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To ask or not to ask: that is the question

To ask or not to ask: that is the question
To ask or not to ask: that is the question
Applicants for credit have to provide information for the risk assessment process. In the current conditions of a saturated consumer lending market, and hence falling “take” rates, can such information be used to assess the probability of a customer accepting the offer?
With the advent of internet broking pages, which allow borrowers to “apply” to a number of different companies at the same time, this “take” problem will increase. In some mortgage markets, it is quite common for more than 50% of those offered credit to reject it. In some cases, this is because the sale falls through but often it is because a relatively better retailer has offered a more suitable product to the borrower.
Lenders do not want to make the application process too complicated, and with the growth in adaptive marketing channels like the Internet and the telephone, they can make the questions they ask depend on the previous answers. We investigate how one could develop such “adaptive” application forms; which would assess acceptance probabilities.
data mining, classification trees, question selection
0377-2217
1513-1520
Seow, Hsin-Vonn
06d4e4e7-fd16-4781-b3c9-39a3b2bdafd1
Thomas, Lyn C.
a3ce3068-328b-4bce-889f-965b0b9d2362
Seow, Hsin-Vonn
06d4e4e7-fd16-4781-b3c9-39a3b2bdafd1
Thomas, Lyn C.
a3ce3068-328b-4bce-889f-965b0b9d2362

Seow, Hsin-Vonn and Thomas, Lyn C. (2007) To ask or not to ask: that is the question. European Journal of Operational Research, 183 (3), 1513-1520. (doi:10.1016/j.ejor.2006.08.061).

Record type: Article

Abstract

Applicants for credit have to provide information for the risk assessment process. In the current conditions of a saturated consumer lending market, and hence falling “take” rates, can such information be used to assess the probability of a customer accepting the offer?
With the advent of internet broking pages, which allow borrowers to “apply” to a number of different companies at the same time, this “take” problem will increase. In some mortgage markets, it is quite common for more than 50% of those offered credit to reject it. In some cases, this is because the sale falls through but often it is because a relatively better retailer has offered a more suitable product to the borrower.
Lenders do not want to make the application process too complicated, and with the growth in adaptive marketing channels like the Internet and the telephone, they can make the questions they ask depend on the previous answers. We investigate how one could develop such “adaptive” application forms; which would assess acceptance probabilities.

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More information

Published date: 16 December 2007
Keywords: data mining, classification trees, question selection

Identifiers

Local EPrints ID: 51327
URI: http://eprints.soton.ac.uk/id/eprint/51327
ISSN: 0377-2217
PURE UUID: e1636f52-0cd3-4c0b-a10b-39a98e029652

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Date deposited: 05 Jun 2008
Last modified: 15 Mar 2024 10:17

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Contributors

Author: Hsin-Vonn Seow
Author: Lyn C. Thomas

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