BASEL ADAPTIVE SEGMENTATION HEURISTICS

• US 20100049665A1
• Filed: 04/27/2009
• Published: 02/25/2010
• Est. Priority Date: 04/25/2008
• Status: Abandoned Application

• Current Assignee: Fair Isaac Corporation
• Original Assignee: Fair Isaac Corporation
• Inventors: RALPH, CHRISTOPHER ALLAN, Sullivan, Gary J., Sossi, Michael S.
• Application Number:
  US12/430,806
• Publication Number:
  US 20100049665A1
• Time in Patent Office: Days
• US Class Current: 705/36.R
• 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

Abstract

A system and method for identifying homogeneous risk pools used in the calculation of minimum capital requirements for a number of segments of a population of portfolios is presented. An F-ratio objective function representing a probability of a risk event across all of the number of segments of the population is calculated using an F-ratio objective function engine. An input dataset that defines a decision tree structure for the population is received. The F-ratio objective function of the risk event is maximized using a generic algorithm-based search engine to optimize the decision tree structure to group the number of segments according to one or more of the homogeneous risk pools, and a score for each homogeneous risk pool is then generated.

Claims

1. A system to identify homogeneous risk pools used in the calculation of minimum capital requirements for a number of segments of a population of portfolios, the system comprising:

a portfolio segmentation tool comprising;

an F-ratio objective function engine to calculate an F-ratio objective function representing a probability of a risk event across all of the number of segments of the population; and

a genetic algorithm-based search engine that receives an input dataset that defines a decision tree structure for the population, maximizes the F-ratio objective function of the risk event to optimize the decision tree structure to group the number of segments according to one or more of the homogeneous risk pools, and generates a score for each homogeneous risk pool.

2. The system in accordance with claim 1, further comprising a client computer that hosts the portfolio segmentation tool.

3. The system in accordance with claim 1, wherein the client computer includes an input device for receiving the input dataset, and a display for graphically displaying a representation of the score for each homogeneous risk pool.

4. The system in accordance with claim 1, wherein the F-ratio objective function represents an across-segment mean variance of the probability of the risk event, divided by a within-segment mean variance of the probability of the risk event.

5. The system in accordance with claim 1, wherein the probability of the risk event includes a probability of default of a portfolio.

6. The system in accordance with claim 1, further comprising:

a server system that hosts the portfolio segmentation tool; and

one or more client computers that access the portfolio segmentation tool via a communications network, each client computer including an input device for receiving the input dataset, and a display for graphically displaying a representation of the score for each homogeneous risk pool.

7. A method for identifying homogeneous risk pools used in the calculation of minimum capital requirements for a number of segments of a population of portfolios, the method comprising:

calculating an F-ratio objective function representing a probability of a risk event across all of the number of segments of the population using an F-ratio objective function engine;

receiving an input dataset that defines a decision tree structure for the population;

maximizing the F-ratio objective function of the risk event using a genetic algorithm-based search engine to optimize the decision tree structure to group the number of segments according to one or more of the homogeneous risk pools; and

generating a score for each homogeneous risk pool.

8. The method in accordance with claim 7, further comprising generating a graphical representation of the score.

9. The method in accordance with claim 7, wherein the F-ratio objective function represents an across-segment mean variance of the probability of the risk event, divided by a within-segment mean variance of the probability of the risk event.

10. The system in accordance with claim 7, wherein the probability of the risk event includes a probability of default of a portfolio.