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Traditional models like linear discriminant analysis and logistic regression often underperform for individuals in developing economies due to limited credit histories. The study synthesizes 36 cross-country articles using neural networks, support vector machines, and ensemble learning to improve predictive accuracy and better serve underserved and previously unbanked borrowers through fintech-enabled credit access.","Enhancing Credit Scoring with Alternative Data and  \nMachine Learning for Financial Inclusion  \nSEEJPH Volume XXVI,2025, ISSN: 2197-5248; Posted: 04-01-2025  \nEnhancing Credit Scoring with Alternative Data and Machine Learning for Financial Inclusion  \nJonnalagadda Anil Kumar1 and S Ramesh Babu2  \n1Ph.D. Scholar, Department of Commerce & Management, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP-522502, Centurion University of Technology and Management, Vizianagaram, AP-535003. Email[mailanilj@gmail.com](mailanilj@gmail.com)  \n2Associate Professor, Department of Commerce & Management, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur District, [AP-522502. Email-srb.rameshbabu@kluniversity.in](AP-522502. Email-srb.rameshbabu@kluniversity.in)  \n[Corresponding Author: Jonnalagadda Anil Kumar](Corresponding Author: Jonnalagadda Anil Kumar),  \n1Ph.D. Scholar, Department of Commerce & Management, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP-522502, Centurion University of Technology and Management, Vizianagaram, AP-535003  \n[mailanilj@gmail.com](mailanilj@gmail.com)  \nKEYWORDS  \nCredit Scoring, Machine Learning, Alternative Data, Financial Inclusion, Risk Assessment  \nABSTRACT  \nIntroduction: This review article explored advancements in credit appraisal through machine learning techniques and alternative data sources, staying focused on their implications for financial inclusion and risk assessment.  \nTraditional credit scoring models, which relied heavily on linear methods and credit history, often excluded individuals in developing economies and those with limited credit records. Methods: This gap underscored the need for innovative approaches leveraging nontraditional data such as psychometrics, email activity, and digital footprints. The research design encompassed a comprehensive analysis of 36 articles examining case studies from several countries covering applications in microfinance, agricultural credit, and fintechdriven solutions. Methodologically, the studies applied neural networks, support vector machines, and ensemble learning to enhance predictive accuracy over logistic regression and linear discriminant analysis.  \nResults: Findings consistently demonstrated that machine learning models outperform traditional approaches, especially in volatile environments and for underserved populations. Including alternative data significantly improved credit access, enabling financial institutions to extend services to high-risk or previously unbanked individuals. Conclusions: Implications for practice highlighted the transformative role of fintech in democratising credit, while theoretical contributions emphasised the evolving nature of credit risk modelling. This synthesis advocated for further interdisciplinary collaboration torefine non-traditional credit models and addressed ongoing barriers to global financial inclusion.  \nINTRODUCTION  \nThe financial technology (fintech) has revolutionized credit scoring, transforming how lenders assess creditworthiness and manage risk. Traditional credit appraisal systems, such as linear discriminant analysis (LDA) and logistic regression (LR), have long dominated the landscape. But these models often rely heavily on structured financial data, limiting their applicability to populations withinsufficient credit histories. This gap disproportionately affected individuals in developing economies, micro-entrepreneurs, and marginalized communities who lack access to formal financial systems (Demirguc-Kunt et al., 2018) . The exclusion of these groups underscored the need for innovative approaches that harness non-conventional data sources and machine learning (ML) techniques for bridging the credit gap and promote financial inclusion (Bussmann et al., 2021) . Ryan (2024) highlighted that the reliance of traditional credit scoring models on credit reports and FICO scores  \nEnhancing Credit Scoring with Alternative Data and  \nMachine Learning for Financial Inclusion ","cbCaidNAEhr9buBI","https://ap.wps.com/l/cbCaidNAEhr9buBI","pdf",169264,1,"English","en",105,"# Introduction\n## Traditional credit scoring limitations\n## Alternative data and machine learning approaches\n# Methods\n## Scope of the reviewed literature\n## Model types used in studies\n# Results\n## Performance vs traditional models\n## Impact on underserved populations\n# Conclusions\n## Implications for fintech and inclusion\n## Future collaboration and barriers","[{\"question\":\"Why do traditional credit scoring models exclude some borrowers?\",\"answer\":\"Traditional approaches rely on structured financial data and credit history assumptions, which often fail for people in developing economies and for those with limited or no credit records.\"},{\"question\":\"What alternative data sources are considered for more inclusive credit scoring?\",\"answer\":\"The review highlights nontraditional inputs such as psychometrics, utility payments, rent, email activity, digital footprints, web activity, and other non-conventional financial transactions.\"},{\"question\":\"How do machine learning models compare with logistic regression and linear methods?\",\"answer\":\"Across the reviewed studies, machine learning models generally outperform traditional approaches, particularly in volatile settings and for underserved populations, by capturing complex non-linear relationships.\"}]","Enhancing Credit Scoring with Alternative Data and Machine Learning for Financial Inclusion | 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