Computer-based systems configured for identifying restricted reversals of operations in data entry and methods of use thereof

1. A method comprising:

receiving, by at least one processor, location data from a computing device associated with a user, wherein the location data represents a physical location of the computing device;

utilizing, by the at least one processor, a data profile classification machine learning model to classify a particular data profile according to a data profile classification type based at least in part on a history of data entries associated with the particular data profile when the physical location is within a predetermined proximity of another physical location associated with the particular data profile;

wherein the data profile classification machine learning model comprises a plurality of classification parameters trained to identify similar entities based at least in part on data profile-related data entries;

wherein the data profile-related data entries represent data profile-related electronic activities and data profile-related electronic activity reversals;

determining, by the at least one processor, a reversal rate of data entries in the history of data entries for the particular data profile;

determining, by the at least one processor, an electronic activity reversal ranking of the data entries in the history of data entries for the particular data profile based at least in part on the reversal rate and the data profile classification type;

determining, by the at least one processor, an electronic activity reversal restriction where the electronic activity reversal ranking is below a predetermined value; and

generating, by the at least one processor, a computer instruction to the computing device to cause a pop-up notification comprising the electronic activity reversal restriction of the particular data profile to be presented to the user.

2. The method of claim 1 , further comprising:

accessing, by the at least one processor, the data profile-related data entries of each data profile in a set of entities;

extracting, by the at least one processor, electronic activity features from each data profile-related data entry;

determining, by the at least one processor, a set of reversal data entries representing reversed electronic activities based at least in part on the electronic activity features from each data profile related data entry; and

training, by the at least one processor, the plurality of classification parameters of the data profile classification machine learning model to correlate the electronic activity features of a plurality of data profiles to electronic activity reversal opportunities based at least in part on the set of reversal data entries.

3. The method of claim 1 , further comprising:

accessing, by the at least one processor, the data profile-related data entries of each data profile in a set of entities;

accessing, by the at least one processor, dispute data entries representing disputed data profile-related data entries of the data profile-related data entries;

extracting, by the at least one processor, electronic activity features from each data profile-related data entry;

determining, by the at least one processor, a set of reversal data entries representing reversed electronic activities based at least in part on the electronic activity features from each data profile related data entry; and

training, by the at least one processor, the plurality of classification parameters of the data profile classification machine learning model to correlate the electronic activity features and the dispute data entries of a plurality of data profiles to electronic activity reversal opportunities based at least in part on the set of reversal data entries.

4. The method of claim 1 , wherein the particular data profile comprises a merchant and the data profile-related data entries comprise transaction records.

5. The method of claim 4 , wherein data profile-related electronic activities comprise transactions associated with the transaction records and data profile-related electronic activity reversals comprise refunds for one or more transactions of the data profile-related electronic activities.

6. The method of claim 1 , wherein the data profile classification machine learning model comprises a clustering model.

7. The method of claim 1 , further comprising:

receiving, by the at least one processor, user feedback data representing user feedback indicating the electronic activity reversal restriction; and

training, by the at least one processor, the plurality of classification parameters of the data profile classification machine learning model based at least in part on the data profile classification type and the user feedback data.

8. A method comprising:

receiving, by at least one processor, web browsing data from a computing device associated with a user, wherein the web browsing data represents a website visited by the computing device;

utilizing, by the at least one processor, a data profile classification machine learning model to classify a particular data profile according to a data profile classification type based at least in part on a history of data entries associated with the particular data profile when the web browsing data represents the website matching a data profile website associated with a particular data profile;

wherein the data profile classification machine learning model comprises a plurality of classification parameters trained to identify similar entities based at least in part on data profile-related data entries;

wherein the data profile-related data entries represent data profile-related electronic activities and data profile-related electronic activity reversals;

determining, by the at least one processor, a reversal rate of data entries in the history of data entries for the particular data profile;

determining, by the at least one processor, an electronic activity reversal ranking of the data entries in the history of data entries for the particular data profile based at least in part on the reversal rate and the data profile classification type;

determining, by the at least one processor, an electronic activity reversal restriction where the electronic activity reversal ranking is below a predetermined value; and

generating, by the at least one processor, a computer instruction to the computing device to cause a pop-up notification comprising the electronic activity reversal restriction of the particular data profile.

