METHODS AND SYSTEMS FOR FINANCIAL SIMULATIONS USING A MACHINE LEARNING MODEL
Using various embodiments, systems, methods, and techniques are disclosed to perform financial simulations using a machine learning model are disclosed. In one embodiment, a system receives credit data of a user and a request to perform a financial simulation of a financial profile pertaining to a consumer. The credit data is aggregated to determine one or more features required by an AI/ML model, and then submits the aggregated data to the model. The system then returns a prediction based on the financial simulation provided by the ML model.
1 . A method comprising:
receiving, by a computer, a request to perform at least one financial simulation of a financial profile pertaining to a consumer, wherein the request includes metadata that is required to perform the at least one financial simulation;
receiving a credit data of the consumer, wherein the credit data includes at least one of a credit score, tradeline, credit inquiry, or a public record of the consumer;
aggregating the credit data of the consumer to determine one or more features required by the ML model;
submitting the aggregated data to a Machine Learning (ML) model, wherein the ML model was trained using credit profiles of a plurality of consumers; and
generating a prediction related to the at least one financial simulation.
2 . The method of claim 1 , wherein the at least one financial simulation includes determining an impact of at least one action comprising: being denied for a credit product while sustaining a hard credit inquiry, getting a new credit card, getting a new personal loan, making a change in credit card balance or utilization, resolving a negative mark such as a collection, or taking on a new delinquency.
3 . The method of claim 1 , wherein the prediction determines both a direction and a magnitude of the user's credit score change under the at least one action.
4 . The method of claim 1 , wherein the aggregating includes creating a feature array that is submitted to the ML model.
5 . The method of claim 1 , wherein the prediction includes a trajectory of a financial condition of the user over a predetermined time period.
6 . The method of claim 5 , wherein the predetermined time period is six months.
7 . The method of claim 5 , wherein the predetermined time period is calculated by a difference between a first credit data pull date and a second credit data pull date of the user.
8 . A non-transitory computer readable medium comprising instructions, which when executed by a processing device, executes a method comprising:
receiving a request to perform at least one financial simulation of a financial profile pertaining to a consumer, wherein the request includes metadata that is required to perform the at least one financial simulation;
receiving a credit data of the consumer, wherein the credit data includes at least one of a credit score, tradeline, credit inquiry, or a public record of the consumer;
aggregating the credit data of the consumer to determine one or more features required by the ML model;
submitting the aggregated data to a Machine Learning (ML) model, wherein the ML model was trained using credit profiles of a plurality of consumers; and
generating a prediction related to the at least one financial simulation.
9 . The non-transitory computer readable medium of claim 8 , wherein the at least one financial simulation includes determining an impact of at least one action comprising: being denied for a credit product while sustaining a hard credit inquiry, getting a new credit card, getting a new personal loan, making a change in credit card balance or utilization, resolving a negative mark such as a collection, or taking on a new delinquency.
10 . The non-transitory computer readable medium of claim 8 , wherein the prediction determines both a direction and a magnitude of the user's credit score change under the at least one action.
11 . The non-transitory computer readable medium of claim 8 , wherein the aggregating includes creating a feature array that is submitted to the ML model.
12 . The non-transitory computer readable medium of claim 8 , wherein the prediction includes a trajectory of a financial condition of the user over a predetermined time period.
13 . The non-transitory computer readable medium of claim 12 , wherein the predetermined time period is six months.
14 . The non-transitory computer readable medium of claim 12 , wherein the predetermined time period is calculated by a difference between a first credit data pull date and a second credit data pull date of the user.
15 . A system comprising:
a memory device;
a processor, coupled to the memory device, wherein the processor is configured to:
receive a request to perform at least one financial simulation of a financial profile pertaining to a consumer, wherein the request includes metadata that is required to perform the at least one financial simulation;
receive a credit data of the consumer, wherein the credit data includes at least one of a credit score, tradeline, credit inquiry, or a public record of the consumer;
aggregate the credit data of the consumer to determine one or more features required by the ML model;
submit the aggregated data to a Machine Learning (ML) model, wherein the ML model was trained using credit profiles of a plurality of consumers; and
generate a prediction related to the at least one financial simulation.
16 . The system of claim 15 , wherein the at least one financial simulation includes determining an impact of at least one action comprising: being denied for a credit product while sustaining a hard credit inquiry, getting a new credit card, getting a new personal loan, making a change in credit card balance or utilization, resolving a negative mark such as a collection, or taking on a new delinquency.
17 . The system of claim 15 , wherein the prediction determines both a direction and a magnitude of the user's credit score change under the at least one action.
18 . The system of claim 15 , wherein the aggregate includes creating a feature array that is submitted to the ML model.
19 . The system of claim 15 , wherein the prediction includes a trajectory of a financial condition of the user over a predetermined time period, wherein the predetermined time period is calculated by a difference between a first credit data pull date and a second credit data pull date of the user.
20 . The system of claim 19 , wherein the predetermined time period is six months.