This quantitative study closely emulated Zou and Khern's (2022) analysis of AI Bias in Mortgage Applications. They used the Home Mortgage Disclosure Act (HMDA) dataset from the Federal Financial Institution Examination Council's (FFEIC) website to review mortgage loan data from 2019 to determine if there was bias in the AI mortgage application approvals. Those researchers concluded that bias does exist in the AI mortgage underwriting software. Since their study, research has demonstrated that discrimination continues to exist in AI software. Therefore, this research expands on Zou and Khern’s study to determine if there are differences in the mortgage loan approval outcomes, whether AI Bias is present in the mortgage application approvals, and if fair AI algorithms reduce AI bias in the mortgage application datasets by analyzing historical mortgage loan data from the HMDA dataset published in 2022. The variables in this study included race and region as an independent variable and mortgage loan approval outcome as a dependent variable. The statistical analysis included a Chi-square test to analyze the relationship between race, geographical, and loan approval outcomes. The methodology included the fair-on-average causal Effect (FACE) and fair-on-average causal Effect on the Treated (FACT) to detect AI bias in the dataset. Additionally, IBM AI Fairness 360 (AIF360) and Microsoft Fairlean (MSF) were used to detect and mitigate bias. The findings concluded that bias does exist in the mortgage application dataset. The research highlighted the need for fair AI algorithms to reduce bias in the mortgage approval process.
","abstract_html":"<p>This quantitative study closely emulated Zou and Khern's (2022) analysis of AI Bias in Mortgage Applications. They used the Home Mortgage Disclosure Act (HMDA) dataset from the Federal Financial Institution Examination Council's (FFEIC) website to review mortgage loan data from 2019 to determine if there was bias in the AI mortgage application approvals. Those researchers concluded that bias does exist in the AI mortgage underwriting software. Since their study, research has demonstrated that discrimination continues to exist in AI software. Therefore, this research expands on Zou and Khern’s study to determine if there are differences in the mortgage loan approval outcomes, whether AI Bias is present in the mortgage application approvals, and if fair AI algorithms reduce AI bias in the mortgage application datasets by analyzing historical mortgage loan data from the HMDA dataset published in 2022. The variables in this study included race and region as an independent variable and mortgage loan approval outcome as a dependent variable. The statistical analysis included a Chi-square test to analyze the relationship between race, geographical, and loan approval outcomes. The methodology included the fair-on-average causal Effect (FACE) and fair-on-average causal Effect on the Treated (FACT) to detect AI bias in the dataset. Additionally, IBM AI Fairness 360 (AIF360) and Microsoft Fairlean (MSF) were used to detect and mitigate bias. The findings concluded that bias does exist in the mortgage application dataset. The research highlighted the need for fair AI algorithms to reduce bias in the mortgage approval process.</p>","abstract_has_math":false,"creators":["Cornelius, Dorian"],"institution":null,"degree_name":"DBA Doctorate in Business Administration","degree_level":"Dissertation - Public Access","degree_discipline":"Business Administration","degree_department":null,"school":null,"contributors":["Dr. Marguerite Chabau","Dr. Colleen Ramos","Dr. David SanFilippo"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-03-01T08:00:00Z","date_published":"2025-03-01T08:00:00Z","updated_at":"2026-07-24T03:21:38Z","subjects":["AI Bias","Fair AI Models","Regional Disparities","Mortgage Underwriting Software","Home Mortgage Disclosure Act","Census Regions","Business Administration, Management, and Operations","Business Law, Public Responsibility, and Ethics","Finance and Financial Management","Other Business","Real Estate"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.nl.edu/diss/879","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Dr. Marguerite Chabau","Dr. Colleen Ramos","Dr. David SanFilippo"]},{"key":"dc:creator","label":"Author","values":["Cornelius, Dorian"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2025-04-23T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Business Administration"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation - Public Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["DBA Doctorate in Business Administration"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["AI Bias","Fair AI Models","Regional Disparities","Mortgage