From ca98568130b98c530ee9e6981c42a7c8e479eb10 Mon Sep 17 00:00:00 2001 From: Chats-001 Date: Sat, 19 Sep 2026 19:28:01 -0400 Subject: [PATCH] fix: calibrate generated risk estimates --- README.md | 26 +- artifacts/figures/capacity_results.csv | 20 +- artifacts/figures/capacity_value.png | Bin 78930 -> 82104 bytes artifacts/figures/dashboard_overview.png | Bin 147494 -> 150613 bytes artifacts/figures/error_analysis.csv | 18 +- artifacts/figures/model_diagnostics.png | Bin 119695 -> 120076 bytes artifacts/figures/precision_recall.png | Bin 74006 -> 76889 bytes artifacts/figures/review_queue.csv | 1500 +++++----- artifacts/figures/score_distribution.png | Bin 38109 -> 36316 bytes artifacts/figures/scored_test.csv | 3498 +++++++++++----------- artifacts/figures/summary.json | 142 +- artifacts/figures/threshold_cost.png | Bin 77003 -> 75482 bytes artifacts/figures/threshold_policies.csv | 6 +- docs/RESULTS.md | 2 +- riskqueue/modeling/train.py | 15 +- riskqueue/reporting.py | 9 +- scripts/run_demo.py | 30 +- scripts/train_model.py | 25 +- tests/test_generated_results.py | 39 + tests/test_modeling.py | 22 + 20 files changed, 2728 insertions(+), 2624 deletions(-) create mode 100644 tests/test_generated_results.py create mode 100644 tests/test_modeling.py diff --git a/README.md b/README.md index 8c4b3b3..8fe7950 100644 --- a/README.md +++ b/README.md @@ -15,8 +15,8 @@ RiskQueue scores transactions, quantifies the dollars exposed, and places the mo | Rare-event ranking | Operational outcome at 750 reviews | Decision policy | Engineering | |---|---|---|---| -| **0.130 average precision** | **97.1% fraud value captured** with expected-loss ranking | **$9,034 modeled cost** at the validation-selected cost threshold | **10.90 ms p95** for batch 1,000 | -| 3.49% demo fraud rate | vs. 95.1% using probability alone | vs. $34,205 at threshold 0.50 | In-process local benchmark; 57 tests pass | +| **0.113 average precision** | **96.9% fraud value captured** with expected-loss ranking | **$8,280 modeled cost** at the validation-selected cost threshold | **10.90 ms p95** for batch 1,000 | +| 3.49% demo fraud rate | vs. 92.3% using probability alone | vs. $38,906 at threshold 0.50 | In-process local benchmark; 60 tests pass | ## Results at a glance @@ -28,21 +28,21 @@ Fraud is a small share of transactions, so accuracy is not a useful headline. Pr | Held-out demo model | Average precision | ROC-AUC | Brier score | |---|---:|---:|---:| -| Logistic Regression | 0.097 | **0.749** | 0.158 | -| Histogram Gradient Boosting demo fallback | **0.130** | 0.745 | **0.033** | +| Logistic Regression + sigmoid calibration | 0.097 | **0.749** | 0.033 | +| Histogram Gradient Boosting demo fallback + sigmoid calibration | **0.113** | 0.734 | **0.033** | ### 2. What happens when analysts can review only a limited queue? ![Analyst capacity versus fraud value](artifacts/figures/capacity_value.png) -The chart measures how much fraudulent transaction value appears in the first *k* reviews. At 750 demo reviews, probability ranking captures 95.1% of fraudulent value, while expected-loss ranking captures 97.1%. The comparison matters because a slightly less likely but much larger transaction can deserve earlier review when the objective is dollars protected. +The chart measures how much fraudulent transaction value appears in the first *k* reviews. At 750 demo reviews, probability ranking captures 92.3% of fraudulent value, while expected-loss ranking captures 96.9%. The comparison matters because a slightly less likely but much larger transaction can deserve earlier review when the objective is dollars protected. | Reviews | Probability queue | Expected-loss queue | Improvement | |---:|---:|---:|---:| -| 100 | 53.6% | **68.3%** | +14.7 points | -| 250 | 79.0% | **85.5%** | +6.5 points | -| 500 | 87.2% | **94.2%** | +7.0 points | -| 750 | 95.1% | **97.1%** | +2.0 points | +| 100 | 55.5% | **68.3%** | +12.8 points | +| 250 | 76.9% | **84.1%** | +7.2 points | +| 500 | 86.9% | **94.7%** | +7.8 points | +| 750 | 92.3% | **96.9%** | +4.6 points | ### 3. Why is 0.50 not automatically the right threshold? @@ -52,9 +52,9 @@ The score estimates risk; it does not decide the operating policy by itself. The | Policy | Threshold | Reviews | Precision | Recall | Modeled cost | |---|---:|---:|---:|---:|---:| -| Default | 0.50 | 2 | 100.0% | 3.3% | $34,205 | -| F1-optimal | 0.15 | 78 | 16.7% | 21.3% | $18,613 | -| Cost-optimized | **0.03** | 291 | 9.3% | **44.3%** | **$9,034** | +| Default | 0.50 | 0 | 0.0% | 0.0% | $38,906 | +| F1-optimal | 0.12 | 75 | 16.0% | 19.7% | $18,628 | +| Cost-optimized | **0.04** | 312 | 8.7% | **44.3%** | **$8,280** | ### 4. Would the system notice changed transaction behavior? @@ -90,6 +90,8 @@ flowchart LR The primary