Methods and system for assessing loss severity for commercial loans
First Claim
1. A method for predicting expected and unexpected loss outcomes for a portfolio of loans using a computer system coupled to a database, the loans issued by a lender to a plurality of borrowers, said method comprising the steps of:
- recording key account and risk attributes in the database for a historical portfolio of loans issued by the lender, the historical portfolio of loans includes loans issued to borrowers that have experienced a financial default with the lender and an associated economic loss;
recording actual default and loss information in the database for each borrower included within the historical portfolio of loans;
comparing the key account and risk attributes with the actual default and loss information over a predetermined period of time, the comparison is performed by the computer;
selecting a sample of loans from the historical portfolio of loans to determine loss drivers based on the comparison of the key account and risk attributes with the actual default and loss information, the loss drivers include the key account and risk attributes predictive of a loss;
building a regression tree based model representing relationships between the loss drivers, and expected and unexpected loss outcomes for the historical portfolio of loans, the regression tree based model is built using the computer system; and
predicting the expected and unexpected loss outcomes for a second portfolio of loans using the regression tree based model and the loss drivers for the second portfolio of loans, the second portfolio of loans includes borrowers that have not experienced a default with the lender.
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Abstract
A method for predicting expected and unexpected loss outcomes for a portfolio of loans is provided. The loans are issued by a lender to a plurality of borrowers. The method includes recording key account and risk attributes for a historical portfolio of loans, recording actual default and loss information for each borrower included within the historical portfolio of loans, and comparing the key account and risk attributes with the actual default and loss information over a period of time. The method also includes selecting a sample of loans from the historical portfolio of loans to determine loss drivers based on the comparison of the key account and risk attributes with the actual default and loss information, building a regression tree based model representing relationships between the loss drivers, and expected and unexpected loss outcomes for the historical portfolio of loans, and predicting the expected and unexpected loss outcomes for a second portfolio of loans using the regression tree based model and the loss drivers for the second portfolio of loans.
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Citations
30 Claims
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1. A method for predicting expected and unexpected loss outcomes for a portfolio of loans using a computer system coupled to a database, the loans issued by a lender to a plurality of borrowers, said method comprising the steps of:
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recording key account and risk attributes in the database for a historical portfolio of loans issued by the lender, the historical portfolio of loans includes loans issued to borrowers that have experienced a financial default with the lender and an associated economic loss;
recording actual default and loss information in the database for each borrower included within the historical portfolio of loans;
comparing the key account and risk attributes with the actual default and loss information over a predetermined period of time, the comparison is performed by the computer;
selecting a sample of loans from the historical portfolio of loans to determine loss drivers based on the comparison of the key account and risk attributes with the actual default and loss information, the loss drivers include the key account and risk attributes predictive of a loss;
building a regression tree based model representing relationships between the loss drivers, and expected and unexpected loss outcomes for the historical portfolio of loans, the regression tree based model is built using the computer system; and
predicting the expected and unexpected loss outcomes for a second portfolio of loans using the regression tree based model and the loss drivers for the second portfolio of loans, the second portfolio of loans includes borrowers that have not experienced a default with the lender. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A network-based system for predicting expected and unexpected loss outcomes for a portfolio of loans issued by a lender to a plurality of borrowers, said system comprising:
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a client system comprising a browser;
a centralized database for storing information; and
a server system configured to be coupled to said client system and said database, said server further configured to;
record key account and risk attributes in the database for a historical portfolio of loans issued by the lender, the historical portfolio of loans includes loans issued to borrowers that have experienced a financial default with the lender and an associated economic loss, record actual default and loss information in the database for each borrower included within the historical portfolio of loans, compare the key account and risk attributes with the actual default and loss information over a predetermined period of time, select a sample of loans from the historical portfolio of loans to determine loss drivers based on the comparison of the key account and risk attributes with the actual default and loss information, the loss drivers include the key account and risk attributes predictive of a loss, build a regression tree based model representing relationships between the loss drivers, and expected and unexpected loss outcomes for the historical portfolio of loans, and predict the expected and unexpected loss outcomes for a second portfolio of loans using the regression tree based model and the loss drivers for the second portfolio of loans, the second portfolio of loans includes borrowers that have not experienced a default with the lender. - View Dependent Claims (11, 12, 13, 14, 15, 16, 17, 18)
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19. A computer for predicting expected and unexpected loss outcomes for a portfolio of loans issued by a lender to a plurality of borrowers, said computer in communication with a database for storing information relating to each loan and each borrower, said computer programmed to:
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record key account and risk attributes in the database for a historical portfolio of loans issued by the lender, the historical portfolio of loans includes loans issued to borrowers that have experienced a financial default with the lender and an associated economic loss;
record actual default and loss information in the database for each borrower included within the historical portfolio of loans;
compare the key account and risk attributes with the actual default and loss information over a predetermined period of time;
select a sample of loans from the historical portfolio of loans to determine loss drivers based on the comparison of the key account and risk attributes with the actual default and loss information, the loss drivers include the key account and risk attributes predictive of a loss;
build a regression tree based model representing relationships between the loss drivers, and expected and unexpected loss outcomes for the historical portfolio of loans; and
predict the expected and unexpected loss outcomes for a second portfolio of loans using the regression tree based model and the loss drivers for the second portfolio of loans, the second portfolio of loans includes borrowers that have not experienced a default with the lender. - View Dependent Claims (20, 21, 22, 23, 24)
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25. A computer program embodied on a computer readable medium for predicting expected and unexpected loss outcomes for a portfolio of loans issued by a lender to a plurality of borrowers, said program comprising at least one code segment that prompts a user to input key account and risk attributes for a historical portfolio of loans issued by the lender and then:
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records the key account and risk attributes in a database, the historical portfolio of loans includes loans issued to borrowers that have experienced a financial default with the lender and an associated economic loss;
records actual default and loss information in the database for each borrower included within the historical portfolio of loans;
compares the key account and risk attributes with the actual default and loss information over a predetermined period of time;
selects a sample of loans from the historical portfolio of loans to determine loss drivers based on the comparison of the key account and risk attributes with the actual default and loss information, the loss drivers include the key account and risk attributes predictive of a loss;
builds a regression tree based model representing relationships between the loss drivers, and expected and unexpected loss outcomes for the historical portfolio of loans; and
predicts the expected and unexpected loss outcomes for a second portfolio of loans using the regression tree based model and the loss drivers for the second portfolio of loans, the second portfolio of loans includes borrowers that have not experienced a default with the lender. - View Dependent Claims (26, 27, 28, 29, 30)
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Specification