Methods and Systems for Financial Transactions
Abstract
Relationship banking and mobile banking are discussed and presented here. In Section 1, we present Pre-approval, Fulfillment, and Application Process. In Section 2, we present Financial Products for Protection of Consumers. In Section 3, we present Relationship-Based Score. In Section 4, we present Application of Credit Report for a “Binding” Pre-Approval for Lending Products. In Section 5, we present Deposit Slip Purchase. In Section 6, we present Reducing Frauds on Credit Cards. We also discuss the system and components, with different variations on system and method, or their designs. Sections 7-9 discuss other applications and examples. The relationship-based score for loans and credit lines is also discussed.
Claims
exact text as granted — not AI-modified1 . A loan product approval method by a lender, said method comprising:
a lender computing device receiving a loan application, regarding a customer, from a lender application receiving device; said lender computing device receiving credit information regarding said customer from one or more credit bureau databases; wherein said credit information comprises one or more credit scores; a ranking and calculating device assigning a first weight to said credit information; a segmentation analyzer performing segmentation analysis based on a first sub-population of potential loan applicants; wherein said first characteristic of said customer is predictive in isolating risk for said first sub-population of potential loan applicants; optimizing said first characteristic of said customer for said first sub-population of potential loan applicants; said lender computing device down-sampling said potential loan applicants; said lender computing device selecting credit variables based on said down-sampled potential loan applicants; said lender computing device applying a regression technique, to reduce a number of said credit variables; a modeling processor fitting a first credit model, based on said reduced number of said credit variables, for said first characteristic; said modeling processor fitting a second credit model, based on said reduced number of said credit variables, for said second characteristic; using a rejection inference technique to improve said first credit model; validating said improved first credit model, based on test data points; using a weight of evidence analysis to modify said improved first credit model; approving or disapproving said loan application for said customer, by a loan product package processing device, based on said one or more credit scores for said customer; assigning an interest rate to said loan application by a rate determination device; and notifying said customer about outcome of said loan application.
2 . The loan product approval method by a lender as recited in claim 1 , wherein said loan application is related to one or more of the following: automobile, motorcycle, bike, RV, marine, unsecured installment, credit card, mortgage, home equity installment, or home equity line of credit.
3 . The loan product approval method by a lender as recited in claim 1 , wherein said loan product package processing device considers one or more of the following: income, own or rent, housing expenditure, collateral type, collateral value, requested amount, down payment, estimated closing cost, or information about said customer.
4 . The loan product approval method by a lender as recited in claim 1 , wherein performance windows for observations are the same.
5 . The loan product approval method by a lender as recited in claim 1 , wherein performance windows are staggered.
6 . The loan product approval method by a lender as recited in claim 1 , said method further comprising: assigning one or more future interest rates.
7 . The loan product approval method by a lender as recited in claim 1 , said method further comprising: assigning one or more future interest rates, with corresponding conditions.
8 . The loan product approval method by a lender as recited in claim 1 , said method further comprising:
said ranking and calculating device taking a weighted average.
9 . The loan product approval method by a lender as recited in claim 1 , said method further comprising: updating said score for said customer, periodically.
10 . The loan product approval method by a lender as recited in claim 1 , said method further comprising: updating said score for said customer, based on a predetermined or trigger event.
11 . The loan product approval method by a lender as recited in claim 1 , said method further comprising: updating said score for said customer, based on a predetermined market index.
12 . The loan product approval method by a lender as recited in claim 1 , said method further comprising: updating said score for said customer, based on a random variable or based on random time intervals.
13 . The loan product approval method by a lender as recited in claim 1 , said method further comprising: classifying said customer, in terms of a preferred interest rate.
14 . The loan product approval method by a lender as recited in claim 1 , said method comprising: assigning a first weight to said score.
15 . The loan product approval method by a lender as recited in claim 1 , said method comprising: analyzing based on first sub-population.
16 . The loan product approval method by a lender as recited in claim 1 , said method comprising: analyzing based on first characteristics.
17 . The loan product approval method by a lender as recited in claim 1 , said method comprising: analyzing based on performance window.
18 . The loan product approval method by a lender as recited in claim 1 , said method comprising: analyzing based on observation window.
19 . The loan product approval method by a lender as recited in claim 1 , said method comprising: fitting a credit model.
20 . The loan product approval method by a lender as recited in claim 1 , said method comprising: using a rejection inference technique to improve credit model.Cited by (0)
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