DATA RECONCILIATION BASED ON COMPUTER ANALYSIS OF DATA

• US 20190012733A1
• Filed: 07/03/2018
• Published: 01/10/2019
• Est. Priority Date: 07/06/2017
• Status: Active Grant

• Current Assignee: Xero Limited
• Original Assignee: Xero Limited
• Inventors: Gorman, Kieran, Walker, Craig
• Granted Patent: US 10,949,916 B2
• Time in Patent Office: Days
• 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 of a first entity by a machine-learning program, the features comprising a description of the transaction, a name of a second entity associated with the transaction, and an account associated with the transaction;

training, by one or more processors, the machine-learning program with training data, the training data comprising values of the features for previously reconciled transactions;

receiving, by the one or more processors, a first transaction that includes a description, a date, and an amount; and

inputting, by the one or more processors, the first transaction to the machine-learning program, the machine-learning program generating one or more suggestions for reconciling the first transaction, each suggestion comprising the name of the second entity in the first transaction and an account associated with the first transaction.

2. The method as recited in claim 1, wherein the features for reconciling transactions 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 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 a statement with one or more transactions; and

extracting the one or more transactions, wherein each extracted transaction is reconciled utilizing the machine-learning program.

5. The method as recited in claim 1, wherein the machine-learning program identifies a score for each of the one or more suggestions, the method further comprising:

for the suggestion with a highest score, checking if the score is above a first predetermined threshold; and

automatically reconciling the first transaction with the suggestion with the highest score when the highest score is above the first predetermined threshold.

6. The method as recited in claim 5, further comprising:

when the highest score is not above the first predetermined threshold, checking if the highest score is above a second predetermined threshold;

presenting a user interface for manually reconciling the first transaction with any of the one or more suggestions when the highest score is above the second predetermined threshold; and

when the highest score is below or equal the second predetermined threshold, requesting manual reconciliation for the first transaction in the user interface without presenting any suggestion.

7. The method as recited in claim 6, wherein the user interface comprises a first region with information about the first transaction and a second region for the suggestion, 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.

8. The method as recited in claim 1, wherein the training data is segmented for a plurality of geographical regions, wherein the machine-learning program reconciles transactions for one of the geographical regions.

9. 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.

10. The method as recited in claim 9, further comprising:

training a custom machine-learning program with transactions previously reconciled by the first entity, the custom machine-learning program providing suggestions for the first entity based on the transactions previously reconciled by the first entity;

generating one or more custom suggestions for the first transaction by the custom machine-learning program; and

selecting a best suggestion for presentation to a user based on the suggestions and the custom suggestions.

11. 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 of a first entity by a machine-learning program, the features comprising a description of the transaction, a name of a second entity associated with the transaction, and an account associated with the transaction;

training the machine-learning program with training data, the training data comprising values of the features for previously reconciled transactions;

receiving a first transaction that includes a description, a date, and an amount; and

inputting the first transaction to the machine-learning program, the machine-learning program generating one or more suggestions for reconciling the first transaction, each suggestion comprising the name of the second entity in the first transaction and an account associated with the first transaction.

12. The system as recited in claim 11, wherein the features for reconciling transactions 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.

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

receiving a statement with one or more transactions; and

extracting the one or more transactions, wherein each extracted transaction is reconciled utilizing the machine-learning program.

14. The system as recited in claim 11, wherein the machine-learning program identifies a score for each of the one or more suggestions, wherein the instructions further cause the one or more computer processors to perform operations comprising:

for the suggestion with a highest score, checking if the score is above a first predetermined threshold; and

automatically reconciling the first transaction with the suggestion with the highest score when the highest score is above the first predetermined threshold.

15. The system as recited in claim 14, wherein the instructions further cause the one or more computer processors to perform operations comprising:

when the highest score is not above the first predetermined threshold, checking if the highest score is above a second predetermined threshold;

presenting a user interface for manually reconciling the first transaction with any of the one or more suggestions when the highest score is above the second predetermined threshold; and

when the highest score is below or equal the second predetermined threshold, requesting manual reconciliation for the first transaction in the user interface without presenting any suggestion.

16. 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 of a first entity by a machine-learning program, the features comprising a description of the transaction, a name of a second entity associated with the transaction, and an account associated with the transaction;

training the machine-learning program with training data, the training data comprising values of the features for previously reconciled transactions;

receiving a first transaction that includes a description, a date, and an amount; and

inputting the first transaction to the machine-learning program, the machine-learning program generating one or more suggestions for reconciling the first transaction, each suggestion comprising the name of the second entity in the first transaction and an account associated with the first transaction.

17. The machine-readable storage medium as recited in claim 16, wherein the features for reconciling transactions 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.

18. The machine-readable storage medium as recited in claim 16, wherein receiving the first transaction further comprises:

receiving a statement with one or more transactions; and

extracting the one or more transactions, wherein each extracted transaction is reconciled utilizing the machine-learning program.

19. The machine-readable storage medium as recited in claim 16, wherein the machine-learning program identifies a score for each of the one or more suggestions, wherein the machine further performs operations comprising:

for the suggestion with a highest score, checking if the score is above a first predetermined threshold; and

automatically reconciling the first transaction with the suggestion with the highest score when the highest score is above the first predetermined threshold.

20. The machine-readable storage medium as recited in claim 19, wherein the machine further performs operations comprising:

when the highest score is not above the first predetermined threshold, checking if the highest score is above a second predetermined threshold;

presenting a user interface for manually reconciling the first transaction with any of the one or more suggestions when the highest score is above the second predetermined threshold; and

when the highest score is below or equal the second predetermined threshold, requesting manual reconciliation for the first transaction in the user interface without presenting any suggestion.