Lesson 1Designing target variables: default definitions, delinquency thresholds, time horizonsLearn how fi define target variables fi credit risk models, including default events, delinquency thresholds, an time horizons, an understand how dese design choices affect model performance, calibration, an business relevance.
Defining default and charge-off eventsChoosing delinquency and DPD thresholdsObservation and prediction window designHandling cures, roll rates, and recoveriesAligning targets with business objectivesLesson 2Customer features to collect: payment history, days past due, invoice-level data, order frequency, average invoice size, sector, size, locationIdentify key customer features fi credit scoring, including payment history, days past due, invoice-level behavior, order frequency, ticket size, sector, size, an location, an learn how fi engineer stable, predictive variables from raw data.
Payment history and delinquency metricsDays past due and aging bucketsInvoice-level and transaction featuresOrder frequency and seasonality signalsSector, size, and geographic attributesLesson 3Model interpretability and explainability methods (SHAP, feature importance, monotonic constraints)Learn interpretability techniques fi credit risk models, including global an local feature importance, SHAP values, partial dependence, an monotonic constraints, fi support regulatory review, customer explanations, an internal trust.
Global versus local explanationsFeature importance and permutation testsSHAP values for local explanationsPartial dependence and ICE plotsMonotonic constraints in credit modelsLesson 4Augmented features: external data (credit bureau, public filings), macro indicators, trade supplier behaviorExplore how fi enrich internal records wid bureau data, public filings, macro indicators, an trade supplier behavior fi build robust credit features weh improve model accuracy, stability, an early warning capabilities cross cycles.
Using credit bureau scores and tradelinesIncorporating public filings and legal eventsMacroeconomic indicators for credit cyclesTrade credit and supplier payment behaviorData quality checks and reconciliationLesson 5Bias and risk mitigation: label bias, sample selection, proxy variables, data imbalance and mitigation strategies (regularization, fairness checks)Examine sources a bias in credit data, such as label bias, sample selection, proxy variables, an class imbalance, an learn mitigation strategies including reweighting, regularization, fairness checks, an careful feature an policy design.
Label bias and historical decision effectsSample selection and survivorship biasProxy variables and sensitive attributesClass imbalance and resampling methodsFairness metrics and monitoring checksLesson 6Common predictive models for credit risk: logistic regression, random forest, gradient boosting (LightGBM/XGBoost)Review common predictive models fi credit risk, including logistic regression, random forests, an gradient boosting methods such as XGBoost an LightGBM, comparing strengths, limitations, tuning practices, an deployment considerations.
Logistic regression for PD estimationTree-based models and random forestsGradient boosting, XGBoost, LightGBMHyperparameter tuning and validationModel comparison and champion–challengerLesson 7Model governance: validation, backtesting, calibration, population stability, and performance monitoringUnderstand model governance fi credit risk, including independent validation, backtesting, calibration, population stability, an ongoing performance monitoring, fi satisfy regulatory expectations an maintain reliable production models.
Independent model validation scopeBacktesting and benchmarking approachesCalibration and probability of default testsPopulation stability and drift metricsOngoing performance dashboards and alertsLesson 8Decisioning: converting model outputs to risk tiers, credit limits, payment terms and approval workflowsDiscover how fi convert model outputs into operational credit decisions, including risk tiers, credit limits, payment terms, an approval workflows, while balancing risk appetite, customer experience, an portfolio profitability.
Score-to-risk tier mapping and cutoffsSetting credit limits from PD and LGDDesigning payment terms by risk bandApproval workflows and escalation rulesChampion–challenger decision strategies
