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Activity Number: 168 - Risk analysis and related topics
Type: Contributed
Date/Time: Tuesday, August 10, 2021 : 10:00 AM to 11:50 AM
Sponsor: Section on Risk Analysis
Abstract #318737
Title: Practical Reject Inference Methodology in Credit Risk Modeling
Author(s): Atrijit Ghosh* and Xuejing Mao and Hari Sunder and Jeff Louallen
Companies: AT&T Inc and AT&T Inc. and AT&T Inc. and AT&T Inc.
Keywords: Reject inference; Machine Learning; Credit Risk Model; Parceling; Fuzzy; Hard cutoff
Abstract:

In telecommunication companies, credit risk models are built to help determine (any and how much) mitigation a customer needs to pay for services requested. However, customers who walk away can’t be included in modeling. It might be highly misleading to solely base the credit decisions on behaviors and characteristics of accepted applicants. Reject inference can be used to solve this problem and improve the quality of credit scoring models. In this paper, we will brief introduce the three methods (parceling, fuzzy and hard cutoff) and then discuss how we conducted reject inferencing for a machine learning credit model project.


Authors who are presenting talks have a * after their name.

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