Blockchain-Based Credit Scoring Systems for Financial Inclusion

Thesis Abstract

Limited access to traditional credit evaluation mechanisms remains a significant barrier to financial inclusion for underserved populations worldwide, particularly those lacking formal financial histories or collateral. This study investigates the potential of blockchain technology to develop a secure, transparent, and decentralized credit scoring system aimed at enhancing financial inclusion. The primary aim is to design and empirically evaluate a blockchain-based credit scoring framework that can serve individuals excluded from conventional credit markets. To achieve this, the study establishes specific objectives (1) to review existing credit scoring models and assess their limitations within the context of financial exclusion; (2) to develop a conceptual framework for a blockchain-enabled credit scoring system integrating alternative data sources; (3) to empirically test the effectiveness and accuracy of the proposed system via pilot implementation; and (4) to analyze stakeholders’ perceptions of blockchain trustworthiness in credit assessment. The research adopts a mixed-methods explanatory design, combining qualitative analyses of existing credit scoring paradigms with quantitative validation of the proposed framework. The population comprises 10,000 unbanked and underbanked individuals from a mid-sized regional economy, with a stratified random sampling technique applied to select a sample of 1,200 participants for quantitative surveys and 50 key informants for qualitative interviews. Data collection tools include structured questionnaires, semi-structured interview guides, and blockchain transaction data logs. The validity and reliability of instruments are ensured through Cronbach's alpha coefficients exceeding 0.8, pilot testing, and triangulation of data sources. The quantitative data are analyzed using logistic regression and machine learning classification algorithms such as random forests to evaluate predictive accuracy, while thematic analysis is employed for qualitative insights into stakeholder perceptions. The analytical framework is reinforced by the integration of the Diffusion of Innovations theory to understand adoption pathways and the Trust Theory to evaluate perceived security and transparency. Expected findings suggest that blockchain-based credit scoring models can significantly improve predictive accuracy over traditional systems, particularly in contexts lacking formal financial data. The study also anticipates identifying key factors influencing stakeholder trust in blockchain solutions, including perceived transparency, data security, and user empowerment. The comparative analysis is expected to reveal that blockchain systems enable more inclusive credit assessments, facilitating access to credit for previously excluded individuals, thereby contributing to financial sector stability and economic growth. These outcomes are expected to demonstrate that blockchain's decentralization and immutability offer tangible benefits in reducing fraud, minimizing biases, and increasing transparency in credit evaluations. The study's contribution to knowledge lies in the development of a novel, empirically validated framework for blockchain-based credit scoring tailored for underserved populations, filling a critical gap in the literature on financial technologies for inclusion. It extends current models by integrating alternative data sources within a blockchain ecosystem and providing practical insights for financial institutions, regulators, and policymakers seeking to adopt such innovations. The findings offer evidence-based guidance on designing inclusive credit systems that balance technological security with user trust and accessibility. In conclusion, the research advocates for the strategic integration of blockchain technology in credit scoring processes to promote broader financial inclusion, recommending policy adaptations, capacity building, and stakeholder engagement strategies to facilitate adoption. Further studies are suggested to explore long-term impacts of blockchain-based credit systems on financial stability, user behavior, and socioeconomic development, as well as scalability across different regional and demographic contexts.