Dynamic affordability assessment: predicting an applicant’s ability to repay over the life of the loan
Dynamic affordability assessment: predicting an applicant’s ability to repay over the life of the loan
In the credit decision-making process, both an applicant's creditworthiness and their affordability should be assessed. While credit scoring focuses on creditworthiness, affordability is often checked on the basis of current income and estimated current consumption as well as existing debts stated in a credit report. Contrary to that static approach, a theoretical framework for dynamic affordability assessment is proposed in this paper. In this approach, both income and consumption are allowed to vary over time and their changes are described with random effects models for panel data. The models are derived from the economic literature, including the Euler equation of consumption. A simulation is run on their basis and predicted time series are generated for a given applicant. For each pair of the predicted income and consumption time series, the applicant's ability to repay is checked over the life of the loan, for all possible instalment amounts. As a result, a probability of default is assigned to each amount, which can help find the maximum affordable instalment. This is illustrated with an example based on artificial data. Assessing affordability over the loan repayment period as well as taking into account variability of income and expenditure over time are in line with recommendations of the UK Office of Fair Trading and the Financial Services Authority. In practice, the suggested approach could contribute to responsible lending.
3-32
Bijak, Katarzyna
5130b6b9-fbf1-44e8-9106-1dd69c6692a6
Thomas, Lyn C.
a3ce3068-328b-4bce-889f-965b0b9d2362
Mues, Christophe
07438e46-bad6-48ba-8f56-f945bc2ff934
March 2014
Bijak, Katarzyna
5130b6b9-fbf1-44e8-9106-1dd69c6692a6
Thomas, Lyn C.
a3ce3068-328b-4bce-889f-965b0b9d2362
Mues, Christophe
07438e46-bad6-48ba-8f56-f945bc2ff934
Bijak, Katarzyna, Thomas, Lyn C. and Mues, Christophe
(2014)
Dynamic affordability assessment: predicting an applicant’s ability to repay over the life of the loan.
Journal of Credit Risk, 10 (1), .
(doi:10.21314/JCR.2014.171).
Abstract
In the credit decision-making process, both an applicant's creditworthiness and their affordability should be assessed. While credit scoring focuses on creditworthiness, affordability is often checked on the basis of current income and estimated current consumption as well as existing debts stated in a credit report. Contrary to that static approach, a theoretical framework for dynamic affordability assessment is proposed in this paper. In this approach, both income and consumption are allowed to vary over time and their changes are described with random effects models for panel data. The models are derived from the economic literature, including the Euler equation of consumption. A simulation is run on their basis and predicted time series are generated for a given applicant. For each pair of the predicted income and consumption time series, the applicant's ability to repay is checked over the life of the loan, for all possible instalment amounts. As a result, a probability of default is assigned to each amount, which can help find the maximum affordable instalment. This is illustrated with an example based on artificial data. Assessing affordability over the loan repayment period as well as taking into account variability of income and expenditure over time are in line with recommendations of the UK Office of Fair Trading and the Financial Services Authority. In practice, the suggested approach could contribute to responsible lending.
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Accepted/In Press date: 14 February 2014
e-pub ahead of print date: March 2014
Published date: March 2014
Organisations:
Centre of Excellence for International Banking, Finance & Accounting
Identifiers
Local EPrints ID: 364203
URI: http://eprints.soton.ac.uk/id/eprint/364203
ISSN: 1744-6619
PURE UUID: f5977f03-15fa-40d7-8e5c-8c4de612b9c0
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Date deposited: 10 Apr 2014 07:35
Last modified: 15 Mar 2024 03:36
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Author:
Lyn C. Thomas
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