This research examines the potential impact of implementing a novel credit scoring model that integrates attributes beyond the traditional FICO model. It aims to address issues related to predatory lending and the financial exclusion affecting individuals often categorized as 'credit invisible,' 'credit unscorable,' 'unbanked,' and 'underbanked.' These individuals typically face difficulties in establishing or repairing a credit history, which poses a challenge for financial institutions in accurately evaluating their creditworthiness. This gap in the credit assessment process often opens doors to unfair lending practices. To tackle this problem, a systematically designed, built, tested, and evaluated innovative credit scoring model was developed. This model incorporates additional attributes aimed at providing a more holistic evaluation of an applicant's creditworthiness, including recent payments for rent, utilities, credit builder loans, and credit builder cards. Based on a representative sample of credit files, a prototype algorithm was created to test the functionality and effectiveness of the new model. Additionally, the research engaged forty stakeholders through surveys and interviews to gather feedback on the selection of model attributes and the model’s overall performance. Furthermore, the study expanded its evaluation by applying the newly designed model to an extensive dataset comprising millions of credit records, which included both approval and rejection decisions. This detailed analysis validated the claim that adopting the new credit scoring model could lead to the approval of thirty percent more credit applications that would otherwise have been rejected using the traditional FICO model. This research highlights the inherent limitations of the FICO model in the context of the contemporary economic landscape. Furthermore, this research underscores the profound transformative potential of adopting a novel credit scoring model more attuned to the complexities of today's financial ecosystem.
","abstract_html":"<p>This research examines the potential impact of implementing a novel credit scoring model that integrates attributes beyond the traditional FICO model. It aims to address issues related to predatory lending and the financial exclusion affecting individuals often categorized as 'credit invisible,' 'credit unscorable,' 'unbanked,' and 'underbanked.' These individuals typically face difficulties in establishing or repairing a credit history, which poses a challenge for financial institutions in accurately evaluating their creditworthiness. This gap in the credit assessment process often opens doors to unfair lending practices. To tackle this problem, a systematically designed, built, tested, and evaluated innovative credit scoring model was developed. This model incorporates additional attributes aimed at providing a more holistic evaluation of an applicant's creditworthiness, including recent payments for rent, utilities, credit builder loans, and credit builder cards. Based on a representative sample of credit files, a prototype algorithm was created to test the functionality and effectiveness of the new model. Additionally, the research engaged forty stakeholders through surveys and interviews to gather feedback on the selection of model attributes and the model’s overall performance. Furthermore, the study expanded its evaluation by applying the newly designed model to an extensive dataset comprising millions of credit records, which included both approval and rejection decisions. This detailed analysis validated the claim that adopting the new credit scoring model could lead to the approval of thirty percent more credit applications that would otherwise have been rejected using the traditional FICO model. This research highlights the inherent limitations of the FICO model in the context of the contemporary economic landscape. Furthermore, this research underscores the profound transformative potential of adopting a novel credit scoring model more attuned to the complexities of today's financial ecosystem.</p>","abstract_has_math":false,"creators":["Zakowska, Anna"],"institution":null,"degree_name":"Information Systems and Technology, PhD","degree_level":"Open Access Dissertation","degree_discipline":"Center for Information Systems and Technology","degree_department":null,"school":null,"contributors":["Wallace Chipidza","Chinazunwa Uwaoma"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-01-01T08:00:00Z","date_published":"2023-01-01T08:00:00Z","updated_at":"2026-07-24T01:40:36Z","subjects":["Computer Sciences","Databases and Information Systems"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarship.claremont.edu/cgu_etd/718","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Wallace Chipidza","Chinazunwa Uwaoma"]},{"key":"dc:creator","label":"Author","values":["Zakowska, Anna"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2024-01-24T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Center for Information Systems and Technology"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Open Access Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Information