Blockchain-based Credit Scoring System for Emerging Market Banks

Thesis Abstract

The persistent challenge of accurate and equitable credit assessment in emerging market banking institutions hampers financial inclusion and economic development, necessitating innovative solutions that address data limitations and enhance credit decision-making processes. This study aims to develop and evaluate a blockchain-based credit scoring system designed specifically for banks operating within emerging markets, with the primary objectives of assessing the system’s technical feasibility, measuring its impact on credit risk accuracy, and exploring its potential to improve access to finance for underbanked populations. The research adopts a mixed-methods approach, combining quantitative analysis of primary data collected from a sample of 15 banks across three emerging economies and qualitative insights derived from interviews with key banking stakeholders and credit bureau officials. The quantitative component employs a cross-sectional survey design, utilizing structured questionnaires distributed to 150 credit officers and risk managers, complemented by secondary data analysis of 200 anonymized credit transactions obtained from participating banks’ databases. Data collection instruments include validated questionnaires and blockchain transaction logs, ensuring comprehensive data capture. The reliability and validity of the instruments are reinforced through Cronbach’s alpha testing and pilot studies. Quantitative data are analyzed via multiple regression analysis and machine learning algorithms, such as random forest classifiers, to evaluate the accuracy and predictive power of the blockchain-powered credit scoring model compared to traditional systems. Thematic analysis is employed for qualitative interview data, providing context on system adoption, perceived challenges, and stakeholder acceptance. The anticipated findings suggest that integrating blockchain technology significantly improves credit scoring accuracy, reduces assessment bias, and enhances data security and transparency, thereby fostering greater confidence among lenders and borrowers. Evidence is expected to demonstrate that blockchain-based scoring models outperform conventional methods, particularly in environments with limited credit histories and fragmented data sources. These results will contribute to the growing body of literature on financial technology adoption in emerging markets, extending current knowledge by empirically validating the efficiency gains and risk mitigation potential of blockchain-integrated credit scoring systems. The study will also uncover key factors influencing the implementation and acceptance of blockchain solutions within banking contexts, providing practical insights for policymakers, financial institutions, and technology developers. The main conclusion emphasizes that blockchain technology can revolutionize credit assessment mechanisms in emerging markets by enabling decentralized, tamper-proof, and real-time data sharing across institutions, thereby overcoming traditional data silos. Consequently, the study recommends the adoption of blockchain-based credit scoring systems as a strategic initiative for banks seeking to expand financial inclusion and reduce credit risk exposure. It advocates for the development of standardized protocols to facilitate interoperability among diverse financial entities and underscores the importance of regulatory frameworks to ensure system security and consumer protection. Future research should explore longitudinal impacts of blockchain integration on borrower behavior and long-term risk management, as well as broader scalability factors across different economic contexts. This study’s outcomes are poised to inform both academic discourse and practical applications, offering a replicable model for deploying blockchain technology to enhance credit evaluation processes in emerging markets globally.