Apparatus and method for generating a pecuniary program
An apparatus and method for generating a pecuniary program, the apparatus including a computing device, configured to receive a user input relating to a user; receive pecuniary data relating to a user; identify a plurality of trends in the pecuniary data; generate a first training data set including: at least a priority scoring criteria; and a plurality of a plurality of identified trends in pecuniary data relating to the user; classify at least an element of the user input to a priority score using a first machine learning model, wherein classifying the at an element of the user input includes training a first machine machine-learning model, as a function of the first training data set, and generate a pecuniary program for the user as a function of the priority score.
1. An apparatus for generating a pecuniary program, the apparatus comprising a computing device, wherein the computing device comprises:
at least a processor; and
a memory communicatively connected to the at least processor, the memory containing instructions configuring the at least processor to:
receive a user input relating to a user;
receive pecuniary data relating to the user;
identify a plurality of trends in the pecuniary data;
generate a first training data set comprising:
at least a priority scoring criteria; and
a plurality of a plurality of identified trends in the pecuniary data relating to the user;
classify at least an element of the user input to a priority score using a first machine-learning model, wherein classifying the at least an element of the user input comprises training a first machine machine-learning model, wherein training the first machine learning model comprises:
correlating a plurality of data entries containing a plurality of user inputs as inputs correlated to a plurality of priority scores as outputs;
updating the first training data set with input and output results from the first machine machine-learning model; and
retraining the first machine machine-learning with an updated first training data set; and
generate a pecuniary program for the user as a function of the priority score.
2. The apparatus of claim 1 , wherein the user input includes a goal timeline.
3. The apparatus of claim 1 , wherein identifying trends in pecuniary data includes using a language processing model.
4. The apparatus of claim 1 , wherein the identified trends in pecuniary data represent a negative pecuniary history of the user.
5. The apparatus of claim 1 , wherein the pecuniary program includes a plurality of target sets for user selection.
6. The apparatus of claim 5 , wherein each target set of the plurality of target sets includes educational data relating to pecuniary literacy.
7. The apparatus of claim 5 , wherein each target set of the plurality of target sets is ranked based on achievability criteria.
8. The apparatus of claim 7 , wherein ranking the plurality of target sets comprises fuzzy set comparison.
9. The apparatus of claim 1 , wherein the memory contains instructions further configuring the at least a processor to receive user feedback relating to the pecuniary program.
10. The apparatus of claim 9 , wherein the memory contains instructions further configuring the at least a processor to generate an updated pecuniary program as a function of user feedback.
11. A method for generating a pecuniary program, the method comprising:
receiving, using a computing device, a user input relating to a user;
receiving using a computing device, pecuniary data relating to a user;
identifying, using the computing device, trends in pecuniary data;
generating, using the computing device, a first training data set comprising:
at least a priority scoring criteria; and
a plurality of identified trends in pecuniary data relating to the user;
classifying, using the computing device, at least an element of the user input to a priority score using a first machine-learning model, wherein classifying the at least an element of the user input comprises training a first machine machine-learning model, wherein training the first machine learning model comprises:
correlating a plurality of data entries containing a plurality of user inputs as inputs correlated to a plurality of priority scores as outputs;
updating the first training data set with input and output results from the first machine machine-learning model; and
retraining the first machine machine-learning with an updated first training data set; and
generating, using the computing device, a pecuniary program for the user as a function of the priority score.
12. The method of claim 11 , wherein the user input includes a goal timeline.
13. The method of claim 11 , wherein identifying trends in pecuniary data comprises using a language processing model.
14. The method of claim 11 , wherein the identified trends in pecuniary data represent a negative pecuniary history of the user.
15. The method of claim 11 , wherein the pecuniary program includes a plurality of target sets for user selection.
16. The method of claim 15 , wherein each target set of the plurality of target sets includes educational data relating to pecuniary literacy.
17. The method of claim 15 , wherein each target set of the plurality of target sets is ranked based on achievability criteria.
18. The method of claim 17 , wherein ranking the plurality of target sets comprises fuzzy set comparison.
19. The method of claim 11 , further comprising receiving, using the computing device, user feedback relating to the pecuniary program.
20. The method of claim 19 , further comprising generating, using the computing device, an updated pecuniary program as a function of user feedback.