split uses whole time steps: earliest 70% for training, next 15% for validation and final 15% for test. History features update state only after a row is transformed. `isFlaggedFraud` and the four balance columns never enter the model feature list. +After validation average precision selects the base model, sigmoid calibration is fitted on that chronological validation period. Thresholds are selected from the calibrated validation probabilities, and the calibrated model is evaluated once on the final test period. + ## Explore in three minutes ```bash diff --git a/artifacts/figures/capacity_results.csv b/artifacts/figures/capacity_results.csv index d3507bb..c2edd1b 100644 --- a/artifacts/figures/capacity_results.csv +++ b/artifacts/figures/capacity_results.csv @@ -7,16 +7,16 @@ probability,500,500.0,0.076,0.6229508196721312,33823.16,0.8693496268468743 probability,750,750.0,0.06,0.7377049180327869,35914.66,0.9231070151142697 probability,1000,1000.0,0.052,0.8524590163934426,38237.840000000004,0.9828192260992316 expected_loss,25,25.0,0.24,0.09836065573770492,16538.129999999997,0.4250761059654123 -expected_loss,50,50.0,0.16,0.13114754098360656,20359.809999999998,0.5233039499021752 -expected_loss,100,100.0,0.15,0.2459016393442623,27137.33,0.697505132847448 -expected_loss,250,250.0,0.092,0.3770491803278688,31930.4,0.8207004113474742 +expected_loss,50,50.0,0.18,0.14754098360655737,22220.8,0.5711365877179726 +expected_loss,100,100.0,0.14,0.22950819672131148,26564.61,0.6827846301419721 +expected_loss,250,250.0,0.088,0.36065573770491804,32707.73,0.8406799622066157 expected_loss,500,500.0,0.072,0.5901639344262295,36863.61,0.9474976790379345 -expected_loss,750,750.0,0.058666666666666666,0.7213114754098361,37817.25,0.972008889053387 -expected_loss,1000,1000.0,0.052,0.8524590163934426,38650.560000000005,0.9934272821765538 +expected_loss,750,750.0,0.056,0.6885245901639344,37719.46,0.9694954130798423 +expected_loss,1000,1000.0,0.049,0.8032786885245902,38517.51,0.9900075257773298 expected_review_value,25,25.0,0.24,0.09836065573770492,16538.129999999997,0.4250761059654123 -expected_review_value,50,50.0,0.16,0.13114754098360656,20359.809999999998,0.5233039499021752 -expected_review_value,100,100.0,0.15,0.2459016393442623,27137.33,0.697505132847448 -expected_review_value,250,250.0,0.092,0.3770491803278688,31930.4,0.8207004113474742 +expected_review_value,50,50.0,0.18,0.14754098360655737,22220.8,0.5711365877179726 +expected_review_value,100,100.0,0.14,0.22950819672131148,26564.61,0.6827846301419721 +expected_review_value,250,250.0,0.088,0.36065573770491804,32707.73,0.8406799622066157 expected_review_value,500,500.0,0.072,0.5901639344262295,36863.61,0.9474976790379345 -expected_review_value,750,750.0,0.058666666666666666,0.7213114754098361,37817.25,0.972008889053387 -expected_review_value,1000,1000.0,0.052,0.8524590163934426,38650.560000000005,0.9934272821765538 +expected_review_value,750,750.0,0.056,0.6885245901639344,37719.46,0.9694954130798423 +expected_review_value,1000,1000.0,0.049,0.8032786885245902,38517.51,0.9900075257773298 diff --git a/artifacts/figures/capacity_value.png b/artifacts/figures/capacity_value.png index da3bab43aab37613484d4053d2ffd7ac36109597..e6c367db8b03c86d146e56cc82d91d49977d5b8b 100644 GIT binary patch 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Logistic regression versus nonlinear gradient boosting. 3. Average precision, ROC-AUC, Brier, log loss, precision, recall, and F1. 4. 0.50, validation F1-optimal, and validation cost-optimized thresholds. -5. Probability versus expected-loss review queues across five capacity levels. +5. Probability versus expected-loss review queues across seven capacity levels. 6. A shifted-batch PSI monitoring demonstration. 7. A local in-process API benchmark: batch 1,000 median 7.50 ms and p95 10.90 ms. diff --git a/riskqueue/modeling/train.py b/riskqueue/modeling/train.py index ce92994..4593543 100644 --- a/riskqueue/modeling/train.py +++ b/riskqueue/modeling/train.py @@ -12,6 +12,11 @@ from sklearn.pipeline import Pipeline from sklearn.preprocessing import OneHotEncoder, StandardScaler +try: + from sklearn.frozen import FrozenEstimator +except ImportError: # scikit-learn 1.5 uses the older prefit calibration API. + FrozenEstimator = None + from riskqueue.features.schema import CATEGORICAL_FEATURES, MODEL_FEATURES, assert_leakage_safe @@ -91,6 +96,14 @@ def train_boosted(frame: pd.DataFrame, labels) -> TrainedModel: def calibrate_prefit(model: TrainedModel, validation: pd.DataFrame, labels) -> TrainedModel: - calibrated = CalibratedClassifierCV(model.estimator, method="sigmoid", cv="prefit") + if FrozenEstimator is None: + calibrated = CalibratedClassifierCV(model.estimator, method="sigmoid", cv="prefit") + else: + indices = np.arange(len(validation)) + calibrated = CalibratedClassifierCV( + FrozenEstimator(model.estimator), + method="sigmoid", + cv=[(indices, indices)], + ) calibrated.fit(validation[model.feature_names], labels) return TrainedModel(f"{model.name} + sigmoid calibration", calibrated, model.feature_names) diff --git a/riskqueue/reporting.py b/riskqueue/reporting.py index bd15613..41a8da6 100644 --- a/riskqueue/reporting.py +++ b/riskqueue/reporting.py @@ -32,6 +32,8 @@ def generate_report_artifacts( model_probabilities: dict[str, np.ndarray], output: str | Path, threshold_source: tuple[np.ndarray, np.ndarray, np.ndarray] | None = None, + *, + selected_model: str, ) -> dict: output = Path(output) output.mkdir(parents=True, exist_ok=True) @@ -39,6 +41,10 @@ def generate_report_artifacts( metrics = { name: classification_metrics(labels, probs) for name, probs in model_probabilities.items() } + if selected_model not in metrics: + raise ValueError(f"Selected model is missing probabilities: {selected_model}") + best_name = selected_model + best_probs = model_probabilities[best_name] fig, ax = plt.subplots(figsize=(8.4, 5.2)) prevalence = labels.mean() @@ -63,9 +69,6 @@ def generate_report_artifacts( ax.legend(frameon=False) _finish(fig, output / "precision_recall.png") - best_name = max(metrics, key=lambda name: metrics[name]["average_precision"]) - best_probs = model_probabilities[best_name] - # Diagnostics used by the model-performance dashboard. fig, axes = plt.subplots(1, 3, figsize=(14, 4.2)) false_positive_rate, true_positive_rate, _ = roc_curve(labels, best_probs) diff --git a/scripts/run_demo.py b/scripts/run_demo.py index c2b6613..cb15f2a 100644 --- a/scripts/run_demo.py +++ b/scripts/run_demo.py @@ -3,10 +3,12 @@ import argparse from pathlib import Path +from sklearn.metrics import average_precision_score + from riskqueue.data.splits import temporal_split from riskqueue.demo import make_demo_transactions from riskqueue.features.pipeline import build_features -from riskqueue.modeling.train import train_boosted, train_logistic +from riskqueue.modeling.train import calibrate_prefit, train_boosted, train_logistic from riskqueue.reporting import generate_report_artifacts @@ -17,27 +19,37 @@ def main() -> None: args = parser.parse_args() frame = build_features(make_demo_transactions(args.rows)) split = temporal_split(frame) - models = [ + base_models = [ train_logistic(split.train, split.train.isFraud), train_boosted(split.train, split.train.isFraud), ] - validation_probs = {m.name: m.predict_proba(split.validation) for m in models} - test_probs = {m.name: m.predict_proba(split.test) for m in models} - best = max( - models, - key=lambda m: __import__("sklearn.metrics").metrics.average_precision_score( - split.validation.isFraud, validation_probs[m.name] + base_validation_probs = { + model.name: model.predict_proba(split.validation) for model in base_models + } + selected_base = max( + base_models, + key=lambda model: average_precision_score( + split.validation.isFraud, base_validation_probs[model.name] ), ) + calibrated = { + model.name: calibrate_prefit(model, split.validation, split.validation.isFraud) + for model in base_models + } + models = list(calibrated.values()) + test_probs = {m.name: m.predict_proba(split.test) for m in models} + best = calibrated[selected_base.name] + validation_probs = best.predict_proba(split.validation) summary = generate_report_artifacts( split.test, test_probs, args.output, ( split.validation.isFraud.to_numpy(), - validation_probs[best.name], + validation_probs, split.validation.amount.to_numpy(), ), + selected_model=best.name, ) print(f"Generated report for {summary['transactions']:,} held-out demo transactions") print(f"Best demo model: {summary['best_model']}") diff --git a/scripts/train_model.py b/scripts/train_model.py index 7dafe1e..2954864 100644 --- a/scripts/train_model.py +++ b/scripts/train_model.py @@ -10,7 +10,7 @@ from riskqueue.features.pipeline import build_features from riskqueue.modeling.artifacts import ModelMetadata, save_artifact from riskqueue.modeling.evaluate import classification_metrics -from riskqueue.modeling.train import train_boosted, train_logistic +from riskqueue.modeling.train import calibrate_prefit, train_boosted, train_logistic from riskqueue.reporting import generate_report_artifacts @@ -21,14 +21,26 @@ def main() -> None: args = parser.parse_args() featured = build_features(load_paysim(args.csv, sample_rows=args.sample_rows)) split = temporal_split(featured) - models = [ + base_models = [ train_logistic(split.train, split.train.isFraud), train_boosted(split.train, split.train.isFraud), ] - validation = {m.name: m.predict_proba(split.validation) for m in models} - best = max( - models, key=lambda m: average_precision_score(split.validation.isFraud, validation[m.name]) + base_validation = { + model.name: model.predict_proba(split.validation) for model in base_models + } + selected_base = max( + base_models, + key=lambda model: average_precision_score( + split.validation.isFraud, base_validation[model.name] + ), ) + calibrated = { + model.name: calibrate_prefit(model, split.validation, split.validation.isFraud) + for model in base_models + } + models = list(calibrated.values()) + best = calibrated[selected_base.name] + validation = best.predict_proba(split.validation) test_probabilities = {m.name: m.predict_proba(split.test) for m in models} summary = generate_report_artifacts( split.test, @@ -36,9 +48,10 @@ def main() -> None: "artifacts/figures", ( split.validation.isFraud.to_numpy(), - validation[best.name], + validation, split.validation.amount.to_numpy(), ), + selected_model=best.name, ) metrics = classification_metrics( split.test.isFraud, test_probabilities[best.name], summary["selected_threshold"] diff --git a/tests/test_generated_results.py b/tests/test_generated_results.py new file mode 100644 index 0000000..eeed8e6 --- /dev/null +++ b/tests/test_generated_results.py @@ -0,0 +1,39 @@ +import json +from pathlib import Path + +ROOT = Path(__file__).resolve().parents[1] + + +def test_generated_results_use_calibrated_models(): + summary = json.loads((ROOT / "artifacts/figures/summary.json").read_text()) + + assert "sigmoid calibration" in summary["best_model"] + assert all("sigmoid calibration" in name for name in summary["metrics"]) + + +def test_readme_headlines_match_generated_results(): + summary = json.loads((ROOT / "artifacts/figures/summary.json").read_text()) + readme = (ROOT / "README.md").read_text() + best_metrics = summary["metrics"][summary["best_model"]] + expected_loss = next( + row + for row in summary["capacity_results"] + if row["strategy"] == "expected_loss" and row["capacity"] == 750 + ) + probability = next( + row + for row in summary["capacity_results"] + if row["strategy"] == "probability" and row["capacity"] == 750 + ) + cost_policy = next( + row for row in summary["threshold_policies"] if row["policy"] == "Cost-optimized" + ) + default_policy = next( + row for row in summary["threshold_policies"] if row["policy"] == "Default 0.50" + ) + + assert f"**{best_metrics['average_precision']:.3f} average precision**" in readme + assert f"**{expected_loss['fraud_value_capture']:.1%} fraud value captured**" in readme + assert f"vs. {probability['fraud_value_capture']:.1%} using probability alone" in readme + assert f"**${cost_policy['modeled_cost']:,.0f} modeled cost**" in readme + assert f"vs. ${default_policy['modeled_cost']:,.0f} at threshold 0.50" in readme diff --git a/tests/test_modeling.py b/tests/test_modeling.py new file mode 100644 index 0000000..2987faf --- /dev/null +++ b/tests/test_modeling.py @@ -0,0 +1,22 @@ +import numpy as np +import pandas as pd +from sklearn.linear_model import LogisticRegression + +from riskqueue.modeling.train import TrainedModel, calibrate_prefit + + +def test_prefit_calibration_uses_validation_probabilities(): + training = pd.DataFrame({"signal": [-3.0, -2.0, -1.0, 1.0, 2.0, 3.0]}) + labels = np.array([0, 0, 0, 1, 1, 1]) + estimator = LogisticRegression().fit(training, labels) + coefficients = estimator.coef_.copy() + model = TrainedModel("test model", estimator, ["signal"]) + + calibrated = calibrate_prefit(model, training, labels) + probabilities = calibrated.predict_proba(training) + + np.testing.assert_array_equal(estimator.coef_, coefficients) + assert calibrated.name == "test model + sigmoid calibration" + assert probabilities.shape == (len(training),) + assert np.isfinite(probabilities).all() + assert ((0 <= probabilities) & (probabilities <= 1)).all()

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-demo-011024,330,CASH_OUT,4210.22,0.2815364943213422,1185.3305791216014,guarded,4,0 -demo-010276,306,CASH_OUT,5768.83,0.20280216362447115,1169.931205581758,guarded,5,0 -demo-011902,357,TRANSFER,4874.63,0.1881295409162225,917.0619040364458,guarded,6,1 -demo-010959,328,CASH_OUT,4973.46,0.17958803999336334,893.1739333853928,guarded,7,0 -demo-011484,345,TRANSFER,4041.15,0.20425568974700578,825.4278806211124,guarded,8,0 -demo-010814,323,TRANSFER,1630.12,0.4719855419642954,769.3930716668372,high,9,0 -demo-011222,335,CASH_OUT,1930.38,0.3767229672532071,727.2184815262459,guarded,10,0 -demo-010514,313,CASH_OUT,3427.36,0.2107727148043141,722.393971811714,guarded,11,0 -demo-010898,326,TRANSFER,1678.31,0.4114156284957513,690.4829634607044,high,12,1 -demo-011770,353,TRANSFER,3645.57,0.17732988273305259,646.4685005951345,guarded,13,1 -demo-010979,329,TRANSFER,1400.55,0.4463661816162728,625.1581556626709,high,14,0 -demo-011614,348,CASH_OUT,2645.57,0.2239964640718304,592.5983254545124,guarded,15,0 -demo-011216,335,CASH_OUT,2491.87,0.23357702167837294,582.0435730096872,guarded,16,0 -demo-011689,351,CASH_OUT,6700.6,0.08643261755569402,579.1503971936834,low,17,0 -demo-010393,309,TRANSFER,2887.78,0.1790959238012817,517.1896268348653,guarded,18,0 -demo-010462,312,CASH_OUT,1749.26,0.2922334931303382,511.1923601931754,guarded,19,0 -demo-011542,346,CASH_OUT,1947.9,0.2602925145216644,507.02378903675014,guarded,20,1 -demo-010885,326,CASH_OUT,1360.27,0.3672312928298511,499.53371069766155,guarded,21,1 -demo-010775,322,TRANSFER,806.98,0.6117483591728898,493.66869088533866,high,22,0 -demo-011808,354,CASH_OUT,1091.99,0.4455902114493307,486.58005500055464,high,23,0 -demo-011710,351,TRANSFER,3678.4,0.12834979265153373,472.1218772894017,guarded,24,0 -demo-010926,327,TRANSFER,1444.47,0.325179806200668,469.7124746626789,guarded,25,0 -demo-011258,336,CASH_OUT,2440.57,0.14743321483734895,359.82108113558877,guarded,26,0 -demo-010564,315,TRANSFER,1928.62,0.1860373421670273,358.7953388501722,guarded,27,0 -demo-011695,351,TRANSFER,801.77,0.4157109711205334,333.3045853153101,high,28,0 -demo-010880,325,CASH_OUT,1377.09,0.24049742577753758,331.1866000639892,guarded,29,0 -demo-010873,325,TRANSFER,850.3,0.38120682747255197,324.1401653999109,guarded,30,0 -demo-010330,308,CASH_OUT,2178.18,0.14836112699809417,323.15723960470876,guarded,31,0 -demo-011768,353,CASH_OUT,1137.37,0.28089543117800425,319.48203655892667,guarded,32,0 -demo-011387,341,PAYMENT,813.02,0.3777287428228512,307.10102248983446,guarded,33,0 -demo-011067,331,CASH_OUT,1261.98,0.24254318687046902,306.0846509667945,guarded,34,0 -demo-010444,311,CASH_OUT,956.9,0.317578943367305,303.89129090817414,guarded,35,0 -demo-011501,345,TRANSFER,1223.08,0.24457586401574816,299.13584776038124,guarded,36,0 -demo-011777,354,CASH_OUT,2525.09,0.11581142341679401,292.43426715551243,guarded,37,0 -demo-011118,332,TRANSFER,810.64,0.359482172041246,291.41062794351564,guarded,38,0 -demo-011319,338,TRANSFER,2774.07,0.10452501946729732,289.9597207536455,guarded,39,1 -demo-011140,333,TRANSFER,1060.22,0.2641079067512695,280.01248489583094,guarded,40,0 -demo-010765,321,TRANSFER,746.29,0.3678932802667345,274.5550761302613,guarded,41,0 -demo-011601,348,CASH_OUT,2913.24,0.0891808414177673,259.8051944518964,low,42,0 -demo-011276,337,TRANSFER,611.83,0.4090097842233084,250.2444562813468,high,43,0 -demo-011592,348,CASH_OUT,737.9,0.3388634236807321,250.0473203340122,guarded,44,0 -demo-011291,337,CASH_OUT,659.95,0.3745275920311578,247.16948436096263,guarded,45,0 -demo-011332,338,CASH_OUT,1047.61,0.23524478335561066,246.44478749117127,guarded,46,1 -demo-010728,320,TRANSFER,3314.46,0.07320602626613629,242.63844581805807,low,47,0 -demo-010928,327,CASH_OUT,1186.46,0.20417913628936737,242.2503780418828,guarded,48,0 -demo-011324,338,CASH_OUT,830.98,0.27870944740124737,231.60197660148853,guarded,49,0 -demo-011654,349,CASH_OUT,1344.3,0.17142241266297706,230.44314934284006,guarded,50,0 -demo-010861,325,TRANSFER,677.78,0.3361514466773588,227.83672752898025,guarded,51,0 -demo-011091,332,TRANSFER,640.54,0.35133399832410983,225.0434792865253,guarded,52,0 -demo-010929,327,CASH_OUT,964.76,0.22910159345811634,221.02805330465233,guarded,53,0 -demo-011877,356,TRANSFER,639.98,0.3395235057169872,217.28825318875747,guarded,54,0 -demo-011909,357,TRANSFER,819.83,0.263046529723771,215.6534364634392,guarded,55,0 -demo-011580,347,CASH_OUT,1860.99,0.11098534651050004,206.5426200025755,guarded,56,1 -demo-011204,335,CASH_OUT,2768.68,0.07315300379549035,202.53725854849822,low,57,0 -demo-011206,335,TRANSFER,1341.12,0.15059788285179293,201.9698326501965,guarded,58,0 -demo-011734,352,TRANSFER,1190.84,0.16791298724059855,199.95750172559437,guarded,59,0 -demo-011057,331,TRANSFER,1329.42,0.15026591514440904,199.76651291128027,guarded,60,0 -demo-011634,349,TRANSFER,776.52,0.25656382094427693,199.2269382396499,guarded,61,1 -demo-011342,339,CASH_OUT,924.29,0.20895551390987815,193.13549195176128,guarded,62,0 -demo-010925,327,TRANSFER,690.02,0.27579086050893215,190.30120956837337,guarded,63,0 -demo-011359,340,TRANSFER,666.89,0.2829460479142587,188.69388989354,guarded,64,1 -demo-011972,359,CASH_OUT,918.35,0.20195978092858763,185.46976481576846,guarded,65,0 -demo-011322,338,TRANSFER,3363.65,0.05361855056479928,180.3540376072871,low,66,0 -demo-010810,323,TRANSFER,854.85,0.20570314827749228,175.84533630501429,guarded,67,0 -demo-011356,339,PAYMENT,870.56,0.19765627692120488,172.0716484365241,guarded,68,0 -demo-011274,337,CASH_OUT,1131.09,0.15203768499848871,171.9683051249406,guarded,69,0 -demo-010352,308,CASH_OUT,1209.72,0.13549689018025574,163.91329798885897,guarded,70,0 -demo-010915,327,PAYMENT,780.64,0.20895029545224716,163.11495864184224,guarded,71,0 