Systems and Technology, PhD"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Computer Sciences","Databases and Information Systems"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarship.claremont.edu/cgu_etd/718"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This research examines the potential impact of implementing a novel credit scoring model that integrates attributes beyond the traditional FICO model. It aims to address issues related to predatory lending and the financial exclusion affecting individuals often categorized as 'credit invisible,' 'credit unscorable,' 'unbanked,' and 'underbanked.' These individuals typically face difficulties in establishing or repairing a credit history, which poses a challenge for financial institutions in accurately evaluating their creditworthiness. This gap in the credit assessment process often opens doors to unfair lending practices. To tackle this problem, a systematically designed, built, tested, and evaluated innovative credit scoring model was developed. This model incorporates additional attributes aimed at providing a more holistic evaluation of an applicant's creditworthiness, including recent payments for rent, utilities, credit builder loans, and credit builder cards. Based on a representative sample of credit files, a prototype algorithm was created to test the functionality and effectiveness of the new model. Additionally, the research engaged forty stakeholders through surveys and interviews to gather feedback on the selection of model attributes and the model’s overall performance. Furthermore, the study expanded its evaluation by applying the newly designed model to an extensive dataset comprising millions of credit records, which included both approval and rejection decisions. This detailed analysis validated the claim that adopting the new credit scoring model could lead to the approval of thirty percent more credit applications that would otherwise have been rejected using the traditional FICO model. This research highlights the inherent limitations of the FICO model in the context of the contemporary economic landscape. Furthermore, this research underscores the profound transformative potential of adopting a novel credit scoring model more attuned to the complexities of today's financial ecosystem.
"]},{"key":"dc:title","label":"Title","values":["A New Credit Scoring Model to Reduce Potential Predatory Lending: A Design Science Approach"]}]}],"canonical_facts":{"dc:contributor":["Wallace Chipidza","Chinazunwa Uwaoma"],"dc:creator":["Zakowska, Anna"],"dc:date.available":["2024-01-24T08:00:00Z"],"dc:description.abstract":["This research examines the potential impact of implementing a novel credit scoring model that integrates attributes beyond the traditional FICO model. It aims to address issues related to predatory lending and the financial exclusion affecting individuals often categorized as 'credit invisible,' 'credit unscorable,' 'unbanked,' and 'underbanked.' These individuals typically face difficulties in establishing or repairing a credit history, which poses a challenge for financial institutions in accurately evaluating their creditworthiness. This gap in the credit assessment process often opens doors to unfair lending practices. To tackle this problem, a systematically designed, built, tested, and evaluated innovative credit scoring model was developed. This model incorporates additional attributes aimed at providing a more holistic evaluation of an applicant's creditworthiness, including recent payments for rent, utilities, credit builder loans, and credit builder cards. Based on a representative sample of credit files, a prototype algorithm was created to test the functionality and effectiveness of the new model. Additionally, the research engaged forty stakeholders through surveys and interviews to gather feedback on the selection of model attributes and the model’s overall performance. Furthermore, the study expanded its evaluation by applying the newly designed model to an extensive dataset comprising millions of credit records, which included both approval and rejection decisions. This detailed analysis validated the claim that adopting the new credit scoring model could lead to the approval of thirty percent more credit applications that would otherwise have been rejected using the traditional FICO model. This research highlights the inherent limitations of the FICO model in the context of the contemporary economic landscape. Furthermore, this research underscores the profound transformative potential of adopting a novel credit scoring model more attuned to the complexities of today's financial ecosystem.
"],"dc:identifier":["https://scholarship.claremont.edu/cgu_etd/718"],"dc:subject":["Computer Sciences","Databases and Information Systems"],"dc:title":["A New Credit Scoring Model to Reduce Potential Predatory Lending: A Design Science Approach"],"thesis:degree_discipline":["Center for Information Systems and Technology"],"thesis:degree_level":["Open Access Dissertation"],"thesis:degree_name":["Information Systems and Technology, PhD"]},"updated_at":"2026-07-24T01:40:36Z"}