Risk-based machine learning classifier
The present disclosure relates generally to a risk-based fraud identification and risk analysis system. For example, the system may receive application data from a first borrower user, determine a segment associated with the application data, apply application data to one or more machine learning (ML) models, and receive a score based at least in part upon output of the ML model.
1. A method for computing an application score for a loan, the method comprising:
receiving, by a computer system, application data identifying a plurality of attributes of: a first borrower, and of collateral;
generating, by the computer system, a plurality of input features based upon the application data, wherein at least one of the plurality of input features are generated based on at least one attribute of the collateral;
applying the plurality of input features to a trained machine learning (ML) model to generate an output;
determining an application score based on the output of the trained ML model;
grouping each of the plurality of input features into one of a plurality of factor groups, wherein each of the plurality of factor groups is related to a different type of fraud;
determining a reason for how each of the input features in a factor group affects the application score;
determining, by the computer system, one or more actions for the application data based at least in part on the application score, the plurality of factor groups, and the determined reason; and
providing, by the computer system, the application score and the one or more actions to a dealer user device or to a lender user device.
2. The method of claim 1 , wherein the application data is received from a borrower user device.
3. The method of claim 1 , wherein the application data is received from a borrower user device and from the dealer user device.
4. The method of claim 3 , wherein the plurality of attributes of collateral are received in application data from the dealer user device.
5. The method of claim 1 , wherein the at least one of the one or more input features generated based on the at least one attribute of the collateral is generated based on a value discrepancy between a value of collateral and a loan amount.
6. The method of claim 5 , wherein the collateral comprises an automobile.
7. The method of claim 6 , wherein the application data identifies a value of the automobile.
8. The method of claim 7 , further comprising: identifying an expected value for the automobile based on attributes of the automobile; and comparing the expected value for the automobile with the identified value of the automobile in the application data.
9. The method of claim 8 , further comprising generating a feature characterizing the comparison of the expected value for the automobile with the identified value of the automobile in the application data.
10. The method of claim 9 , further comprising: determining a second action based on the comparison of the expected value for the automobile with the identified value of the automobile in the application data; and providing the second action to the dealer user device or to the lender user device.
11. A system comprising:
one or more processors; and
a non-transitory computer-readable medium including instructions that, when executed by the one or more processors, cause the one or more processors to:
receive application data identifying a plurality of attributes of: a first borrower, and of collateral;
generate a plurality of input features based upon the application data, wherein at least one of the a plurality of input features are generated based on at least one attribute of the collateral;
apply the a plurality of input features to a trained machine learning (ML) model to generate an output;
determine an application score based on the output of the trained ML model;
group each of the plurality of input features into one of a plurality of factor groups, wherein each of the plurality of factor groups is related to a different type of fraud;
determine a reason for how each of the input features in a factor group affects the application score;
determine one or more actions for the application data based at least in part on the application score, the plurality of factor groups, and the determined reason; and
provide the application score and the one or more actions to a dealer user device or to a lender user device.
12. The system of claim 11 , wherein the application data is received from a borrower user device.
13. The system of claim 11 , wherein the application data is received from a borrower user device and from the dealer user device.
14. The system of claim 13 , wherein the plurality of attributes of collateral are received in application data from the dealer user device.
15. The system of claim 11 , wherein the at least one of the one or more input features generated based on the at least one attribute of the collateral is generated based on a value discrepancy between a value of collateral and a loan amount.
16. The system of claim 15 , wherein the collateral comprises an automobile.
17. The system of claim 16 , wherein the application data identifies a value of the automobile.
18. The system of claim 17 , wherein the non-transitory computer-readable medium further includes instructions that, when executed by the one or more processors, cause the one or more processors to: identify an expected value for the automobile based on attributes of the automobile; and compare the expected value for the automobile with the identified value of the automobile in the application data.
19. The system of claim 18 , wherein the non-transitory computer-readable medium further includes instructions that, when executed by the one or more processors, cause the one or more processors to generate a feature characterizing the comparison of the expected value for the automobile with the identified value of the automobile in the application data.
20. The system of claim 19 , wherein the non-transitory computer-readable medium further includes instructions that, when executed by the one or more processors, cause the one or more processors to: determine a second action based on the comparison of the expected value for the automobile with the identified value of the automobile in the application data; and provide the second action to the dealer user device or to the lender user device.