JSM 2005 - Toronto

Abstract #304790

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Activity Number: 68
Type: Contributed
Date/Time: Sunday, August 7, 2005 : 4:00 PM to 5:50 PM
Sponsor: Section on Risk Analysis
Abstract - #304790
Title: On the Application of Zero-Inflated Count Models to Business Delinquency Prediction
Author(s): Edgar Ortiz*+ and Paul Chin
Companies: Dun & Bradstreet and Dun & Bradstreet Global Decision Sciences
Address: 103 JFK Parkway, Short Hills, NJ, 07078, United States
Keywords: Account Delinquency ; Count Models ; Zero-Inflated Poisson ; Zero-Inflated Negative Binomial ; Portfolio Management ; Risk Management
Abstract:

This paper introduces the application of Zero-Inflated Count models to analyze and predict account delinquency. By and large, binary logistic regression is the statistical model of choice to predict account delinquency. Using this approach typically results in loss information in that the severity of delinquency is clustered into a single class that fails to distinguish the number of times an account has reached the delinquency state. When monitoring portfolio performance, it is not only important to know whether an account will become delinquent, but also to be able to predict the severity or frequency of occurrence of the event. Using this information, lending institutions can be proactive in setting risk-based strategies that match the level of risk across accounts and segments in their portfolios. This paper presents and contrasts results from the application of Zero-Inflated Poisson, Zero-Inflated Negative Binomial, and binary logistic regression to model account delinquency in a large population of business accounts extracted from Dun & Bradstreet Commercial database.

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Revised March 2005