CUSTOMIZING LOAN SPECIFICS ON A PER-USER BASIS
Techniques are disclosed to provide customized loans on a per-user basis. With user permission or affirmative consent, user data may be monitored for several users, which may be used to calculate initial loan specifics such as a loan rate and term based upon a portion of this user input data. The user data may include demographic data, behavioral data, or other data indicative of a user's future potential earnings or other relevant information that may be analyzed to determine, for that specific user, the current likelihood that the user will default on the loan and a future likelihood of default. When this future statistical likelihood is determined, the initial loan specific may be further modified and/or a targeted notification may be sent indicating these customized loan specifics.
1 . A computer-implemented method, comprising:
training, by one or more processors, a machine learning model to predict a set of historical earnings values associated with a set of users, wherein the training comprises determining at least one correlation between a set of factors within demographic profiles of the set of users and the set of historical earnings values;
receiving, by the one or more processors, a request for a loan for a user, the request indicating a loan amount and a loan term;
determining, by the one or more processors, an initial statistical risk of default on the loan, based at least in part on user profile data associated with the user that indicates a current earnings value of the user;
determining, by the one or more processors, an initial loan rate for the loan, based at least in part upon the initial statistical risk of default;
generating, by the one or more processors and using the machine learning model, a predicted future earnings value associated with the user over the loan term, based at least in part on whether the user profile data indicates that the user is currently enrolled in an educational program that the machine learning model predicts will cause an increase in earnings of the user, during the loan term, relative to the current earnings value of the user;
generating, by the one or more processors, an adjusted statistical risk of default on the loan by adjusting the initial statistical risk of default, based at least in part upon a difference between the current earnings value and the predicted future earnings value;
generating, by the one or more processors, an adjusted loan rate for the loan by adjusting the initial loan rate, based at least in part upon the adjusted statistical risk of default; and
causing, by the one or more processors, a computing device associated with the user to present the adjusted loan rate.
2 . (canceled)
3 . (canceled)
4 . The computer-implemented method of claim 1 , wherein generating the predicted future earnings value comprises:
identifying, by the one or more processors, a subset of the demographic profiles that is associated with other users who have completed the same educational program in which the user is currently enrolled; and
determining, by the one or more processors, the predicted future earnings value associated with the user based at least in part on an average of the historical earnings values associated with the other users.
5 . The computer-implemented method of claim 1 , wherein generating the adjusted loan rate comprises:
determining, by the one or more processors, that the predicted future earnings value is greater than the current earnings value and the difference between the current earnings value and the predicted future earnings value exceeds a threshold amount; and
generating, by the one or more processors, the adjusted loan rate by decreasing the initial loan rate.
6 . The computer-implemented method of claim 5 , wherein generating the adjusted loan rate by decreasing the initial loan rate comprises:
decreasing, by the one or more processors, the initial loan rate by an amount that is proportional to the difference between the current earnings value and the predicted future earnings value.
7 . The computer-implemented method of claim 1 , wherein causing the computing device to present the adjusted loan rate comprises:
causing, by the one or more processors, transmission of a notification to the computing device via wireless communication or data transmission over one or more radio links or wireless communication channels, and
wherein the notification causes a display associated with the computing device to present the adjusted loan rate.
8 . A computer system, comprising:
one or more processors;
memory storing computer-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
training a machine learning model to predict a set of historical earnings values associated with a set of users, wherein the training comprises determining at least one correlation between a set of factors within demographic profiles of the set of users and the set of historical earnings values;
receiving a request for a loan for a user, the request indicating a loan amount and a loan term;
determining an initial statistical risk of default on the loan, based at least in part on user profile data associated with the user that indicates a current earnings value of the user;
determining an initial loan rate for the loan, based at least in part upon the initial statistical risk of default;
generating, using the machine learning model, a predicted future earnings value associated with the user over the loan term, based at least in part on whether the user profile data indicates that the user is currently enrolled in an educational program that the machine learning model predicts will cause an increase in earnings of the user, during the loan term, relative to the current earnings value of the user;
generating an adjusted statistical risk of default on the loan by adjusting the initial statistical risk of default, based at least in part upon a difference between the current earnings value and the predicted future earnings value;
generating an adjusted loan rate for the loan by adjusting the initial loan rate, based at least in part upon the adjusted statistical risk of default; and
causing a client device associated with the user to present the adjusted loan rate.
9 . (canceled)
10 . (canceled)
11 . The computer system of claim 8 , wherein generating the predicted future earnings value comprises:
identifying a subset of the demographic profiles that is associated with other users who have completed the same educational program in which the user is currently enrolled, and
determining the predicted future earnings value associated with the user based at least in part on an average of the historical earnings values associated with the other users.
