Data reconciliation based on computer analysis of data

• US 10,949,916 B2
• Filed: 07/03/2018
• Issued: 03/16/2021
• Est. Priority Date: 07/06/2017
• Status: Active Grant

• Current Assignee: Xero Limited
• Original Assignee: Xero Limited
• Inventors: Gorman, Kieran, Walker, Craig
• Primary Examiner(s): Campen, Kelly S.
• Application Number:
  US16/027,109
• Publication Number:
  US 20190012733A1
• Time in Patent Office: 987 Days
• Field of Search: 705 35, 705 38, 705 39, 705 44, 706 12
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes
• CPC Class Codes: G06Q 40/02 Banking, e.g. interest calculation or account maintenance credit or loans G06Q40/03
• CPC Class Codes: G06Q 40/12 Accounting

Abstract

Methods, systems, and computer programs are presented for reconciling a transaction against data in a database to identify the transaction parameters based on text descriptors provided for the transaction. One method includes an operation for identifying features for reconciling transactions of a first entity by a machine-learning program. The features include, at least, a description of the transaction, a name of a second entity in the transaction, a location of the second entity, and an account for the transaction. The machine-learning program is trained with training data that includes values of the features for previously reconciled transactions. A received first transaction includes a description, a date, and an amount. The first transaction is input for the machine-learning program, which generates one or more suggestions for reconciling the first transaction. Each suggestion includes the name of the second entity in the first transaction and an account.

Claims

1. A method comprising:

identifying features for reconciling transactions, in a bank statement of a first entity, by two machine-learning programs that comprise a community predictor and a user predictor, the features comprising a description of the transaction, a name of a second entity associated with the transaction, and an account in an accounting system;

training, by one or more processors, the community predictor with training data, the training data comprising values of the features for previously reconciled transactions of a plurality of businesses that match transactions in bank statements to corresponding accounts in the accounting system, the community predictor being for determining reconciliation suggestions based on the previously reconciled transactions for the plurality of businesses;

training, by the one or more processors, the user predictor with training data comprising values of the features for previously reconciled transactions of the first entity, the user predictor being for determining reconciliation suggestions based on the previously reconciled transactions of the first entity;

receiving, by the one or more processors, a first transaction in the bank statement, the first transaction relating to a user and including a description, a date, and an amount;

inputting, by the one or more processors, the first transaction to the community predictor and the user predictor;

determining, by the community predictor, one or more first suggestions for reconciling the first transaction and a first confidence level corresponding to each of the one or more first suggestions;

determining, by the user predictor, one or more second suggestions for reconciling the first transaction and a second confidence level corresponding to each of the one or more second suggestions;

determining a best suggestion corresponding to a highest confidence level of the first confidence levels and second confidence levels of the one or more first suggestions and second suggestions;

responsive to the highest confidence level of the best suggestion not exceeding a predetermined first threshold, causing, by the one or more processors;

presentation in a user interface one or more suggestions from the one or more first suggestions and second suggestions, each suggestion comprising the name of the second entity in the first transaction and an account in the accounting system associated with the first transaction;

receiving through the user interface, a user designation reconciliation suggestion from the one or more suggestions for manually reconciling the first transaction;

reconciling the first transaction with the user designation reconciliation suggestion; and

re-training, by the one or more processors, the user predictor with training data comprising the first transaction and the user designated reconciliation suggestion; and

responsive to the confidence level of the best suggestion exceeding the predetermined first threshold, automatically reconciling the first transaction with the determined best suggestion.

2. The method as recited in claim 1, wherein the features for reconciling transactions by the two machine-learning programs further comprise a location of the first entity, an industry of the first entity, and a location of the second entity.

3. The method as recited in claim 1, wherein the features for reconciling transactions by the two machine-learning programs further comprise a tax rate of the transaction, an account identifier, an account name associated with the account identifier, an invoice identifier, and a bill identifier.

4. The method as recited in claim 1, wherein receiving the first transaction further comprises:

receiving the bank statement with one or more transactions; and

extracting the one or more transactions from the bank statement, wherein each extracted transaction is reconciled utilizing the two machine-learning programs.

5. The method as recited in claim 1, wherein the user interface comprises a first region with information about the first transaction and a second region for the one or more suggestions, the second region including a name of the second entity, an account, a bill identifier, a confirmation selector, and a manual selector for requesting manual reconciliation.

6. The method as recited in claim 1, wherein the training data is segmented for a plurality of geographical regions, wherein the two machine-learning programs reconcile transactions for one of the geographical regions.

7. The method as recited in claim 1, wherein the training data comprises data obtained from a plurality of entities, wherein entities without a history of reconciled transactions are provided with suggestions for reconciling transactions.

8. The method of claim 1 further comprising,

re-training, by the one or more processors, the community predictor with training data comprising values of the features for previously reconciled transactions of a plurality of businesses that match transactions in bank statements to corresponding accounts in the accounting system.

