Methods and systems for providing estimated transactional data
A computer-implemented method for providing an estimated utility expenditure to a user may include: obtaining, via one or more processors, historical transactional data of one or more customers other than the user from one or more transactional entities, wherein the historical transactional data includes: at least one address of a given customer of the one or more customers; and a historical utility expenditure associated with the at least one address; generating, via the one or more processors, a heatmap based on the historical transactional data of the one or more customers via one or more algorithms, wherein the heatmap is indicative of at least the estimated utility expenditure associated with the at least one address during a predetermined period; and causing a display of a user device associated with the user to demonstrate the heatmap.
1. A computer-implemented method for estimating utility transactional data, comprising:
obtaining, via one or more processors, historical utility transactional data of one or more customers from one or more transactional entities;
determining, via the one or more processors and based on the historical utility transactional data, estimated transactional utility data of the one or more customers, each of the one or more customers being associated with a respective address within a geographical location, wherein:
determining the estimated utility transactional data includes using a trained machine learning model; and
the trained machine learning model has been trained, based on consecutive historical utility transactional data of one or more individuals, to predict the estimated utility transactional data in response to input of the historical utility transactional data and an input time;
generating, via the one or more processors and based on the determined estimated utility transactional data of the one or more customers, a heatmap indicative of the geographical location and of estimated utility transactional data corresponding to the respective addresses;
causing, via the one or more processors, a display of a computer system to output the generated heatmap via a graphical user interface;
receiving, via the graphical user interface, a user interaction specifying at least one of a specific address or a specific point in time; and
in response to receiving the user interaction, causing an update of the output of the heatmap based on the at least one specific address or specific point in time.
2. The computer-implemented method of claim 1 , wherein the estimated utility transactional data includes, for at least one of the one or more customers, one or more of at least one estimated customer preference, at least one estimated transaction time, or at least one estimated spending pattern.
3. The computer-implemented method of claim 1 , wherein the historical utility transactional data includes historical residence data of the one or more customers.
4. The computer-implemented method of claim 3 , wherein the historical residence data includes one or more residence demographic data for the one or more customers or residence behavior data of the one or more customers.
5. The computer-implemented method of claim 1 , wherein the estimated utility transactional data is associated with a predetermined period of time.
6. The computer-implemented method of claim 5 , wherein the predetermined period of time is a future period of time.
7. The computer-implemented method of claim 5 , wherein the predetermined period of time is a past period of time.
8. The computer-implemented method of claim 1 , wherein the heatmap is indicative of an estimated spending pattern of one or more of the customers.
9. A system for estimating utility transactional data, comprising:
at least one memory storing instructions; and
at least one processor operatively connected to the at least one memory, and configured to execute the instructions to perform operations, including:
obtaining historical utility transactional data of one or more customers from one or more transactional entities;
determining, based on the historical utility transactional data, estimated utility transactional data of the one or more customers, each of the one or more customers being associated with a respective address within a geographical location, wherein:
determining the estimated utility transactional data includes using a trained machine learning model; and
the trained machine learning model has been trained, based on consecutive historical utility transactional data of one or more individuals, to predict the estimated utility transactional data in response to input of the historical utility transactional data and an input time;
generating, based on the determined estimated utility transactional data of the one or more customers, a heatmap indicative of the geographical location and of estimated transactional utility data corresponding to the respective addresses;
causing a display of a computer system to output the generated heatmap via a graphical user interface;
receiving, via the graphical user interface, a user interaction specifying at least one of a specific address or a specific point in time; and
in response to receiving the user interaction, causing an update of the output of the heatmap based on the at least one specific address or specific point in time.
10. The system of claim 9 , wherein the estimated utility transactional data includes, for at least one of the one or more customers, one or more of at least one estimated customer preference, at least one estimated transaction time, or at least one estimated spending pattern.
11. The system of claim 9 , wherein:
the historical utility transactional data includes historical residence data of the one or more customers; and
the historical residence data includes one or more residence demographic data for the one or more customers or residence behavior data of the one or more customers.
12. The system of claim 9 , wherein the estimated utility transactional data is associated with a predetermined period of time.
13. The system of claim 12 , wherein the predetermined period of time is a future period of time.
14. The system of claim 12 , wherein the predetermined period of time is a past period of time.
15. The system of claim 9 , wherein the heatmap is indicative of an estimated spending pattern of one or more of the customers.
16. A computer-implemented method for estimating utility transactional data, comprising:
obtaining, via one or more processors, historical utility transactional data of one or more customers from one or more transactional entities;
determining, via the one or more processors and based on the historical utility transactional data, estimated utility transactional data of the one or more customers, each of the one or more customers being associated with a respective address within a geographical location, wherein:
determining the estimated utility transactional data includes using a trained machine learning model;
the trained machine learning model has been trained, based on training consecutive historical utility transactional data of one or more individuals, to predict the estimated utility transactional data in response to input of the historical utility transactional data and of an input time;
the estimated utility transactional data includes an estimated spending pattern of at least one of the one or more customers; and
the estimated utility transactional data is associated with a future period of time;
generating, via the one or more processors and based on the determined estimated utility transactional data of the one or more customers, a heatmap indicative of the geographical location and of estimated utility transactional data corresponding to the respective addresses;
causing, via the one or more processors, a display of a computer system to output the generated heatmap via a graphical user interface;
receiving, via the graphical user interface, a user interaction specifying at least one of a specific address or a specific point in time; and
in response to receiving the user interaction, causing an update of the output of the heatmap based on the at least one specific address or specific point in time.