Build practical credit risk analytics skills for banking, lending, investment research, and financial risk management.
Analyze creditworthiness, apply rating models, predict defaults with Python, and evaluate operational risk frameworks.
This Specialization develops an end-to-end understanding of how financial institutions identify, assess, model, and manage credit and operational risk. You will conduct credit research, interpret credit ratings, evaluate borrower financial strength, and analyze financial statements, cash flows, ratios, working capital, and repayment capacity.
You will apply established credit risk techniques, including the KMV Model and Altman Z-Score, while examining internal and external credit rating processes. Using Python, you will prepare credit datasets, perform exploratory analysis, build classification models, and evaluate logistic regression, decision tree, and Random Forest performance. You will also use hyperparameter tuning to improve credit default predictions.
The Specialization concludes with operational risk assessment across US and UK financial markets, covering RCSA, BIA, SA, AMA, loss events, and risk controls. By completion, you will be prepared to support structured, evidence-based lending, investment, and risk management decisions.
Applied Learning Project
Learners will complete applied projects using borrower financial data and credit datasets to assess creditworthiness, interpret rating factors, and estimate default risk. They will build and evaluate Python-based classification models and apply operational risk frameworks to recommend evidence-based lending and risk-control decisions.
