Expect the Unexpected: Measuring Noise & Bias in the Credit Assessment Process
(2022) FEKH39 20212Department of Business Administration
- Abstract
- The purpose of the thesis is to measure how bias impacts loan officers’ decision-making upon assessing mortgage applications and the level of noise embedded within the process. Quantitative data were collected from 15 loan officers working at three different branches at Handelsbanken answering a questionnaire based on fictional mortgage applications. The statistical analysis used an unpaired t-test and the relative approximation error to assess bias and noise respectively. We found large levels of noise within loan officers’ credit assessments that impact loan officers’ decision-making capabilities regarding credit granted, the interest rate given, and the risk perceived with the applications. Furthermore, the findings also illustrated the... (More)
- The purpose of the thesis is to measure how bias impacts loan officers’ decision-making upon assessing mortgage applications and the level of noise embedded within the process. Quantitative data were collected from 15 loan officers working at three different branches at Handelsbanken answering a questionnaire based on fictional mortgage applications. The statistical analysis used an unpaired t-test and the relative approximation error to assess bias and noise respectively. We found large levels of noise within loan officers’ credit assessments that impact loan officers’ decision-making capabilities regarding credit granted, the interest rate given, and the risk perceived with the applications. Furthermore, the findings also illustrated the impact of bias as loan officers perceive applicants with socially less prestigious occupations as riskier than applicants with socially considered more prestigious occupations. The theoretical contributions of this study further enhance our understanding of human decision-making and more specifically how and to what extent bias and noise impact the credit assessment process. The main implications of these findings are that households that are applying for a mortgage can likely expect large variations in the amount of credit they can borrow and at what interest rate. Additionally, the empirical findings imply that loan officers’ assessment of the applicant's creditworthiness can be viewed as subjective despite relying on standardized credit policies. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9075284
- author
- Skoglund, Jacob LU ; Ekberg, Leonard LU and Govenius, Pontus LU
- supervisor
- organization
- course
- FEKH39 20212
- year
- 2022
- type
- M2 - Bachelor Degree
- subject
- keywords
- mortgage, credit assessment, loan officer, decision-making, bias, noise, kreditgivningsprocess, kredithandläggare, beslutsfattande
- language
- English
- id
- 9075284
- date added to LUP
- 2022-02-14 13:44:47
- date last changed
- 2022-02-14 13:44:47
@misc{9075284,
abstract = {{The purpose of the thesis is to measure how bias impacts loan officers’ decision-making upon assessing mortgage applications and the level of noise embedded within the process. Quantitative data were collected from 15 loan officers working at three different branches at Handelsbanken answering a questionnaire based on fictional mortgage applications. The statistical analysis used an unpaired t-test and the relative approximation error to assess bias and noise respectively. We found large levels of noise within loan officers’ credit assessments that impact loan officers’ decision-making capabilities regarding credit granted, the interest rate given, and the risk perceived with the applications. Furthermore, the findings also illustrated the impact of bias as loan officers perceive applicants with socially less prestigious occupations as riskier than applicants with socially considered more prestigious occupations. The theoretical contributions of this study further enhance our understanding of human decision-making and more specifically how and to what extent bias and noise impact the credit assessment process. The main implications of these findings are that households that are applying for a mortgage can likely expect large variations in the amount of credit they can borrow and at what interest rate. Additionally, the empirical findings imply that loan officers’ assessment of the applicant's creditworthiness can be viewed as subjective despite relying on standardized credit policies.}},
author = {{Skoglund, Jacob and Ekberg, Leonard and Govenius, Pontus}},
language = {{eng}},
note = {{Student Paper}},
title = {{Expect the Unexpected: Measuring Noise & Bias in the Credit Assessment Process}},
year = {{2022}},
}