I built a bureau-independent credit risk assessment tool for borrowers with limited traditional credit history. For many people entering digital lending markets, traditional credit bureau data may be limited or unavailable. That raises an important question: How can risk be assessed transparently when traditional bureau history is incomplete? For my Fintech Credit Risk Analysis project, I analysed 149,999 borrower records and developed a bureau-independent risk segmentation methodology using observable borrower characteristics and behavioural indicators. The analysis explored: • Late-payment behaviour • Credit-line availability and thin-file risk • Income and age patterns • Financial pressure associated with dependents • Exposure and behavioural risk combinations One finding that influenced the demonstration tool: Borrowers with zero open credit lines had a 25.64% default rate — the highest among the credit-line segments analysed. I then built a working demonstration application: Bureau-Independent Credit Risk Check. The tool uses four transparent binary risk indicators and clearly shows users: ✅ Which risk flags were triggered ✅ Why they were triggered ✅ The resulting risk tier ✅ The complete breakdown of all four indicators No black-box score or hidden weighting system. Just a transparent demonstration of how data-driven risk segmentation could support financial inclusion when traditional credit history is limited. Try the live demonstration: https://lnkd.in/e4fmxc2p Explore the full project and methodology: https://lnkd.in/eYiuu7g8 Built with SQL, MySQL, Python, Pandas, Tableau, and a working web application.