Underwriting Software","Home Mortgage Disclosure Act","Census Regions","Business Administration, Management, and Operations","Business Law, Public Responsibility, and Ethics","Finance and Financial Management","Other Business","Real Estate"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.nl.edu/diss/879"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This quantitative study closely emulated Zou and Khern's (2022) analysis of AI Bias in Mortgage Applications. They used the Home Mortgage Disclosure Act (HMDA) dataset from the Federal Financial Institution Examination Council's (FFEIC) website to review mortgage loan data from 2019 to determine if there was bias in the AI mortgage application approvals. Those researchers concluded that bias does exist in the AI mortgage underwriting software. Since their study, research has demonstrated that discrimination continues to exist in AI software. Therefore, this research expands on Zou and Khern’s study to determine if there are differences in the mortgage loan approval outcomes, whether AI Bias is present in the mortgage application approvals, and if fair AI algorithms reduce AI bias in the mortgage application datasets by analyzing historical mortgage loan data from the HMDA dataset published in 2022. The variables in this study included race and region as an independent variable and mortgage loan approval outcome as a dependent variable. The statistical analysis included a Chi-square test to analyze the relationship between race, geographical, and loan approval outcomes. The methodology included the fair-on-average causal Effect (FACE) and fair-on-average causal Effect on the Treated (FACT) to detect AI bias in the dataset. Additionally, IBM AI Fairness 360 (AIF360) and Microsoft Fairlean (MSF) were used to detect and mitigate bias. The findings concluded that bias does exist in the mortgage application dataset. The research highlighted the need for fair AI algorithms to reduce bias in the mortgage approval process.
"]},{"key":"dc:title","label":"Title","values":["Does Artificial Intelligence Bias Exist in Mortgage Underwriting Software? Investigating Bias, Regional Disparities, and Fair AI Models"]}]}],"canonical_facts":{"dc:contributor":["Dr. Marguerite Chabau","Dr. Colleen Ramos","Dr. David SanFilippo"],"dc:creator":["Cornelius, Dorian"],"dc:date.available":["2025-04-23T07:00:00Z"],"dc:description.abstract":["This quantitative study closely emulated Zou and Khern's (2022) analysis of AI Bias in Mortgage Applications. They used the Home Mortgage Disclosure Act (HMDA) dataset from the Federal Financial Institution Examination Council's (FFEIC) website to review mortgage loan data from 2019 to determine if there was bias in the AI mortgage application approvals. Those researchers concluded that bias does exist in the AI mortgage underwriting software. Since their study, research has demonstrated that discrimination continues to exist in AI software. Therefore, this research expands on Zou and Khern’s study to determine if there are differences in the mortgage loan approval outcomes, whether AI Bias is present in the mortgage application approvals, and if fair AI algorithms reduce AI bias in the mortgage application datasets by analyzing historical mortgage loan data from the HMDA dataset published in 2022. The variables in this study included race and region as an independent variable and mortgage loan approval outcome as a dependent variable. The statistical analysis included a Chi-square test to analyze the relationship between race, geographical, and loan approval outcomes. The methodology included the fair-on-average causal Effect (FACE) and fair-on-average causal Effect on the Treated (FACT) to detect AI bias in the dataset. Additionally, IBM AI Fairness 360 (AIF360) and Microsoft Fairlean (MSF) were used to detect and mitigate bias. The findings concluded that bias does exist in the mortgage application dataset. The research highlighted the need for fair AI algorithms to reduce bias in the mortgage approval process.
"],"dc:identifier":["https://digitalcommons.nl.edu/diss/879"],"dc:subject":["AI Bias","Fair AI Models","Regional Disparities","Mortgage Underwriting Software","Home Mortgage Disclosure Act","Census Regions","Business Administration, Management, and Operations","Business Law, Public Responsibility, and Ethics","Finance and Financial Management","Other Business","Real Estate"],"dc:title":["Does Artificial Intelligence Bias Exist in Mortgage Underwriting Software? Investigating Bias, Regional Disparities, and Fair AI Models"],"thesis:degree_discipline":["Business Administration"],"thesis:degree_level":["Dissertation - Public Access"],"thesis:degree_name":["DBA Doctorate in Business Administration"]},"updated_at":"2026-07-24T03:21:38Z"}