Systems and methods for restoring bus functionality
US-12181993-B1 · Dec 31, 2024 · US
US2024354771A1 · US · A1
| Field | Value |
|---|---|
| Publication number | US-2024354771-A1 |
| Application number | US-202418751642-A |
| Country | US |
| Kind code | A1 |
| Filing date | Jun 24, 2024 |
| Priority date | Dec 20, 2019 |
| Publication date | Oct 24, 2024 |
| Grant date | — |
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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.
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What is claimed is: 1 . A computer-implemented method comprising: receiving, by a screening microservice, 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; updating, by the screening microservice and based on the determined corrective action, 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 , further comprising: 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; and receiving, by the screening microservice from the first user device, a response indicating acceptance of the corrective action. 6 . 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. 7 . A computing device comprising: at least one processor; and memory storing instructions that, when executed by the at least one processor, cause the computing device to: receive, by a 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; update, by the screening microservice and based on the determined corrective action, 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. 8 . The computing device of claim 7 , 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. 9 . The computing device of claim 7 , 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. 10 . The computing device of claim 7 , 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. 11 . The computing device of claim 7 , 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. 12 . The computing device of claim 7 , wherein the instructions, when executed by the at least one processor, cause the computing device to: train the predictive model to identify payment transactions that have a likelihood
Transaction verification · CPC title
by exceeding a time limit, i.e. time-out, e.g. watchdogs · CPC title
in an input/output transactions management context (input/output processing in general G06F13/00) · CPC title
Updates performed during online database operations; commit processing · CPC title
at machine instruction level · CPC title
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