Settings optimization engine using artificial intelligence to enhance client privacy
Aspects of the disclosure relate to using machine learning to modify account privacy settings. A computing platform may identify initial account settings for an individual of a plurality of individuals. Using a settings optimization model, the computing platform may identify account data and third party data for the individual. Using the settings optimization model, the computing platform may identify discrepancies between the initial account settings for the first individual and account settings for a subset of the plurality of individuals having common characteristics with the individual. Based on the discrepancies, the computing platform may identify settings modifications for the individual, and may determine that a modification of the settings modifications applies to a detected interaction of the individual. The computing platform may direct an enterprise data source to perform the modification, which may cause the enterprise data source to modify the initial account settings based on the modification.
1. A computing platform, comprising:
at least one processor;
a communication interface communicatively coupled to the at least one processor; and
memory storing computer-readable instructions that, when executed by the at least one processor, cause the computing platform to:
identify, by inputting enterprise account data and third party source data for a first individual into a settings optimization model, a subset of a plurality of individuals having common characteristics with the first individual;
identify, using the settings optimization model, one or more discrepancies between initial account settings for the first individual and account settings for the subset of the plurality of individuals, wherein the account settings for the subset of the plurality of individuals are included in the enterprise account data for the plurality of individuals, wherein the setting optimization model is further configured to identify investment recommendations, based on an identified risk tolerance for the first individual, and wherein identifying the investment recommendations comprises:
identifying that the first individual has applied maximum privacy settings to an account,
identifying, based on the maximum privacy settings, that the first individual is risk averse, and
generating a recommendation to shift from a higher risk investment to a lower risk investment;
identify, based on the one or more discrepancies between the initial account settings for the first individual and the account settings for the subset of the plurality of individuals, one or more account settings modifications for the first individual;
monitor an event processing system to detect an interaction by the first individual;
based on the detected interaction, compare account settings corresponding to the detected interaction to the one or more account settings modifications, wherein the comparison of the account settings corresponding to the detected interaction to the one or more account settings modifications results in a determination that a first modification of the one or more account settings modifications applies to the account settings corresponding to the detected interaction; and
send one or more commands directing an enterprise data source to modify the initial account settings based on the first modification, wherein sending the one or more commands directing the enterprise data source to modify the initial account settings based on the first modification causes the enterprise data source to modify the initial account settings based on the first modification.
2. The computing platform of claim 1 , wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, further cause the computing platform to:
send, to the enterprise data source, a request for the enterprise account data for the plurality of individuals; and
receive, from the enterprise data source, the enterprise account data for the plurality of individuals, wherein the enterprise account data comprises privacy settings for online banking accounts for the plurality of individuals.
3. The computing platform of claim 2 , wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, further cause the computing platform to:
send, to a third party data source, a request for the third party source data for the plurality of individuals; and
receive, from the third party data source, the third party source data for the plurality of individuals.
4. The computing platform of claim 3 , wherein the third party source data comprises one or more of: social media privacy settings, device privacy settings, privacy settings for an application, or privacy settings for another website.
5. The computing platform of claim 1 , wherein the initial account settings comprise privacy settings for an online banking account.
6. The computing platform of claim 1 , wherein the one or more account settings modifications comprise one or more of: password strength parameters, frequency of password changes, transaction limits, suspicious transaction identification, or attempted login notifications related to unknown devices.
7. The computing platform of claim 1 , wherein the detected interaction comprises an action of the first individual within an online banking account.
8. The computing platform of claim 1 , wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, further cause the computing platform to:
based on the determination that the first modification of the one or more account settings modifications applies to the account settings corresponding to the detected interaction, compute a likelihood of acceptance score indicating a likelihood of acceptance of the first modification by the first individual.
9. The computing platform of claim 8 , wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, further cause the computing platform to:
compare the likelihood of acceptance score to a first predetermined threshold, wherein sending the one or more commands directing the enterprise data source to modify the initial account settings based on the first modification is based on a determination that the likelihood of acceptance score exceeds the first predetermined threshold.
10. The computing platform of claim 9 , wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, further cause the computing platform to:
monitor the event processing system to detect a second interaction by the first individual;
based on the second detected interaction, compare account settings corresponding to the second detected interaction to the one or more account settings modifications, wherein the comparison of the account settings corresponding to the second detected interaction to the one or more account settings modifications results in a determination that a second modification of the one or more account settings modifications applies to the account settings corresponding to the second detected interaction;
based on the determination that the second modification of the one or more account settings modifications applies to the account settings corresponding to the second detected interaction, compute a second likelihood of acceptance score indicating a likelihood of acceptance of the second modification by the first individual;
determine that the second likelihood of acceptance score does not exceed the first predetermined threshold;
based on the determination that the second likelihood of acceptance score does not exceed the first predetermined threshold, compare the second likelihood of acceptance score to a second predetermined threshold;
based on a determination that the second predetermined threshold is exceeded, send one or more commands directing a user device corresponding to the first individual to display a prompt requesting permission to perform the second modification, wherein sending the one or more commands directing the user device corresponding to the first individual to display the prompt requesting the permission to perform the second modification causes the user device corresponding to the first individual to display the prompt requesting the permission to perform the second modification; and
based on a determination that the second predetermined threshold is not exceeded, determine that the second modification should not be performed.
