Practice · browser-run Python

Runnable credit-risk Python examples

Run every published sample, inspect its output, and edit the code. Examples use synthetic data and execute in your browser; your code and inputs are not uploaded to the site.

Scorecards and probability of default

Reproduce WOE and IV, convert a logistic score to PD, and build a cumulative PD term structure from conditional hazards.

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Calculate WOE and IV from grouped counts

Use synthetic good and bad counts for three ordered scorecard bins. The 0.5 adjustment avoids a zero distribution in this small example.

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Output

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Convert a logistic model logit to PD

Apply the logistic function to an illustrative intercept plus two feature contributions.

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Output

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Calculate year-by-year marginal and cumulative PD

Follow years 1–3 separately: conditional PD is the rate among survivors, marginal PD is the share of the original cohort defaulting in that year, and cumulative PD adds those marginal shares.

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Output

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LGD, EAD, and IFRS 9 ECL

Discount recovery cash flows, estimate undrawn exposure, and combine survival-weighted losses across scenarios.

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Discount recovery cash flows for workout LGD

Discount synthetic recoveries and costs to default date and calculate a capped loss fraction.

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Output

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Estimate revolving EAD with a CCF

Apply a hypothetical conversion factor to the currently undrawn facility limit.

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Output

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Calculate survival-weighted, discounted ECL

Combine period marginal PD, LGD, EAD, discount factors, and two scenario weights in a small illustrative horizon.

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Output

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Feature engineering

Build numeric, categorical, and date features with training-only fit rules, explicit missingness handling, fixed encoding columns, and a clear point-in-time cutoff.

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Engineer numeric credit-risk features

Use a training-only median, explicit missingness flags, a log transform, a payment-to-income ratio, and a delinquency indicator. Amounts are synthetic CAD; transforms are illustrative and should be validated for the intended population.

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from math import log1p

training = [
    {"income": 48000, "monthly_payment": 420, "utilization": 0.35, "dpd": 0},
    {"income": 72000, "monthly_payment": 900, "utilization": 0.72, "dpd": 12},
    {"income": 56000, "monthly_payment": 650, "utilization": 0.51, "dpd": 0},
]
scoring = [
    {"account": "A", "income": 60000, "monthly_payment": 750, "utilization": 0.60, "dpd": 0},
    {"account": "B", "income": None, "monthly_payment": 500, "utilization": None, "dpd": 35},
]

# Fit the imputation value on training data only; reuse it unchanged at scoring time.
income_values = sorted(row["income"] for row in training if row["income"] is not None)
training_income_median = income_values[len(income_values) // 2]
utilization_values = sorted(row["utilization"] for row in training if row["utilization"] is not None)
training_utilization_median = utilization_values[len(utilization_values) // 2]

for row in scoring:
    income_missing = row["income"] is None
    utilization_missing = row["utilization"] is None
    income = row["income"] if not income_missing else training_income_median
    utilization = row["utilization"] if not utilization_missing else training_utilization_median
    features = {
        "log_income": round(log1p(income), 4),
        "income_missing": int(income_missing),
        "payment_to_monthly_income": round(row["monthly_payment"] / (income / 12), 4),
        "utilization": utilization,
        "utilization_missing": int(utilization_missing),
        "dpd_30_plus": int(row["dpd"] >= 30),
    }
    print("{}: {}".format(row["account"], features))

Output

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Encode categorical variables with stable columns

Create one-hot features using a declared category schema, with separate missing and other buckets so unknown values do not shift the model columns.

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Output

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Build point-in-time date features

Derive account age and days since the last payment relative to a fixed scoring date. Future-dated source values fail closed instead of leaking into features.

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Output

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Data preparation, validation, and monitoring

Check point-in-time leakage, rank and calibrate PD predictions, measure distribution drift, and summarize portfolio outcomes.

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Detect a point-in-time feature leak

A miniature data preparation check compares each feature's observation date with the prediction date.

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Output

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Monitor observed-to-expected and Brier score

Compare a small set of matured binary outcomes against predicted PD values.

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Output

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Calculate ROC-AUC, Gini, KS, precision, and recall

Use synthetic out-of-sample scores and matured labels. Higher scores indicate higher risk; the example computes pairwise ROC-AUC and a threshold-specific operating point.

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scores = [0.92, 0.81, 0.65, 0.58, 0.42, 0.20]
observed_default = [1, 1, 1, 0, 0, 0]
positives = [score for score, y in zip(scores, observed_default) if y == 1]
negatives = [score for score, y in zip(scores, observed_default) if y == 0]
pairs = [(bad, good) for bad in positives for good in negatives]
auc = sum(1 if bad > good else 0.5 if bad == good else 0 for bad, good in pairs) / len(pairs)
gini = 2 * auc - 1
threshold = 0.60
tp = sum(y == 1 and score >= threshold for score, y in zip(scores, observed_default))
fp = sum(y == 0 and score >= threshold for score, y in zip(scores, observed_default))
fn = sum(y == 1 and score < threshold for score, y in zip(scores, observed_default))
precision = tp / (tp + fp) if tp + fp else 0
recall = tp / (tp + fn) if tp + fn else 0
ks = max(abs(sum(y == 1 and score >= cut for score, y in zip(scores, observed_default)) / len(positives) - sum(y == 0 and score >= cut for score, y in zip(scores, observed_default)) / len(negatives)) for cut in scores)
print(f"ROC-AUC={auc:.3f}; Gini={gini:.3f}; KS={ks:.3f}")
print(f"At threshold {threshold:.2f}: precision={precision:.3f}; recall={recall:.3f}")

Output

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Compare expected defaults, Brier score, and log loss

Review several probability metrics on the same synthetic, matured population. Small samples are noisy; this output is not an alert rule.

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Output

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Calculate PSI, feature drift, and missingness

Compare synthetic reference and current bin counts with a small smoothing constant. Inspect the bins and data-quality indicators as well as the summary index.

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Output

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Summarize rating migration, arrears roll, and recovery LGD

Use synthetic account paths to calculate grade transitions, a delinquency roll rate, and a simple discounted recovery outcome.

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Output

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These samples illustrate methods; they are not production model code or accounting advice. Confirm target definitions, horizons, data quality, and institutional policy before applying a method.