METHOD AND APPARATUS FOR MODELING ECONOMIC CONDITIONS AS APPLIED TO MULTIPLE RISK GRADES

• US 20100125532A1
• Filed: 11/20/2008
• Published: 05/20/2010
• Est. Priority Date: 11/20/2008
• Status: Abandoned Application

• Current Assignee: Fair Isaac Corporation
• Original Assignee: Fair Isaac Corporation
• Inventors: Lian, Chenyang, COHEN, MICHAEL, Sullivan, Gary J.
• Application Number:
  US12/275,017
• Publication Number:
  US 20100125532A1
• Time in Patent Office: Days
• US Class Current: 705/36.R
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes
• CPC Class Codes: G06Q 40/02 Banking, e.g. interest calculation or account maintenance credit or loans G06Q40/03
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis
• CPC Class Codes: G06Q 40/08 Insurance

Abstract

A computerized method includes scoring a plurality of loans, and banding the plurality of loans into risk pools on the basis of the scores associated with the plurality of loans. The computerized method also includes modeling a change in y-intercept and slope of the natural log of the odds to the loan scores relationship, using that predicted log odds to calculate the probability of default for the plurality of risk pools over time as a function of a set of macro-economic data. A machine readable medium provides instructions that, when executed by a machine, cause the machine to perform the above on a system for determining an amount of capital to hold in reserve for a plurality of loan risk pools and to set strategies for managing risk for a plurality of risk pools.

Claims

1. A computerized method comprising:

scoring a plurality of loans;

banding the plurality of loans into risk pools on the basis of the scores associated with the plurality of loans;

modeling the log odds associated with a plurality of loan scores as a linear function of the loan score wherein the y-intercept and slope accounts for changes, including economic changes, that effect the natural log of the odds;

fitting the log of the odds to loan score relationship at several points in time, to obtain intercept and slope statistics at each point in time;

producing models of the how the slope and y-intercept change with regard to economic conditions;

predicting the odds in a plurality of risk pools under any current or assumed future economic conditions using the predicted slope and intercept and the log odds to score relationship; and

setting a reserve level for a plurality of risk pools using the predicted log odds to score relationship.

2. The computerized method of claim 1 wherein producing models of the how the slope and y-intercept change with regard to economic conditions further comprises:

calculating the average slope over time;

assuming that this average slope is a fixed slope in the odds to score relationship over all points in time;

obtaining the best fit intercept at each point in time using this average slope; and

modeling the changes in the y-intercept obtained based on the fixed slope.

3. The computerized method of claim 1 wherein producing models of the how the slope and y-intercept change with regard to economic conditions further comprises:

calculating the average intercept over time;

assuming that this average intercept is a fixed intercept in the odds to score relationship over all points in time;

obtaining the best fit slope at each point in time using this average intercept; and

modeling the changes in the slope obtained based on this fixed y-intercept.

4. The computerized method of claim 1 wherein producing models of the how the slope and y-intercept change with regard to economic conditions further comprises:

translating the slope and intercept changes into translational and rotational changes over time;

modeling of the change in translational and rotational components as a function of a set of macro-economic data;

obtaining the slope and intercept as functions of economic data from the translational and rotational models; and

predicting the intercept and slope under various economic conditions using the model for the change in translational and rotational components as a function of a set of macro-economic data.

5. The computerized method of claim 1 wherein the predicted odds associated with a plurality of loan scores is expressed as a linear function relating the natural log of the odds to a risk score on the loans in the plurality of risk pools.

6. The computerized method of claim 1 wherein fitting the log of the odds to score relationship includes using a linear regression.

7. The computerized method of claim 1 wherein fitting the log of the odds to score relationship includes using a logistic regression.

8. The computerized method of claim 1 wherein the set of macro-economic data includes measures of the Gross Domestic Product (GDP).

