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How much effort should be spent to detect fraudulent applications when engaged in classifier-based lending?

How much effort should be spent to detect fraudulent applications when engaged in classifier-based lending?
How much effort should be spent to detect fraudulent applications when engaged in classifier-based lending?
Credit scoring is an automated, objective and consistent tool which helps lenders to provide quick loan decisions. It can replace some of the more mechanical work done by experienced loan officers whose decisions are intuitive but potentially subject to bias. Prospective borrowers may have a strong motivation to fraudulently falsify one or more of the attributes they report on their application form. Applicants learn about the characteristics that are used to build credit scoring models, and may alter the answers on their application form to improve their chance of loan approval. Few automated credit scoring models have considered falsified information from borrowers. We will show that sometimes it is profitable for financial institutions to spend money and effort to identify dishonest customers. We will also find the optimal effort that banks should spend on identifying these liars. Furthermore, we will show that it is possible for liars to eventually adjust their lies to escape from credit checks. The proposed issue will be studied using simulated data and discriminant analysis. This research can help lending financial institutions to reduce risk and maximize profit, and it also shows that it is feasible for customers to lie intelligently so as to evade credit checks and get loans
1088-467x
S87-S101
Chong, Mimi
67f1e893-b832-4370-a608-06babf693cbb
Bravo, Cristián
a5d0f685-d730-497c-8e12-b1e1d2e560e4
Davison, Matt
d3bae0fe-6789-4a28-a3a6-50005992c8e4
Bravo, Cristian
b22c4145-644e-40ee-85d8-431c59c3c71b
Jofré, Alejandro
e76911eb-122d-403b-9329-27ae13b662b1
Maldonado, Sebastián
9e5fb121-d905-4337-beb3-bba6f7da9ae2
Weber, Richard
da9918d6-bc84-4c98-8ffe-2aaf7b58cf1b
Chong, Mimi
67f1e893-b832-4370-a608-06babf693cbb
Bravo, Cristián
a5d0f685-d730-497c-8e12-b1e1d2e560e4
Davison, Matt
d3bae0fe-6789-4a28-a3a6-50005992c8e4
Bravo, Cristian
b22c4145-644e-40ee-85d8-431c59c3c71b
Jofré, Alejandro
e76911eb-122d-403b-9329-27ae13b662b1
Maldonado, Sebastián
9e5fb121-d905-4337-beb3-bba6f7da9ae2
Weber, Richard
da9918d6-bc84-4c98-8ffe-2aaf7b58cf1b

Chong, Mimi, Bravo, Cristián, Davison, Matt, Bravo, Cristian, Jofré, Alejandro, Maldonado, Sebastián and Weber, Richard (2015) How much effort should be spent to detect fraudulent applications when engaged in classifier-based lending? Intelligent Data Analysis, 19, supplement 1, S87-S101. (doi:10.3233/IDA-150771).

Record type: Article

Abstract

Credit scoring is an automated, objective and consistent tool which helps lenders to provide quick loan decisions. It can replace some of the more mechanical work done by experienced loan officers whose decisions are intuitive but potentially subject to bias. Prospective borrowers may have a strong motivation to fraudulently falsify one or more of the attributes they report on their application form. Applicants learn about the characteristics that are used to build credit scoring models, and may alter the answers on their application form to improve their chance of loan approval. Few automated credit scoring models have considered falsified information from borrowers. We will show that sometimes it is profitable for financial institutions to spend money and effort to identify dishonest customers. We will also find the optimal effort that banks should spend on identifying these liars. Furthermore, we will show that it is possible for liars to eventually adjust their lies to escape from credit checks. The proposed issue will be studied using simulated data and discriminant analysis. This research can help lending financial institutions to reduce risk and maximize profit, and it also shows that it is feasible for customers to lie intelligently so as to evade credit checks and get loans

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More information

Published date: 9 October 2015
Organisations: Southampton Business School

Identifiers

Local EPrints ID: 396638
URI: http://eprints.soton.ac.uk/id/eprint/396638
ISSN: 1088-467x
PURE UUID: f73bce6e-0511-4ad3-8056-9f2eaf02534e
ORCID for Cristian Bravo: ORCID iD orcid.org/0000-0003-1579-1565

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Date deposited: 09 Jun 2016 13:02
Last modified: 15 Mar 2024 03:33

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Contributors

Author: Mimi Chong
Author: Cristián Bravo
Author: Matt Davison
Author: Cristian Bravo ORCID iD
Author: Alejandro Jofré
Author: Sebastián Maldonado
Author: Richard Weber

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