9. The method of claim 8 , further comprising:

accessing, by the at least one processor, the data profile-related data entries of each data profile in a set of entities;

extracting, by the at least one processor, electronic activity features from each data profile-related data entry;

determining, by the at least one processor, a set of reversal data entries representing reversed electronic activities based at least in part on the electronic activity features from each data profile related data entry; and

training, by the at least one processor, the plurality of classification parameters of the data profile classification machine learning model to correlate the electronic activity features of a plurality of data profiles to electronic activity reversal opportunities based at least in part on the set of reversal data entries.

10. The method of claim 8 , further comprising:

accessing, by the at least one processor, the data profile-related data entries of each data profile in a set of entities;

accessing, by the at least one processor, dispute data entries representing disputed data profile-related data entries of the data profile-related data entries;

extracting, by the at least one processor, electronic activity features from each data profile-related data entry;

determining, by the at least one processor, a set of reversal data entries representing reversed electronic activities based at least in part on the electronic activity features from each data profile related data entry; and

training, by the at least one processor, the plurality of classification parameters of the data profile classification machine learning model to correlate the electronic activity features and the dispute data entries of a plurality of data profiles to electronic activity reversal opportunities based at least in part on the set of reversal data entries.

11. The method of claim 8 , wherein the particular data profile comprises a merchant and the data profile-related data entries comprise transaction records.

12. The method of claim 11 , wherein data profile-related electronic activities comprise transactions associated with the transaction records and data profile-related electronic activity reversals comprise refunds for one or more transactions of the data profile-related electronic activities.

13. The method of claim 8 , wherein the data profile classification machine learning model comprises a clustering model.

14. The method of claim 8 , further comprising:

receiving, by the at least one processor, user feedback data representing user feedback indicating the electronic activity reversal restriction; and

training, by the at least one processor, the plurality of classification parameters of the data profile classification machine learning model based at least in part on the data profile classification type and the user feedback data.

15. A system comprising:

at least one processor configured to execute computer instructions that cause the at least one processor to perform steps to:

receive location data from a computing device associated with a user, wherein the location data represents a physical location of the computing device;

utilize a data profile classification machine learning model to classify a particular data profile according to a data profile classification type based at least in part on a history of data entries associated with the particular data profile when the physical location is within a predetermined proximity of another physical location associated with the particular data profile;

wherein the data profile classification machine learning model comprises a plurality of classification parameters trained to identify similar entities based at least in part on data profile-related data entries;

wherein the data profile-related data entries represent data profile-related electronic activities and data profile-related electronic activity reversals;

determine a reversal rate of data entries in the history of data entries for the particular data profile;

determine an electronic activity reversal ranking of the data entries in the history of data entries for the particular data profile based at least in part on the reversal rate and the data profile classification type;

determine an electronic activity reversal restriction where the electronic activity reversal ranking is below a predetermined value; and

generate a computer instruction to the computing device to cause a pop-up notification comprising the electronic activity reversal restriction of the particular data profile to be presented to the user.

16. The system of claim 15 , wherein the at least one processor is further configured to execute computer instructions that cause the at least one processor to perform further steps to:

access the data profile-related data entries of each data profile in a set of entities;

extract electronic activity features from each data profile-related data entry;

determine a set of reversal data entries representing reversed electronic activities based at least in part on the electronic activity features from each data profile related data entry; and

train the plurality of classification parameters of the data profile classification machine learning model to correlate the electronic activity features of a plurality of data profiles to electronic activity reversal opportunities based at least in part on the set of reversal data entries.

17. The system of claim 15 , wherein the at least one processor is further configured to execute computer instructions that cause the at least one processor to perform further steps to:

access the data profile-related data entries of each data profile in a set of entities;

access dispute data entries representing disputed data profile-related data entries of the data profile-related data entries;

extract electronic activity features from each data profile-related data entry;

determine a set of reversal data entries representing reversed electronic activities based at least in part on the electronic activity features from each data profile related data entry; and

train the plurality of classification parameters of the data profile classification machine learning model to correlate the electronic activity features and the dispute data entries of a plurality of data profiles to electronic activity reversal opportunities based at least in part on the set of reversal data entries.

18. The system of claim 15 , wherein the particular data profile comprises a merchant and the data profile-related data entries comprise transaction records.

19. The system of claim 15 , wherein the data profile classification machine learning model comprises a clustering model.

20. The system of claim 15 , wherein the at least one processor is further configured to execute computer instructions that cause the at least one processor to perform further steps to:

receive user feedback data representing user feedback indicating the electronic activity reversal restriction; and

train the plurality of classification parameters of the data profile classification machine learning model based at least in part on the data profile classification type and the user feedback data.