SYSTEMS AND METHODS FOR DETERMINING CREDIT WORTHINESS OF A BORROWER
First Claim
Patent Images
1. A method comprising:
- receiving, from a user, a request for a loan, wherein the request comprises a loan amount;
retrieving a description of a plurality of transactions performed by the user;
determining, for each transaction of the plurality of transactions, one of a plurality of categories corresponding to the respective transaction;
determining, for each category of the plurality of categories, a total amount spent corresponding to the respective category and a total amount of transactions corresponding to the respective category;
determining, by a first machine learning algorithm (MLA) and based on the loan amount, the total amount spent in each category, and the total amount of transactions for each category, a predicted likelihood that the loan will be approved,wherein the first MLA was trained based on loan data corresponding to a plurality of users and transaction data corresponding to the plurality of users;
determining, by a second MLA and based on the loan amount, the total amount spent in each category, and the amount of transactions for each category, a predicted likelihood that the loan will be repaid,wherein the second MLA was trained based on the loan data corresponding to the plurality of users and the transaction data corresponding to the plurality of users;
determining whether to approve the request for the loan based on the predicted likelihood that the loan will be approved and the predicted likelihood that the loan will be repaid; and
outputting an indication of whether the loan was approved.
0 Assignments
0 Petitions
Accused Products
Abstract
There is disclosed a method and system for determining the credit worthiness of a borrower. The method comprises receiving a loan application from a prospective borrower. Transaction history for the prospective borrower is retrieved. A category is determined for each transaction in the transaction history. Transaction data metrics are determined for each category. A first machine learning algorithm (MLA) uses the transaction data metrics to predict a likelihood that the loan application will be approved. A second MLA uses the transaction data metrics to predict a likelihood that the loan will be repaid. The loan is approved or denied based on the predicted likelihoods.
17 Citations
20 Claims
-
1. A method comprising:
-
receiving, from a user, a request for a loan, wherein the request comprises a loan amount; retrieving a description of a plurality of transactions performed by the user; determining, for each transaction of the plurality of transactions, one of a plurality of categories corresponding to the respective transaction; determining, for each category of the plurality of categories, a total amount spent corresponding to the respective category and a total amount of transactions corresponding to the respective category; determining, by a first machine learning algorithm (MLA) and based on the loan amount, the total amount spent in each category, and the total amount of transactions for each category, a predicted likelihood that the loan will be approved, wherein the first MLA was trained based on loan data corresponding to a plurality of users and transaction data corresponding to the plurality of users; determining, by a second MLA and based on the loan amount, the total amount spent in each category, and the amount of transactions for each category, a predicted likelihood that the loan will be repaid, wherein the second MLA was trained based on the loan data corresponding to the plurality of users and the transaction data corresponding to the plurality of users; determining whether to approve the request for the loan based on the predicted likelihood that the loan will be approved and the predicted likelihood that the loan will be repaid; and outputting an indication of whether the loan was approved. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
-
-
14. A method comprising:
-
receiving, from a user, a request for a loan, wherein the request comprises a loan amount; retrieving a description of a plurality of transactions performed by the user; determining, for each transaction of the plurality of transactions, one of a plurality of categories corresponding to the respective transaction; determining, for each category of the plurality of categories, a total amount spent corresponding to the respective category and a total amount of transactions corresponding to the respective category; determining, by a first machine learning algorithm (MLA) and based on the loan amount, the total amount spent in each category, and the total amount of transactions for each category, a predicted likelihood that the loan will be approved, wherein the first MLA was trained based on loan data corresponding to a plurality of users and transaction data corresponding to the plurality of users; determining, by a second MLA and based on the loan amount, the total amount spent in each category, and the amount of transactions for each category, a predicted likelihood that the loan will be repaid, wherein the second MLA was trained based on the loan data corresponding to the plurality of users and the transaction data corresponding to the plurality of users; determining, based on the predicted likelihood that the loan will be approved and the predicted likelihood that the loan will be repaid, a recommendation to approve or deny the loan; and outputting for display the recommendation. - View Dependent Claims (15, 16)
-
-
17. A method for training a machine learning algorithm (MLA) to predict the likelihood that a loan will be repaid, the method comprising:
-
retrieving historic loan data corresponding to a plurality of users, each entry in the historic loan data indicating a loan amount, a status of the loan, and an identifier of a user of the plurality of users; retrieving historic transaction data corresponding to the plurality of users, each transaction in the historic transaction data indicating an amount of the respective transaction and a description of the respective transaction; determining, for each transaction in the historic transaction data, a category, of a plurality of categories, corresponding to the respective transaction, thereby generating categorized historic transaction data; determining, based on the categorized transaction data, transaction data metrics for each user of the plurality of users, wherein the transaction data metrics comprise a count of transactions and a sum of transaction amounts for each category of the plurality of categories; and training, based on the historic loan data and the transaction data metrics, the MLA. - View Dependent Claims (18, 19, 20)
-
Specification