11. The computing platform of claim 1 , wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, further cause the computing platform to:
monitor the enterprise data source to identify input of initial account settings for the first individual of the plurality of individuals; and
identify, using an identity of the first individual and a settings optimization model, the enterprise account data and the third party source data for the first individual, wherein the settings optimization model is trained by the computing platform using the enterprise account data and the third party source data for the plurality of individuals.
12. The computing platform of claim 1 , wherein the one or more account settings modifications are further based on the third party source data for the first individual, wherein the third party source data indicates a lack of enrollment in a particular service, and wherein the one or more account settings modifications comprise enrolling the first individual in the particular service.
13. The computing platform of claim 1 , wherein the one or more account settings modifications are further based on the enterprise account data for the first individual, indicating an investment risk profile for a particular account of the first individual, and wherein the one or more account settings modifications comprise adjusting privacy settings for a different account of the first individual based on the investment risk profile.
14. The computing platform of claim 1 , wherein modifying the initial account settings further includes modifying account settings for a second application, different than a first application, and wherein monitoring the event processing system to detect the interaction by the first individual comprises detecting an interaction in the first application.
15. A method comprising:
at a computing platform comprising at least one processor, a communication interface, and memory:
identifying, by inputting enterprise account data and third party source data for a first individual into a settings optimization model, a subset of a plurality of individuals having common characteristics with the first individual;
identifying, using the settings optimization model, one or more discrepancies between initial account settings for the first individual and account settings for the subset of the plurality of individuals, wherein the account settings for the subset of the plurality of individuals are included in the enterprise account data for the plurality of individuals;
identifying, based on the one or more discrepancies between the initial account settings for the first individual and the account settings for the subset of the plurality of individuals, one or more account settings modifications for the first individual, wherein the setting optimization model is further configured to identify investment recommendations, based on an identified risk tolerance for the first individual, and wherein identifying the investment recommendations comprises:
identifying that the first individual has applied maximum privacy settings to an account,
identifying, based on the maximum privacy settings, that the first individual is risk averse, and
generating a recommendation to shift from a higher risk investment to a lower risk investment;
monitoring an event processing system to detect an interaction by the first individual;
based on the detected interaction, comparing account settings corresponding to the detected interaction to the one or more account settings modifications, wherein the comparison of the account settings corresponding to the detected interaction to the one or more account settings modifications results in a determination that a first modification of the one or more account settings modifications applies to the account settings corresponding to the detected interaction; and
sending one or more commands directing an enterprise data source to modify the initial account settings based on the first modification, wherein sending the one or more commands directing the enterprise data source to modify the initial account settings based on the first modification causes the enterprise data source to modify the initial account settings based on the first modification.
16. The method of claim 15 , further comprising:
sending, to the enterprise data source, a request for the enterprise account data for the plurality of individuals; and
receiving, from the enterprise data source, the enterprise account data for the plurality of individuals, wherein the enterprise account data comprises privacy settings for online banking accounts for the plurality of individuals.
17. The method of claim 16 , further comprising:
sending, to a third party data source, a request for the third party source data for the plurality of individuals; and
receiving, from the third party data source, the third party source data for the plurality of individuals.
18. The method of claim 17 , wherein the third party source data comprises one or more of: social media privacy settings, device privacy settings, privacy settings for an application, or privacy settings for another website.
19. The method of claim 15 , wherein the initial account settings comprise privacy settings for an online banking account.
20. One or more non-transitory computer-readable media storing instructions that, when executed by a computing platform comprising at least one processor, a communication interface, and memory, cause the computing platform to:
identify, by inputting enterprise account data and third party source data for a first individual into a settings optimization model, a subset of a plurality of individuals having common characteristics with the first individual;
identify, using the settings optimization model, one or more discrepancies between initial account settings for the first individual and account settings for the subset of the plurality of individuals, wherein the account settings for the subset of the plurality of individuals are included in the enterprise account data for the plurality of individuals, wherein the setting optimization model is further configured to identify investment recommendations, based on an identified risk tolerance for the first individual, and wherein identifying the investment recommendations comprises:
identifying that the first individual has applied maximum privacy settings to an account,
identifying, based on the maximum privacy settings, that the first individual is risk averse, and
generating a recommendation to shift from a higher risk investment to a lower risk investment;
identify, based on the one or more discrepancies between the initial account settings for the first individual and the account settings for the subset of the plurality of individuals, one or more account settings modifications for the first individual;
monitor an event processing system to detect an interaction by the first individual;
based on the detected interaction, compare account settings corresponding to the detected interaction to the one or more account settings modifications, wherein the comparison of the account settings corresponding to the detected interaction to the one or more account settings modifications results in a determination that a first modification of the one or more account settings modifications applies to the account settings corresponding to the detected interaction; and
send one or more commands directing an enterprise data source to modify the initial account settings based on the first modification, wherein sending the one or more commands directing the enterprise data source to modify the initial account settings based on the first modification causes the enterprise data source to modify the initial account settings based on the first modification.