Credit risk foundations
Core concepts, lending lifecycle, ratings, and portfolio risk.
A practical credit-risk learning hub
Practical credit risk, made understandable.
Learn the methods, inspect the assumptions, and reproduce the calculations—from scorecards and PD to IFRS 9 expected credit loss.
Explore the field
Credit risk, from data to decisions
Credit scorecards
Build, scale, validate, and monitor application and behavioural scorecards.
Probability of default (PD)
Define, estimate, calibrate, and project default probabilities.
Loss given default (LGD)
Measure discounted losses after recovery, costs, and collateral.
Exposure at default (EAD)
Estimate drawn and undrawn exposure at the point of default.
IFRS 9 expected credit loss
Understand impairment stages, scenarios, and discounted expected losses.
Credit-risk data engineering
Build reliable point-in-time datasets, labels, joins, and controls.
Statistics and machine learning
Compare statistical and machine-learning approaches for risk modelling.
Validation and governance
Backtest, challenge, document, and govern credit-risk models.
Learn
Guides for working methods
Scorecards, probability of default, loss severity, exposure, data preparation, statistics, and governance.
Explore topics →Calculate
Inspectable calculators
Change assumptions and see the formula steps for score scaling, PD, LGD, EAD, and scenario-weighted ECL.
Try a calculator →Practice
Run code and inspect data
Run editable Python samples for scorecards, PD, LGD, EAD, ECL, and model monitoring, or profile a CSV locally.
Browse runnable Python examples →Open the data toolkit →Educational material only. Examples use synthetic data and simplified assumptions. Confirm current accounting requirements, regulatory expectations, and institutional policy before applying a method to decisions or reporting.