Systems and methods for allocating resources using information technology infrastructure

1. A data processing system comprising one or more processors, coupled with memory, wherein the data processing system is configured to:

identify, for a participant, a feature vector comprising a first feature indicating demographic information, a second feature indicating healthcare spending amount, a third feature indicating a health savings account contribution, and a fourth feature indicating a healthiness of the participant;

determine a metric that indicates similarity between the feature vector and a plurality of feature vectors of a plurality of healthcare expense prediction models stored in a database and generated from financial data and health data of a plurality of participants via machine learning, the health data comprising diet information, fitness information, or exercise information of the plurality of participants;

select, based on the metric that indicates similarity, a healthcare expense prediction model of the plurality of healthcare expense prediction models configured to predict future healthcare expenses of the participant, the healthcare expense prediction model: i) trained by the data processing system via machine learning based at least in part on the financial data and at least one of the diet information, the fitness information, or the exercise information of the health data of the plurality of participants, and ii) re-trained via machine learning by the data processing system responsive to receipt of an update to the diet information, the fitness information, or the exercise information of the health data of the plurality of participants;

determine, from the healthcare expense prediction model trained and re-trained by the data processing system, a predicted lifetime healthcare expense of the participant;

identify, responsive to determination of the predicted lifetime healthcare expense, a lifetime non-healthcare expense of the participant;

determine, based on the predicted lifetime healthcare expense of the participant, a first amount of funds to allocate per time period to a healthcare tax benefit account of the participant;

determine, based on the lifetime non-healthcare expense of the participant, a second amount of funds to allocate per time period to a non-healthcare tax benefit account of the participant; and

provide, for presentation via an interactive user interface, an indication of the first amount of funds to allocate to the healthcare tax benefit account and the second amount of funds to allocate to the non-healthcare tax benefit account, the interactive user interface including a control object configured to i) receive an input to adjust the first amount of funds, and ii) responsive to receipt of the input to adjust the first amount of funds, update a total amount of funds projected to be allocated to the healthcare tax benefit account.

2. The data processing system of claim 1 , wherein the data processing system is further configured to:

train, via the machine learning, the healthcare expense prediction model based at least in part on the financial data, the diet information, the fitness information, and the exercise information of the health data of the plurality of participants.

3. The data processing system of claim 1 , wherein the data processing system is further configured to:

receive the update to the diet information, the fitness information, or the exercise information of the health data of the plurality of participants; and

re-train, responsive to receipt of the update to the diet information, the fitness information, or the exercise information of the health data of the plurality of participants, the previously trained healthcare expense prediction model to maintain or improve an effectiveness or efficiency of the data processing system to make predictions for resource allocation based on the re-trained healthcare expense prediction model relative to the previously trained healthcare expense prediction model.

4. The data processing system of claim 1 , wherein the data processing system is further configured to:

receive a diet information, a fitness information, or an exercise information for an additional participant different from the plurality of participants; and

re-train, responsive to receipt of the diet information, the fitness information, or the exercise information for the additional participant, the previously trained healthcare expense prediction model to maintain or improve an effectiveness or efficiency of the data processing system to make predictions for resource allocation based on the re-trained healthcare expense prediction model relative to the previously trained healthcare expense prediction model.

5. The data processing system of claim 1 , wherein the data processing system is further configured to:

perform a lookup to identify the healthcare tax benefit account of the participant to provide funds towards the predicted lifetime healthcare expense of the participant and the non-healthcare tax benefit account of the participant to provide funds towards lifetime non-healthcare expenses of the participant.

6. The data processing system of claim 1 , wherein the data processing system is further configured to:

initiate a countdown timer for the interactive user interface configured to manage network security by disabling input via the interactive user interface responsive to expiration of the countdown timer.

7. The data processing system of claim 1 , wherein the data processing system is further configured to:

select the healthcare expense prediction model with a highest rank based on the metric that indicates similarity.

8. The data processing system of claim 1 , wherein the data processing system is further configured to:

determine a first allocation priority associated with the healthcare tax benefit account; and

determine a second allocation priority associated with the non-healthcare tax benefit account, the second allocation priority less than the first allocation priority.

9. The data processing system of claim 8 , wherein the data processing system is further configured to:

determine the first amount of funds to allocate to the healthcare tax benefit account based on the first allocation priority, the second allocation priority, and the healthcare expense prediction model.

10. The data processing system of claim 1 , wherein the data processing system is further configured to:

generate the interactive user interface with an electronic survey comprising one or more input elements, the interactive user interface configured to receive the financial data and the health data.

