Machine learning based third party entity modeling for predictive exposure prevention

1. An electronic communication system for preventing unauthorized interactions, comprising:

one or more computer processors;

a memory; and

a processing module stored in the memory, executable by the one or more computer processors and configured to:

receive historical data from one or more data sources, wherein the historical data comprises exposure data associated with one or more exposures, user data associated with one or more users, and resource entity data associated with one or more resource entities;

store the historical data in a historical database;

analyze, using one or more machine learning models, the historical data associated with the one or more exposures, the one or more users and the one or more resource entities;

generate, using the one or more machine learning models, an output associated with each of the one or more resource entities based on analyzing the historical data associated with the one or more resource entities, wherein the output comprises an exposure rating associated with the one or more resource entities; and

monitor real-time streaming data associated with the one or more resource entities and the one or more exposures; and

dynamically update the output associated with each of the one or more resource entities based on monitoring the real-time streaming data.

2. The electronic communication system according to claim 1 , wherein receiving the historical data from the one or more data sources comprises:

receiving the exposure data from an entity system, wherein the exposure data comprises information associated at least with mass exposures, misappropriation information associated with one or more devices associated with the one or more exposures;

receiving the user data from the entity system, wherein the user data is associated with the one or more users and comprises at least interaction history and personal data; and

receiving the resource entity data from one or more resource entity systems, wherein the resource entity data comprises at least interaction velocities, raw interaction data, and supplemental resource information.

3. The electronic communication system according to claim 1 , wherein the processing module stored in the memory, executable by the one or more computer processors and configured for generating exposure characteristics for potential interactions between each of the one or more users and each of the one or more resource entities by combining the user data and the output, wherein the exposure characteristics are different for each of the potential interactions.

4. The electronic communication system according to claim 3 , wherein the processing module stored in the memory, executable by the one or more computer processors and configured for generating the exposure characteristics in response to identifying initiation of the potential interactions.

5. The electronic communication system according to claim 3 , wherein identifying initiation of the potential interactions is based on the user data associated with the one or more users.

6. The electronic communication system according to claim 3 , wherein the processing module stored in the memory, executable by the one or more computer processors and configured for storing the exposure characteristics in the historical database and transmitting the exposure characteristics to the one or more users based upon identifying initiation of the potential interactions.

7. The electronic communication system according to claim 1 , wherein the processing module stored in the memory, executable by the one or more computer processors and configured to:

predict occurrence of a first potential interaction between a first user and a first merchant based on first user data associated with the first user; and

generate first exposure characteristics associated with the first potential interaction.

8. A computer program product for preventing, identifying and remediating decision boundary exposure, comprising a non-transitory computer-readable storage medium having computer-executable instructions for:

receiving historical data from one or more data sources, wherein the historical data comprises exposure data associated with one or more exposures, user data associated with one or more users, and resource entity data associated with one or more resource entities;

storing the historical data in a historical database;

analyzing, using one or more machine learning models, the historical data associated with the one or more exposures, the one or more users and the one or more resource entities;

generating, using the one or more machine learning models, an output associated with each of the one or more resource entities based on analyzing the historical data associated with the one or more resource entities, wherein the output comprises an exposure rating associated with the one or more resource entities; and

monitoring real-time streaming data associated with the one or more resource entities and the one or more exposures; and

dynamically updating the output associated with each of the one or more resource entities based on monitoring the real-time streaming data.

9. The computer program product according to claim 8 , wherein the computer-executable instructions for receiving the historical data from the one or more data sources further comprise:

receiving the exposure data from an entity system, wherein the exposure data comprises information associated at least with mass exposures, misappropriation information associated with one or more devices associated with the one or more exposures;

receiving the user data from the entity system, wherein the user data is associated with the one or more users and comprises at least interaction history and personal data; and

receiving the resource entity data from one or more resource entity systems, wherein the resource entity data comprises at least interaction velocities, raw interaction data, and supplemental resource information.

10. The computer program product according to claim 8 , wherein the non-transitory computer-readable storage medium comprises computer-executable instructions for generating exposure characteristics for potential interactions between each of the one or more users and each of the one or more resource entities by combining the user data and the output, wherein the exposure characteristics are different for each of the potential interactions.

11. The computer program product according to claim 10 , wherein the non-transitory computer-readable storage medium comprises computer-executable instructions for storing the exposure characteristics in the historical database and transmitting the exposure characteristics to the one or more users based upon identifying initiation of the potential interactions.

12. The computer program product according to claim 8 , wherein the non-transitory computer-readable storage medium comprises computer-executable instructions for:

predicting occurrence of a first potential interaction between a first user and a first merchant based on first user data associated with the first user; and

generating first exposure characteristics associated with the first potential interaction.

13. A computerized method for preventing unauthorized interactions, comprising:

receiving historical data from one or more data sources, wherein the historical data comprises exposure data associated with one or more exposures, user data associated with one or more users, and resource entity data associated with one or more resource entities;

storing the historical data in a historical database;

analyzing, using one or more machine learning models, the historical data associated with the one or more exposures, the one or more users and the one or more resource entities;

generating, using the one or more machine learning models, an output associated with each of the one or more resource entities based on analyzing the historical data associated with the one or more resource entities, wherein the output comprises an exposure rating associated with the one or more resource entities; and

monitoring real-time streaming data associated with the one or more resource entities and the one or more exposures; and

dynamically updating the output associated with each of the one or more resource entities based on monitoring the real-time streaming data.

14. The computerized method according to claim 13 , wherein receiving the historical data from the one or more data sources further comprises:

receiving the exposure data from an entity system, wherein the exposure data comprises information associated at least with mass exposures, misappropriation information associated with one or more devices associated with the one or more exposures;

receiving the user data from the entity system, wherein the user data is associated with the one or more users and comprises at least interaction history and personal data; and

receiving the resource entity data from one or more resource entity systems, wherein the resource entity data comprises at least interaction velocities, raw interaction data, and supplemental resource information.

15. The computerized method according to claim 13 , wherein the method further comprises generating exposure characteristics for potential interactions between each of the one or more users and each of the one or more resource entities by combining the user data and the output, wherein the exposure characteristics are different for each of the potential interactions.

16. The computerized method according to claim 15 , wherein the method further comprises storing the exposure characteristics in the historical database and transmitting the exposure characteristics to the one or more users based upon identifying initiation of the potential interactions.

17. The computerized method according to claim 13 , the method further comprises:

predicting occurrence of a first potential interaction between a first user and a first merchant based on first user data associated with the first user; and

generating first exposure characteristics associated with the first potential interaction.