Various embodiments of the present disclosure can include systems, methods, and non-transitory computer readable media configured to receive historical transaction information associated with one or more historical transactions. A machine learning model is trained based on the historical transaction information. Transaction information associated with a transaction to be analyzed for potential data input errors is received. A potential data input error is detected in the transaction information based on the machine learning model. A visual indication is provided on a graphical user interface based on the detecting the potential data input error.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method comprising: receiving, by a computing system, historical transaction information associated with one or more historical transactions; training, by the computing system, a machine learning model based on the historical transaction information; receiving, by the computing system, transaction information associated with a transaction to be analyzed for potential data input errors; detecting, by the computing system, a potential data input error in the transaction information based on the machine learning model; and providing, by the computing system, a visual indication on a graphical user interface based on the detecting the potential data input error. 2. The computer-implemented method of claim 1, wherein the detecting the potential data input error comprises: determining a likelihood of error for a data field based on the machine learning model; and determining that the likelihood of error exceeds a threshold likelihood value. 3. The computer-implemented method of claim 1, wherein the historical transaction information comprises initial transaction information and audited transaction information associated with the initial transaction information. 4. The computer-implemented method of claim 3, wherein the training the machine learning model based on the historical transaction information comprises: determining historical errors based on differences between the initial transaction information and the audited transaction information. 5. The computer-implemented method of claim 3, wherein the training the machine learning model based on the historical transaction information further comprises: receiving a second set of initial transaction information and a second set of audited transaction information associated with the second set of initial transaction information; testing the machine learning model based on the second set of initial transaction information and the second set of audited transaction information; and re-training the machine learning model based on the testing the machine learning model. 6. The computer-implemented method of claim 5, wherein, the training the machine learning model comprises selecting a first machine learning algorithm, and the re-training the machine learning model comprises selecting a second machine learning algorithm that is different from the first machine learning algorithm. 7. The computer-implemented method of claim 1, wherein the visual indication on the graphical user interface comprises highlighting the potential data input error. 8. The computer-implemented method of claim 7, wherein the shade of the highlighting varies based on a likelihood of error determination made by the machine learning model. 9. The computer-implemented method of claim 1, wherein the transaction information comprises a blank data field, and the method further comprises calculating a default value for the blank data field based on the machine learning model. 10. The computer-implemented method of claim 9, wherein the default value comprises a value satisfying a threshold likelihood of error as determined by the machine learning model. 11. A system comprising: at least one processor; and a memory storing instructions that, when executed by the at least one processor, cause the system to perform: receiving historical transaction information associated with one or more historical transactions; training a machine learning model based on the historical transaction information; receiving transaction information associated with a transaction to be analyzed for potential data input errors; detecting a potential data input error in the transaction information based on the machine learning model; and providing a visual indication on a graphical user interface based on the detecting the potential data input error. 12. The system of claim 11, wherein the detecting the potential data input error comprises: determining a likelihood of error for a data field based on the machine learning model; and determining that the likelihood of error exceeds a threshold likelihood value. 13. The system of claim 11, wherein the historical transaction information comprises initial transaction information and audited transaction information associated with the initial transaction information. 14. The system of claim 13, wherein the training the machine learning model based on the historical transaction information comprises: determining historical errors based on differences between the initial transaction information and the audited transaction information. 15. The system of claim 13, wherein the training the machine learning model based on the historical transaction information further comprises: receiving a second set of initial transaction information and a second set of audited transaction information associated with the second set of initial transaction information; testing the machine learning model based on the second set of initial transaction information and the second set of audited transaction information; and re-training the machine learning model based on the testing the machine learning model. 16. A non-transitory computer-readable storage medium including instructions that, when executed by at least one processor of a computing system, cause the computing system to perform: receiving historical transaction information associated with one or more historical transactions; training a machine learning model based on the historical transaction information; receiving transaction information associated with a transaction to be analyzed for potential data input errors; detecting a potential data input error in the transaction information based on the machine learning model; and providing a visual indication on a graphical user interface based on the detecting the potential data input error. 17. The non-transitory computer-readable storage medium of claim 16, wherein the detecting the potential data input error comprises: determining a likelihood of error for a data field based on the machine learning model; and determining that the likelihood of error exceeds a threshold likelihood value. 18. The non-transitory computer-readable storage medium of claim 16, wherein the historical transaction information comprises initial transaction information and audited transaction information associated with the initial transaction information. 19. The non-transitory computer-readable storage medium of claim 18, wherein the training the machine learning model based on the historical transaction information comprises: determining historical errors based on differences between the initial transaction information and the audited transaction information. 20. The non-transitory computer-readable storage medium of claim 18, wherein the training the machine learning model based on the historical transaction information further comprises: receiving a second set of initial transaction information and a second set of audited transaction information associated with the second set of initial transaction information; testing the machine learning model based on the second set of initial transaction information and the second set of audited transaction information; and re-training the machine learning model based on the testing the machine learning model.