TARGETED ANTI-SCAM EDUCATION AND FEEDBACK

20250069172

18/454382

August 23, 2023

In some implementations, an education system may receive demographic information and account information associated with a user. The education system may generate a risk profile based on the demographic information and the account information. The education system may map the risk profile to at least one threat, out of a plurality of possible threats indicated in a data structure, likely to be targeted to the user. The education system may transmit, to a user device, an educational message that is associated with the at least one threat and that is indicated in the data structure.

1. A system for generating targeted anti-scam education, the system comprising: one or more memories; and one or more processors, communicatively coupled to the one or more memories, configured to: receive demographic information and account information associated with a user; map the demographic information and the account information to at least one threat, out of a plurality of possible threats, likely to be targeted to the user; transmit, to a device associated with the user, an indication of the at least one threat; receive, from the device associated with the user, an indication of a news story associated with a scam; determine, based on the demographic information and the account information, a likelihood that the user will be impacted by the scam; and transmit, to the device associated with the user, an indication of the likelihood.

2. The system of claim 1, wherein the one or more processors, to receive the indication of the news story, are configured to: transmit instructions for an input element of a mobile application or a website; and receive the indication of the news story via the input element.

3. The system of claim 1, wherein the one or more processors, to transmit the indication of the at least one threat, are configured to: transmit a hyperlink to an educational module associated with the at least one threat.

4. The system of claim 1, wherein the one or more processors, to transmit the indication of the likelihood, are configured to: transmit instructions for a pop-up window indicating the likelihood.

5. The system of claim 1, wherein the demographic information includes an age, a gender, a socioeconomic bracket, or an educational attainment, associated with the user.

6. The system of claim 1, wherein the account information includes an account type, a balance, or one or more historical transactions.

7. A method of generating targeted anti-scam education, comprising: receiving demographic information and account information associated with a user; generating a risk profile based on the demographic information and the account information; mapping the risk profile to at least one threat, out of a plurality of possible threats indicated in a data structure, likely to be targeted to the user; and transmitting, to a user device, an educational message that is associated with the at least one threat and that is indicated in the data structure.

8. The method of claim 7, further comprising: receiving an indication of an interaction with the educational message; and updating the risk profile based on the indication of the interaction.

9. The method of claim 7, wherein generating the risk profile comprises: applying a machine learning model to vectorized representations of the demographic information and the account information, wherein the risk profile includes a plurality of scores, associated with a plurality of categories, output by the machine learning model.

10. The method of claim 9, wherein mapping the risk profile to the at least one threat comprises: determining a plurality of distances between the risk profile and the plurality of possible threats indicated in the data structure; and selecting the at least one threat based on the plurality of distances.

11. The method of claim 7, wherein transmitting the educational message comprises: transmitting instructions for a push notification to the user device.

12. The method of claim 7, further comprising: selecting the educational message, from a plurality of possible educational messages, using an identifier, associated with the educational message, indicated as corresponding to the at least one threat in the data structure.

13. The method of claim 7, further comprising: receiving, from the user device, supplemental information associated with the user, wherein the risk profile is further based on the supplemental information.

14. A non-transitory computer-readable medium storing a set of instructions for providing targeted anti-scam feedback, the set of instructions comprising: one or more instructions that, when executed by one or more processors of a device, cause the device to: receive demographic information and account information associated with a user; generate a risk profile based on the demographic information and the account information; receive, from a device associated with the user, an indication of a news story associated with a scam; determine, based on the risk profile, a likelihood that the user will be impacted by the scam; and transmit, to the device associated with the user, an indication of the likelihood.

15. The non-transitory computer-readable medium of claim 14, wherein the one or more instructions, when executed, cause the device to: transmit, to the device associated with the user, instructions for a user interface (UI) associated with a website or a mobile application, wherein the indication of the news story is received via the UI.

16. The non-transitory computer-readable medium of claim 14, wherein the one or more instructions, that cause the device to receive the indication of the news story, cause the device to: receive, from the device associated with the user, a hyperlink associated with the news story.

17. The non-transitory computer-readable medium of claim 14, wherein the one or more instructions, that cause the device to determine the likelihood that the user will be impacted by the scam, cause the device to: determine an identifier associated with the scam based on the news story; map the identifier associated with the scam to a set of risks using a data structure; and determine the likelihood based on a distance between the set of risks and the risk profile.

18. The non-transitory computer-readable medium of claim 14, wherein the one or more instructions, that cause the device to determine the likelihood that the user will be impacted by the scam, cause the device to: determine a set of risks associated with the scam by applying a machine learning model to the news story; and determine the likelihood based on a distance between the set of risks and the risk profile.

19. The non-transitory computer-readable medium of claim 14, wherein the one or more instructions, when executed, cause the device to: transmit instructions for a loading screen in response to receiving the indication of the news story, wherein the indication of the likelihood is transmitted based on determining the likelihood.

20. The non-transitory computer-readable medium of claim 14, wherein the one or more instructions, when executed, cause the device to: receive, from the device associated with the user, supplemental information associated with the user, wherein the risk profile is further based on the supplemental information.

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edspap.20250069172