9. The computerized method of claim 1 wherein the set of macro-economic data includes a set of interest rates over time.

10. The computerized method of claim 1 wherein the set of macro-economic data includes a set of unemployment rates.

11. The computerized method of claim 1 wherein the set of macro-economic data includes a set of personal savings rates.

12. A machine readable medium that provides instructions that, when executed by a machine, cause the machine to:

score a plurality of loans;

band the plurality of loans into risk pools on the basis of the scores associated with the plurality of loans;

model the log odds associated with a plurality of loan scores as a linear function of the loan score wherein the y-intercept and slope accounts for changes, including economic changes, that effect the natural log of the odds;

fit the log of the odds to loan score relationship at several points in time, to obtain intercept and slope statistics at each point in time;

produce models of the how the slope and y-intercept change with regard to economic conditions;

predict the odds in a plurality of risk pools under any current or assumed future economic conditions using the predicted slope and intercept and the log odds to score relationship; and

set a reserve level for a plurality of risk pools using the predicted log odds to score relationship.

13. The machine readable medium of claim 12 wherein the instructions to model the log odds further cause the machine to:

calculate the average slope over time;

assume that this average slope is a fixed slope in the odds to score relationship over all points in time;

obtain the best fit intercept at each point in time using this average slope; and

model the changes in the y-intercept obtained based on the fixed slope.

14. The machine readable medium of claim 12 wherein the instructions to model the log odds further cause the machine to:

calculate the average intercept over time;

assume that this average intercept is a fixed intercept in the odds to score relationship over all points in time;

obtain the best fit slope at each point in time using this average intercept; and

model the changes in the slope obtained based on this fixed y-intercept.

15. The machine readable medium of claim 12 wherein the instructions to model the log odds further cause the machine to:

translate the slope and intercept changes into translational and rotational changes over time;

model of the change in translational and rotational components as a function of a set of macro-economic data;

obtain the slope and intercept as functions of economic data from the translational and rotational models; and

predict the intercept and slope under various economic conditions using the model for the change in translational and rotational components as a function of a set of macro-economic data.

16. The computerized method of claim 12 wherein the predicted odds associated with a plurality of loan scores is expressed as a linear function relating the natural log of the odds to a risk score on the loans in the plurality of risk pools.

17. A system for determining an amount of capital to hold in reserve for a plurality of loan risk pools, the system comprising:

a first model component for modeling the log odds associated with the plurality of loan scores as a linear function of the loan scores, the y-intercept and slope of the linear function accounting for changes, including economic changes, that effect the natural log of the odds;

a second model component for producing a model of the change in intercept and slope over time as a function of a set of macro-economic data;

a fit component for fitting the log of the odds to loan score relationship at several points in time, to obtain intercept and slope statistics at each point in time; and

a prediction component for predicting the odds in a plurality of risk pools under current or future predicted economic conditions using the predicted intercept and slope and the odds to score relationship; and

a reserve level component for setting a reserve level for at least one risk pool.

18. The system for determining an amount of capital to hold in reserve for a plurality of loan risk pools of claim 17 wherein the reserve level component sets reserve levels in at least two of the plurality of risk pools.

19. A computerized method comprising:

scoring a plurality of loans;

banding the plurality of loans into risk pools on the basis of the scores associated with the plurality of loans;

modeling the log odds associated with a plurality of loan scores as a linear function of the loan score wherein the y-intercept and slope accounts for changes, including economic changes, that effect the natural log of the odds;

fitting the log of the odds to loan score relationship at several points in time, to obtain intercept and slope statistics at each point in time;

producing models of the how the slope and y-intercept change with regard to economic conditions;

predicting the odds in a plurality of risk pools under any current or assumed future economic conditions using the predicted slope and intercept and the log odds to score relationship; and

using the predicted log odds to make strategic portfolio decisions.

20. The computerized method of claim 19 wherein the strategic portfolio decision is to use the expected odds to score relationship to proactively realign the risk score based on this expected odds to score relationship.

21. The computerized method of claim 19 wherein the strategic portfolio decision is to modify acquisition strategies based on the expected future odds to score relationship.

22. The computerized method of claim 19 wherein the strategic portfolio decision is to modify account management strategies.