11. A method, comprising:

identifying, by a data processing system comprising one or more processors coupled to memory, a feature vector for a participant comprising a first feature indicating demographic information, a second feature indicating healthcare spending amount, a third feature indicating a health savings account contribution, and a fourth feature indicating a healthiness of the participant;

determining, by the data processing system, a metric that indicates similarity between the feature vector and a plurality of feature vectors of a plurality of healthcare expense prediction models stored in a database and generated from financial data and health data of a plurality of participants via machine learning, the health data comprising diet information, fitness information, or exercise information of the plurality of participants;

selecting, by the data processing system based on the metric that indicates similarity, a healthcare expense prediction model of the plurality of healthcare expense prediction models configured to predict future healthcare expenses of the participant, the healthcare expense prediction model: i) trained by the data processing system via machine learning based at least in part on the financial data and at least one of the diet information, the fitness information, or the exercise information of the health data of the plurality of participants, and ii) re-trained via machine learning by the data processing system responsive to receipt of an update to the diet information, the fitness information, or the exercise information of the health data of the plurality of participants;

determining, by the data processing system from the healthcare expense prediction model trained and re-trained by the data processing system, a predicted lifetime healthcare expense of the participant;

identifying, by the data processing system responsive to determination of the predicted lifetime healthcare expense, a lifetime non-healthcare expense of the participant;

determining, by the data processing system based on the predicted lifetime healthcare expense of the participant, a first amount of funds to allocate per time period to a healthcare tax benefit account of the participant;

determining, by the data processing system based on the lifetime non-healthcare expense of the participant, a second amount of funds to allocate per time period to a non-healthcare tax benefit account of the participant; and

providing, by the data processing system for presentation via an interactive user interface, an indication of the first amount of funds to allocate to the healthcare tax benefit account and the second amount of funds to allocate to the non-healthcare tax benefit account, the interactive user interface including a control object configured to i) receive an input to adjust the first amount of funds, and ii) responsive to receipt of the input to adjust the first amount of funds, update a total amount of funds projected to be allocated to the healthcare tax benefit account.

12. The method of claim 11 , comprising:

training, by the data processing system the machine learning, the healthcare expense prediction model based at least in part on the financial data, the diet information, the fitness information, and the exercise information of the health data of the plurality of participants.

13. The method of claim 11 , comprising:

receiving, by the data processing system, the update to the diet information, the fitness information, or the exercise information of the health data of the plurality of participants; and

re-training, by the data processing system responsive to receipt of the update to the diet information, the fitness information, or the exercise information of the health data of the plurality of participants, the previously trained healthcare expense prediction model to maintain or improve an effectiveness or efficiency of the data processing system to make predictions for resource allocation based on the re-trained healthcare expense prediction model relative to the previously trained healthcare expense prediction model.

14. The method of claim 11 , comprising:

receiving, by the data processing system, a diet information, a fitness information, or an exercise information for an additional participant different from the plurality of participants; and

re-training, by the data processing system responsive to receipt of the diet information, the fitness information, or the exercise information for the additional participant, the previously trained healthcare expense prediction model to maintain or improve an effectiveness or efficiency of the data processing system to make predictions for resource allocation based on the re-trained healthcare expense prediction model relative to the previously trained healthcare expense prediction model.

15. The method of claim 11 , comprising:

performing, by the data processing system, a lookup to identify the healthcare tax benefit account of the participant to provide funds towards the predicted lifetime healthcare expense of the participant and the non-healthcare tax benefit account of the participant to provide funds towards lifetime non-healthcare expenses of the participant.

16. The method of claim 11 , comprising:

initiating, by the data processing system, a countdown timer for the interactive user interface configured to manage network security by disabling input via the interactive user interface responsive to expiration of the countdown timer.

17. The method of claim 11 , comprising:

selecting, by the data processing system, the healthcare expense prediction model with a highest rank based on the metric that indicates similarity.

18. The method of claim 11 , comprising:

determining, by the data processing system, a first allocation priority associated with the healthcare tax benefit account; and

determining, by the data processing system, a second allocation priority associated with the non-healthcare tax benefit account, the second allocation priority less than the first allocation priority.

19. The method of claim 18 , comprising:

determining, by the data processing system, the first amount of funds to allocate to the healthcare tax benefit account based on the first allocation priority, the second allocation priority, and the healthcare expense prediction model.

20. The method of claim 11 , comprising:

generating, by the data processing system, the interactive user interface with an electronic survey comprising one or more input elements, the interactive user interface configured to receive the financial data and the health data.