated with the account to correctly identify an account associated with the payee.

16 . The one or more non-transitory computer readable media of claim 12 , wherein the instructions for determining that the first transaction details indicate the first likelihood that processing of the first transaction object is going to fail cause the computing device to:

determine, by the screening microservice, that the first transaction details do not comport with a first workflow for processing the first transaction object, wherein the corrective action comprises changing a workflow type of the first transaction object to a second workflow.

17 . The one or more non-transitory computer readable media of claim 12 , wherein the instructions, when executed cause the computing device to:

train the predictive model to identify payment transactions that have a likelihood of failing due to one or more errors, wherein a dataset for training the predictive model comprises:

transaction details associated with a plurality of payment transactions that failed; and

a reason each of the plurality of transactions failed.