12 . The computer system of claim 8 , wherein generating the adjusted loan rate comprises:
determining that the predicted future earnings value is greater than the current earnings value and the difference between the current earnings value and the predicted future earnings value exceeds a threshold amount; and
generating the adjusted loan rate by decreasing the initial loan rate.
13 . The computer system of claim 12 , wherein generating the adjusted loan rate by decreasing the initial loan rate comprises:
decreasing the initial loan rate by an amount that is proportional to the difference between the current earnings value and the predicted future earnings value.
14 . The computer system of claim 8 , wherein causing the client device to present the adjusted loan rate comprises:
causing transmission of a notification to the client device via wireless communication or data transmission over one or more radio links or wireless communication channels, and
wherein the notification causes a display of the client device to present the adjusted loan rate.
15 . A non-transitory, tangible computer-readable medium storing instructions thereon, that, when executed by one or more processors, cause the one or more processors to:
train a machine learning model to predict a set of historical earnings values associated with a set of users, wherein training the machine learning model comprises determining at least one correlation between a set of factors within demographic profiles of the set of users and the set of historical earnings values;
receive a request for a loan for a user, the request indicating a loan amount and a loan term;
determine an initial statistical risk of default on the loan, based at least in part on user profile data associated with the user that indicates a current earnings value of the user;
determine an initial loan rate for the loan, based at least in part upon the initial statistical risk of default;
generating, using the machine learning model, a predicted future earnings value associated with the user over the loan term, based at least in part on whether the user profile data indicates that the user is currently enrolled in an educational program that the machine learning model predicts will cause an increase in earnings of the user, during the loan term, relative to the current earnings value of the user;
generating an adjusted statistical risk of default on the loan, based at least in part upon a difference between the current earnings value and the predicted future earnings value;
generating an adjusted loan rate for the loan by adjusting the initial loan rate, based at least in part upon the adjusted statistical risk of default; and
causing a client device associated with the user to present the adjusted loan rate.
16 . (canceled)
17 . (canceled)
18 . The non-transitory, tangible, computer-readable medium of claim 15 , wherein the instructions cause the one or more processors to generate the predicted future earnings value by:
identifying a subset of the demographic profiles that is associated with other users who have completed the same educational program in which the user is currently enrolled; and
determining the predicted future earnings value associated with the user based at least in part on an average of the historical earnings values associated with the other users.
19 . The non-transitory, tangible, computer-readable medium of claim 15 , wherein the instructions cause the one or more processors to generate the adjusted loan rate by:
determining that the predicted future earnings value is greater than the current earnings value and the difference between the current earnings value and the predicted future earnings value exceeds a threshold amount; and
generating the adjusted loan rate by decreasing the initial loan rate.
20 . The non-transitory, tangible, computer-readable medium of claim 19 , wherein generating the adjusted loan rate by decreasing the initial loan rate comprises:
decreasing the initial loan rate by an amount that is proportional to the difference between the current earnings value and the predicted future earnings value.
21 . The computer-implemented method of claim 1 , wherein the computing device is a mobile device of the user, and the one or more processors receive the request for the loan from the mobile device.
22 . The computer-implemented method of claim 1 , wherein generating the predicted future earnings value is further based at least in part on one or more predicted life events associated with the user, the one or more predicted life events being predicted by the machine learning model based at least in part on the user profile data.
23 . The computer-implemented method of claim 22 , wherein the one or more predicted life events includes at least one of: the user getting married, a child associated with the user attending college, the user paying off a previous loan, the user receiving a settlement, or the user receiving an inheritance.
24 . The computer system of claim 8 , wherein generating the predicted future earnings value is further based at least in part on one or more predicted life events associated with the user, the one or more predicted life events being predicted by the machine learning model based at least in part on the user profile data, the one or more predicted life events including at least one of: the user getting married, a child associated with the user attending college, the user paying off a previous loan, the user receiving a settlement, or the user receiving an inheritance.
25 . (canceled)
26 . The non-transitory, tangible, computer-readable medium of claim 15 , wherein the instructions cause the one or more processors to generate the predicted future earnings value further based at least in part on one or more predicted life events associated with the user, the one or more predicted life events being predicted by the machine learning model based at least in part on the user profile data, the one or more predicted life events including at least one of: the user getting married, a child associated with the user attending college, the user paying off a previous loan, the user receiving a settlement, or the user receiving an inheritance.
27 . The computer-implemented method of claim 1 , wherein the set of factors within the demographic profiles of the set of users includes educational programs associated with the set of users.