9. A system comprising:

a memory comprising instructions; and

one or more computer processors, wherein the instructions, when executed by the one or more computer processors, cause the one or more computer processors to perform operations comprising;

identifying features for reconciling transactions, in a bank statement of a first entity by two machine-learning programs that comprise a community predictor and a user predictor, the features comprising a description of the transaction, a name of a second entity associated with the transaction, and an account in an accounting system;

training, by one or more processors, the community predictor with training data, the training data comprising values of the features for previously reconciled transactions of a plurality of businesses that match transactions in bank statements to corresponding accounts in the accounting system, the community predictor being for determining reconciliation suggestions based on the previously reconciled transactions for the plurality of businesses;

training, by the one or more processors, the user predictor with training data comprising values of the features for previously reconciled transactions of the first entity, the user predictor being for determining reconciliation suggestions based on the previously reconciled transactions of the first entity;

receiving a first transaction in the bank statement, the first transaction relating to a user and including a description, a date, and an amount;

inputting the first transaction to the community predictor and the user predictor;

determining, by the community predictor, one or more first suggestions for reconciling the first transaction and a first confidence level corresponding to each of the one or more first suggestions;

determining, by the user predictor, one or more second suggestions for reconciling the first transaction and a second confidence level corresponding to each of the one or more second suggestions;

determining a best suggestion corresponding to a highest confidence level of the first confidence levels and second confidence levels of the one or more first suggestions and second suggestions;

responsive to the highest confidence level of the best suggestion not exceeding a predetermined first threshold, causing;

presentation in a user interface one or more suggestions from the one or more first suggestions and second suggestions, each suggestion comprising the name of the second entity in the first transaction and an account in the accounting system associated with the first transaction;

receiving through the user interface, a user designation reconciliation suggestion from the one or more suggestions for manually reconciling the first transaction;

reconciling the first transaction with the user designation reconciliation suggestion; and

re-training, by the one or more processors, the user predictor with training data comprising the first transaction and the user designated reconciliation suggestion; and

responsive to the confidence level of the best suggestion exceeding the predetermined first threshold, automatically reconciling the first transaction with the determined best suggestion.

10. The system as recited in claim 9, wherein the features for reconciling transactions by the two machine-learning programs further comprise a location of the first entity, an industry of the first entity, a location of the second entity, a tax rate of the transaction, an account identifier, an account name associated with the account identifier, an invoice identifier, and a bill identifier.

11. The system as recited in claim 9, wherein receiving the first transaction further comprises:

receiving the bank statement with one or more transactions; and

extracting the one or more transactions from the bank statement, wherein each extracted transaction is reconciled utilizing the two machine-learning programs.

12. The system of claim 9 wherein the instructions, when executed by the one or more computer processors, cause the one or more computer processors to further:

re-training, by the one or more processors, the community predictor with training data comprising values of the features for previously reconciled transactions of a plurality of businesses that match transactions in bank statements to corresponding accounts in the accounting system.

13. A non-transitory machine-readable storage medium including instructions that, when executed by a machine, cause the machine to perform operations comprising:

identifying features for reconciling transactions, in a bank statement of a first entity, by two machine-learning programs that comprise a community predictor and a user predictor, the features comprising a description of the transaction, a name of a second entity associated with the transaction, and an account in an accounting system;

training, by one or more processors, the community predictor with training data, the training data comprising values of the features for previously reconciled transactions of a plurality of businesses that match transactions in bank statements to corresponding accounts in the accounting system, the community predictor being for determining reconciliation suggestions based on the previously reconciled transactions for the plurality of businesses;

training, by the one or more processors, the user predictor with training data comprising values of the features for previously reconciled transactions of the first entity, the user predictor being for determining reconciliation suggestions based on the previously reconciled transactions of the first entity;

receiving a first transaction in the bank statement, the first transaction relating to a user and including a description, a date, and an amount;

inputting the first transaction to the community predictor and the user predictor;

determining, by the community predictor, one or more first suggestions for reconciling the first transaction and a first confidence level corresponding to each of the one or more first suggestions;

determining, by the user predictor, one or more second suggestions for reconciling the first transaction and a second confidence level corresponding to each of the one or more second suggestions;

determining a best suggestion corresponding to a highest confidence level of the first confidence levels and second confidence levels of the one or more first suggestions and second suggestions;

responsive to the highest confidence level of the best suggestion not exceeding a predetermined first threshold, causing;

presentation in a user interface one or more suggestions from the one or more first suggestions and second suggestions, each suggestion comprising the name of the second entity in the first transaction and an account in the accounting system associated with the first transaction;

receiving through the user interface, a user designation reconciliation suggestion from the one or more suggestions for manually reconciling the first transaction;

reconciling the first transaction with the user designation reconciliation suggestion; and

re-training, by the one or more processors, the user predictor with training data comprising the first transaction and the user designated reconciliation suggestion; and

responsive to the confidence level of the best suggestion exceeding the predetermined first threshold, automatically reconciling the first transaction with the determined best suggestion.

14. The non-transitory machine-readable storage medium as recited in claim 13, wherein the features for reconciling transactions by the two machine-learning programs further comprise a location of the first entity, an industry of the first entity, a location of the second entity, a tax rate of the transaction, an account identifier, an account name associated with the account identifier, an invoice identifier, and a bill identifier.

15. The non-transitory machine-readable storage medium as recited in claim 13, wherein receiving the first transaction further comprises:

receiving the bank statement with one or more transactions; and

extracting the one or more transactions from the bank statement, wherein each extracted transaction is reconciled utilizing the two machine-learning programs.

16. The non-transitory machine-readable storage medium as recited in claim 13, wherein instructions that, when executed by a machine, cause the machine to further:

re-training, by the one or more processors, the community predictor with training data comprising values of the features for previously reconciled transactions of a plurality of businesses that match transactions in bank statements to corresponding accounts in the accounting system.