I manage a USD 10 million lending portfolio and need a structured credit-risk assessment that lets me understand, quantify, and monitor the probability of default and potential loss. This work sits squarely in Finance and is entirely focused on Credit Risk. Scope of work • Analyse the current loan book (ageing, collateral, industry mix, concentration). • Build or refine PD, LGD, and EAD models—Excel, R or Python are all acceptable as long as the logic is transparent. • Run stress-testing scenarios (rate shocks, macro downturn). • Deliver a concise risk-rating methodology aligned with Basel guidelines, plus a user-friendly dashboard that updates as new data flows in. • Summarise findings in a short report highlighting key exposures, recommended limits, and early-warning triggers. Acceptance criteria – Models reproduce results from the sample data I provide, with code formulas fully commented. – Stress-test outputs clearly show impacts on capital and expected loss. – Dashboard loads in under five seconds on a standard laptop and allows me to change assumptions without editing the code. Please include any prior examples of credit-risk modelling, especially if you have worked with SME or micro-loan portfolios.