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transaction_id,step,type,amount,nameOrig,nameDest,isFraud,isFlaggedFraud,sender_count_1h,sender_count_6h,sender_count_24h,sender_total_amount_24h,sender_average_amount_24h,sender_historical_median_amount,sender_unique_recipients_24h,sender_hours_since_prior,amount_to_sender_median,recipient_incoming_count_24h,recipient_unique_senders_24h,recipient_total_received_24h,recipient_hours_since_prior,prior_pair_count,first_time_recipient,hour,day_index,fraud_probability,expected_loss,risk_band -demo-010251,306,PAYMENT,111.4,C0000361,M0000673,0,0,0.0,0.0,0.0,0.0,0.0,148.45,0.0,40.0,0.750421017177501,1.0,1.0,125.76,13.0,0.0,1.0,18,12,0.0013927338323821816,0.15515054892737504,low -demo-010252,306,PAYMENT,285.19,C0000920,M0000351,0,0,0.0,0.0,2.0,133.59,66.795,84.66,2.0,15.0,3.3686510748877865,0.0,0.0,0.0,145.0,0.0,1.0,18,12,0.0038788285256947334,1.106203107242881,low -demo-010253,306,CASH_IN,308.65,C0000482,M0000996,0,0,0.0,1.0,2.0,382.06,191.03,213.29500000000002,2.0,4.0,1.447056893035467,0.0,0.0,0.0,73.0,0.0,1.0,18,12,0.0014587989230553636,0.4502582876010379,low -demo-010254,306,CASH_OUT,37.79,C0001423,M0000989,1,0,0.0,0.0,0.0,0.0,0.0,252.3,0.0,113.0,0.14978200554894966,1.0,1.0,61.25,23.0,0.0,1.0,18,12,0.004679768775902645,0.17684846204136095,low -demo-010255,306,CASH_OUT,40.23,C0000622,M0001778,0,0,0.0,0.0,0.0,0.0,0.0,181.71,0.0,89.0,0.22139673105497767,1.0,1.0,79.7,1.0,0.0,1.0,18,12,0.014617443146043164,0.5880597377653165,low -demo-010256,306,PAYMENT,287.49,C0001122,M0000016,0,0,0.0,0.0,0.0,0.0,0.0,89.09,0.0,33.0,3.226961499607139,1.0,1.0,9.99,16.0,0.0,1.0,18,12,0.006200481625503122,1.7825764625158926,low -demo-010257,306,CASH_IN,57.57,C0000401,M0001748,0,0,0.0,0.0,0.0,0.0,0.0,131.99,0.0,92.0,0.43616940677324034,0.0,0.0,0.0,120.0,0.0,1.0,18,12,0.0042481867849006195,0.24456811320672867,low -demo-010258,306,PAYMENT,5.27,C0001250,M0000422,0,0,0.0,0.0,0.0,0.0,0.0,73.94,0.0,38.0,0.0712740059507709,0.0,0.0,0.0,68.0,0.0,1.0,18,12,0.0007043691888250871,0.003712025625108209,low -demo-010259,306,PAYMENT,33.11,C0000433,M0000351,0,0,0.0,0.0,1.0,971.09,971.09,73.725,1.0,13.0,0.44910139030179724,0.0,0.0,0.0,145.0,0.0,1.0,18,12,0.0005739871888736839,0.019004715823607674,low -demo-010260,306,CASH_IN,47.63,C0000761,M0000836,0,0,0.0,0.0,0.0,0.0,0.0,63.79,0.0,114.0,0.7466687568584418,0.0,0.0,0.0,56.0,0.0,1.0,18,12,0.0013068115170154191,0.062243432555444414,low -demo-010261,306,TRANSFER,133.9,C0001202,M0001102,0,0,0.0,0.0,0.0,0.0,0.0,699.09,0.0,48.0,0.19153470940794462,2.0,2.0,64.35,8.0,0.0,1.0,18,12,0.011500214114584028,1.5398786699428013,low -demo-010262,306,TRANSFER,514.89,C0001389,M0001859,0,0,0.0,1.0,1.0,80.95,80.95,86.905,1.0,3.0,5.9247454116564064,0.0,0.0,0.0,82.0,0.0,1.0,18,12,0.06116272678488486,31.492076394269365,low -demo-010263,306,CASH_IN,25.81,C0001127,M0001068,0,0,0.0,0.0,0.0,0.0,0.0,51.4,0.0,42.0,0.5021400778210117,0.0,0.0,0.0,109.0,0.0,1.0,18,12,0.002474450934696176,0.0638655786245083,low -demo-010264,306,TRANSFER,637.28,C0001001,M0000290,0,0,0.0,0.0,1.0,113.95,113.95,82.78,1.0,17.0,7.69847789321092,0.0,0.0,0.0,61.0,0.0,1.0,18,12,0.0476265705578906,30.35146088513252,low -demo-010265,306,CASH_OUT,282.65,C0001038,M0000900,1,0,0.0,0.0,2.0,882.9,441.45,114.09,2.0,12.0,2.4774300990446134,0.0,0.0,0.0,171.0,0.0,1.0,18,12,0.006076479574860472,1.7175169518343123,low -demo-010266,306,PAYMENT,16.51,C0000609,M0001841,0,0,0.0,1.0,1.0,11.81,11.81,199.99,1.0,3.0,0.08255412770638533,1.0,1.0,109.75,17.0,0.0,1.0,18,12,0.0005048377498239325,0.008334871249593126,low -demo-010267,306,CASH_OUT,132.75,C0001030,M0001429,0,0,0.0,0.0,3.0,994.22,331.40666666666664,131.915,3.0,8.0,1.0063298336049729,0.0,0.0,0.0,235.0,0.0,1.0,18,12,0.013199980665977737,1.7522974334085446,low -demo-010268,306,PAYMENT,153.03,C0000862,M0000435,0,0,0.0,0.0,1.0,45.7,45.7,98.42,1.0,8.0,1.5548668969721602,0.0,0.0,0.0,91.0,0.0,1.0,18,12,0.0035510485938886373,0.5434169663227781,low -demo-010269,306,PAYMENT,86.64,C0000782,M0001291,0,0,0.0,0.0,0.0,0.0,0.0,150.47,0.0,28.0,0.5757958397022662,0.0,0.0,0.0,64.0,0.0,1.0,18,12,0.0032614117176031207,0.28256871121313437,low -demo-010270,306,TRANSFER,317.83,C0000469,M0001467,0,0,0.0,0.0,1.0,62.82,62.82,233.945,1.0,19.0,1.3585671845946696,0.0,0.0,0.0,70.0,0.0,1.0,18,12,0.01993705778269707,6.33659507507461,low -demo-010271,306,TRANSFER,94.97,C0000526,M0000550,0,0,0.0,0.0,1.0,37.14,37.14,66.64,1.0,18.0,1.4251200480192077,0.0,0.0,0.0,94.0,0.0,1.0,18,12,0.026596605384054096,2.5258796133236174,low -demo-010272,306,CASH_IN,164.97,C0000185,M0001055,0,0,0.0,0.0,0.0,0.0,0.0,708.69,0.0,91.0,0.23278161114168394,1.0,1.0,91.02,17.0,0.0,1.0,18,12,0.0018579297618307473,0.3065026728092184,low 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-demo-010313,307,TRANSFER,221.84,C0000025,M0000010,0,0,0.0,0.0,0.0,0.0,0.0,228.28,0.0,31.0,0.9717890310145435,0.0,0.0,0.0,188.0,0.0,1.0,19,12,0.04938748697337403,10.956120110173295,low -demo-010314,307,CASH_IN,174.93,C0000847,M0001089,0,0,0.0,0.0,0.0,0.0,0.0,173.11,0.0,29.0,1.0105135463000403,1.0,1.0,209.11,9.0,0.0,1.0,19,12,0.0012303209200353697,0.21522003854178723,low -demo-010315,307,TRANSFER,410.27,C0000716,M0001870,0,0,0.0,0.0,0.0,0.0,0.0,247.59,0.0,69.0,1.6570540005654508,1.0,1.0,266.35,19.0,0.0,1.0,19,12,0.021805243765505748,8.946037359674042,low -demo-010316,307,PAYMENT,12.65,C0000986,M0001793,0,0,0.0,0.0,0.0,0.0,0.0,145.49,0.0,27.0,0.08694755653309505,0.0,0.0,0.0,79.0,0.0,1.0,19,12,0.0007401433155733522,0.009362812942002906,low -demo-010317,307,PAYMENT,479.27,C0000389,M0001808,0,0,0.0,0.0,0.0,0.0,0.0,93.4,0.0,37.0,5.1313704496788,1.0,1.0,200.64,3.0,0.0,1.0,19,12,0.003623596428787165,1.7366810604248244,low 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-demo-010883,326,CASH_OUT,360.15,C0000408,M0000565,0,0,0.0,0.0,0.0,0.0,0.0,45.91,0.0,50.0,7.8446961446308,0.0,0.0,0.0,50.0,0.0,1.0,14,13,0.04368741620248619,15.7340229453254,low -demo-010884,326,PAYMENT,254.86,C0000350,M0000632,0,0,0.0,0.0,0.0,0.0,0.0,130.05,0.0,51.0,1.9597078046905037,1.0,1.0,61.84,22.0,0.0,1.0,14,13,0.00253400425185171,0.6458163236269269,low -demo-010885,326,CASH_OUT,1360.27,C0001472,M0001234,1,0,0.0,0.0,0.0,0.0,0.0,93.61000000000001,0.0,59.0,14.531246661681442,0.0,0.0,0.0,37.0,0.0,1.0,14,13,0.3672312928298511,499.53371069766155,guarded -demo-010886,326,PAYMENT,27.17,C0000036,M0001471,0,0,0.0,0.0,0.0,0.0,0.0,127.75,0.0,98.0,0.21268101761252448,0.0,0.0,0.0,128.0,0.0,1.0,14,13,0.004286539801651713,0.11646528641087704,low -demo-010887,326,TRANSFER,1304.35,C0000408,M0000325,0,0,0.0,0.0,0.0,0.0,0.0,45.91,0.0,50.0,28.411021563929427,0.0,0.0,0.0,70.0,0.0,1.0,14,13,0.08371070068203837,109.18805243461674,low 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-demo-010913,327,PAYMENT,138.05,C0000683,M0001024,0,0,0.0,1.0,1.0,412.16,412.16,99.89,1.0,5.0,1.382020222244469,0.0,0.0,0.0,29.0,0.0,1.0,15,13,0.0011754161451101093,0.1622661988324506,low -demo-010914,327,PAYMENT,21.53,C0001430,M0001350,0,0,0.0,0.0,0.0,0.0,0.0,164.2,0.0,63.0,0.1311205846528624,0.0,0.0,0.0,48.0,0.0,1.0,15,13,0.0023430488280909816,0.050445841268798836,low -demo-010915,327,PAYMENT,780.64,C0001074,M0001902,0,0,0.0,1.0,1.0,18.32,18.32,118.885,1.0,4.0,6.566345628128023,0.0,0.0,0.0,220.0,0.0,1.0,15,13,0.20895029545224716,163.11495864184224,guarded -demo-010916,327,CASH_OUT,249.59,C0000402,M0001670,0,0,0.0,0.0,1.0,73.31,73.31,171.21,1.0,15.0,1.4578003621283804,1.0,1.0,759.44,10.0,0.0,1.0,15,13,0.0043657338017179075,1.0896434995707724,low -demo-010917,327,CASH_OUT,97.51,C0000245,M0000640,0,0,0.0,0.0,0.0,0.0,0.0,108.07499999999999,0.0,67.0,0.9022438121674764,0.0,0.0,0.0,39.0,0.0,1.0,15,13,0.054118655000057476,5.277110049055604,low 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-demo-011538,346,PAYMENT,44.17,C0000745,M0000375,0,0,0.0,0.0,0.0,0.0,0.0,114.33,0.0,58.0,0.3863377941047844,1.0,1.0,1341.12,11.0,0.0,1.0,10,14,0.005349675473292163,0.23629516565531486,low -demo-011539,346,PAYMENT,26.24,C0001380,M0000602,0,0,0.0,0.0,0.0,0.0,0.0,107.61500000000001,0.0,34.0,0.24383217952887606,0.0,0.0,0.0,39.0,0.0,1.0,10,14,0.0016055030764908825,0.042128400727120756,low -demo-011540,346,TRANSFER,141.01,C0001210,M0000358,0,0,0.0,0.0,1.0,15.45,15.45,136.99,1.0,18.0,1.0293452076793925,2.0,2.0,2981.3,10.0,0.0,1.0,10,14,0.01304410332749323,1.8393490102098202,low -demo-011541,346,CASH_OUT,129.9,C0000271,M0001615,0,0,0.0,0.0,0.0,0.0,0.0,143.28,0.0,64.0,0.9066164154103853,0.0,0.0,0.0,83.0,0.0,1.0,10,14,0.017410282781514554,2.2615957333187406,low -demo-011542,346,CASH_OUT,1947.9,C0000415,M0001867,1,0,0.0,0.0,1.0,76.33,76.33,121.04499999999999,1.0,10.0,16.092362344582597,1.0,1.0,249.6,23.0,0.0,1.0,10,14,0.2602925145216644,507.02378903675014,guarded -demo-011543,346,CASH_OUT,683.97,C0000277,M0000176,0,0,0.0,0.0,0.0,0.0,0.0,296.74,0.0,27.0,2.304947091730134,0.0,0.0,0.0,26.0,0.0,1.0,10,14,0.2182188353217816,149.25513679503896,guarded -demo-011544,346,PAYMENT,69.74,C0001366,M0000478,0,0,0.0,0.0,0.0,0.0,0.0,145.17,0.0,67.0,0.4804022869738927,0.0,0.0,0.0,140.0,0.0,1.0,10,14,0.003523342309315889,0.24571789265169008,low -demo-011545,346,CASH_OUT,116.59,C0000530,M0000570,0,0,0.0,0.0,1.0,11.27,11.27,37.68,1.0,21.0,3.0942144373673037,0.0,0.0,0.0,46.0,0.0,1.0,10,14,0.024979279703451814,2.9123342206254472,low -demo-011546,346,CASH_OUT,426.92,C0001336,M0001983,0,0,0.0,0.0,1.0,36.74,36.74,60.855000000000004,1.0,15.0,7.015364390764933,1.0,1.0,74.15,24.0,0.0,1.0,10,14,0.01723599046382167,7.3583890488147485,low -demo-011547,346,PAYMENT,934.65,C0000090,M0001242,0,0,0.0,0.0,1.0,172.55,172.55,102.13,1.0,14.0,9.151571526485851,0.0,0.0,0.0,42.0,0.0,1.0,10,14,0.02232856042161364,20.86938899806119,low -demo-011548,346,PAYMENT,223.19,C0000838,M0001908,0,0,0.0,2.0,2.0,194.86,97.43,112.59,2.0,4.0,1.9823252509103828,1.0,1.0,8.89,22.0,0.0,1.0,10,14,0.006678830622477562,1.490648206630767,low -demo-011549,346,CASH_OUT,23.05,C0000112,M0000494,0,0,0.0,0.0,0.0,0.0,0.0,55.735,0.0,64.0,0.4135641876738136,0.0,0.0,0.0,85.0,0.0,1.0,10,14,0.006101299498395228,0.14063495343801,low -demo-011550,346,DEBIT,242.57,C0000831,M0000135,0,0,0.0,0.0,0.0,0.0,0.0,48.91,0.0,31.0,4.959517481087713,0.0,0.0,0.0,78.0,0.0,1.0,10,14,0.0027588890411437378,0.6692237147102365,low -demo-011551,346,CASH_OUT,726.46,C0000016,M0000961,0,0,1.0,1.0,1.0,84.94,84.94,71.12,1.0,1.0,10.214566929133857,0.0,0.0,0.0,74.0,0.0,1.0,10,14,0.2010718831648972,146.07068024397122,guarded -demo-011552,346,DEBIT,1288.67,C0000234,M0001634,0,0,0.0,0.0,1.0,4.51,4.51,28.894999999999996,1.0,17.0,44.5983734210071,0.0,0.0,0.0,101.0,0.0,1.0,10,14,0.006662980360169565,8.586382900739714,low -demo-011553,346,DEBIT,60.45,C0000219,M0001674,0,0,1.0,1.0,1.0,17.19,17.19,50.12,1.0,1.0,1.2061053471667997,1.0,1.0,361.52,22.0,0.0,1.0,10,14,0.0022446381535812013,0.13568837638398362,low -demo-011554,346,CASH_OUT,235.33,C0001063,M0000944,0,0,0.0,0.0,0.0,0.0,0.0,138.53,0.0,38.0,1.6987656103371112,1.0,1.0,572.72,8.0,0.0,1.0,10,14,0.01355862197875803,3.190750510261127,low -demo-011555,346,CASH_IN,144.77,C0000960,M0000539,0,0,0.0,0.0,0.0,0.0,0.0,118.695,0.0,64.0,1.2196806942162688,0.0,0.0,0.0,116.0,0.0,1.0,10,14,0.013896307984640751,2.0117685069364417,low -demo-011556,346,TRANSFER,76.16,C0000837,M0000094,0,0,0.0,0.0,0.0,0.0,0.0,48.89,0.0,58.0,1.557782777664144,1.0,1.0,130.42,16.0,0.0,1.0,10,14,0.02248321278446091,1.7123214856645428,low -demo-011557,346,TRANSFER,68.0,C0000294,M0000871,0,0,0.0,0.0,0.0,0.0,0.0,75.60499999999999,0.0,59.0,0.8994114145889823,2.0,2.0,259.47,11.0,0.0,1.0,10,14,0.024173424376103023,1.6437928575750056,low -demo-011558,346,CASH_IN,135.85,C0000398,M0001071,0,0,1.0,1.0,2.0,150.28,75.14,56.52,2.0,1.0,2.4035739561217264,0.0,0.0,0.0,61.0,0.0,1.0,10,14,0.0040700414927031725,0.552915136783726,low -demo-011559,346,PAYMENT,11.98,C0000566,M0000036,0,0,0.0,0.0,1.0,138.19,138.19,111.685,1.0,8.0,0.10726597125845011,0.0,0.0,0.0,29.0,0.0,1.0,10,14,0.0004365001482565891,0.005229271776113938,low -demo-011560,346,CASH_IN,15.9,C0001468,M0001869,0,0,0.0,1.0,1.0,952.39,952.39,318.47,1.0,3.0,0.04992620969008069,0.0,0.0,0.0,26.0,0.0,1.0,10,14,0.0003059415373680012,0.0048644704441512185,low -demo-011561,346,PAYMENT,228.59,C0001058,M0001416,0,0,0.0,0.0,1.0,191.22,191.22,145.855,1.0,14.0,1.5672414384148643,0.0,0.0,0.0,238.0,0.0,1.0,10,14,0.01666949708656259,3.8104803390173423,low -demo-011562,346,TRANSFER,368.74,C0000047,M0000653,0,0,0.0,0.0,0.0,0.0,0.0,47.59,0.0,44.0,7.748266442529943,0.0,0.0,0.0,307.0,0.0,1.0,10,14,0.0645955785525996,23.81897363548558,low -demo-011563,346,PAYMENT,393.27,C0000047,M0000710,0,0,0.0,0.0,0.0,0.0,0.0,47.59,0.0,44.0,8.26371086362681,1.0,1.0,326.18,22.0,0.0,1.0,10,14,0.0038947004769029564,1.5316688565516257,low -demo-011564,346,PAYMENT,147.14,C0000521,M0000714,0,0,0.0,0.0,0.0,0.0,0.0,64.46,0.0,43.0,2.2826559106422586,0.0,0.0,0.0,129.0,0.0,1.0,10,14,0.01090928948458995,1.6051928547625651,low -demo-011565,346,DEBIT,83.54,C0000313,M0001720,0,0,0.0,2.0,2.0,683.67,341.835,163.32999999999998,2.0,4.0,0.5114798261189005,0.0,0.0,0.0,33.0,0.0,1.0,10,14,0.000695442093130949,0.058097232460159486,low -demo-011566,346,CASH_OUT,991.41,C0000610,M0001447,0,0,0.0,0.0,1.0,340.55,340.55,286.58,1.0,9.0,3.4594528578407426,1.0,1.0,211.35,12.0,0.0,1.0,10,14,0.10471968614633875,103.8201440423417,guarded -demo-011567,346,PAYMENT,48.61,C0001493,M0000177,0,0,0.0,0.0,0.0,0.0,0.0,79.2,0.0,101.0,0.6137626262626262,0.0,0.0,0.0,186.0,0.0,1.0,10,14,0.00548350299770432,0.266553080718407,low -demo-011568,346,CASH_OUT,130.47,C0000293,M0000612,0,0,0.0,0.0,0.0,0.0,0.0,153.325,0.0,34.0,0.8509375509538563,0.0,0.0,0.0,79.0,0.0,1.0,10,14,0.017640199490353618,2.3015168275064366,low -demo-011569,346,PAYMENT,76.07,C0000895,M0001564,0,0,0.0,0.0,1.0,364.77,364.77,399.88,1.0,12.0,0.19023206962088626,0.0,0.0,0.0,250.0,0.0,1.0,10,14,0.0010530166815135588,0.08010297896273641,low -demo-011570,346,PAYMENT,144.81,C0000723,M0000712,0,0,0.0,0.0,0.0,0.0,0.0,83.875,0.0,36.0,1.7264977645305515,2.0,2.0,36.33,9.0,0.0,1.0,10,14,0.006948865991056695,1.00626528416492,low -demo-011571,347,CASH_OUT,182.09,C0000989,M0001627,0,0,0.0,1.0,3.0,300.91,100.30333333333334,103.69,3.0,6.0,1.756099913202816,0.0,0.0,0.0,109.0,0.0,1.0,11,14,0.03324535765269782,6.053647174979747,low -demo-011572,347,PAYMENT,12.88,C0000820,M0001014,0,0,0.0,1.0,1.0,41.13,41.13,84.2,1.0,6.0,0.15296912114014252,0.0,0.0,0.0,60.0,0.0,1.0,11,14,0.0013271903496509668,0.017094211703504454,low -demo-011573,347,PAYMENT,59.09,C0000774,M0001552,0,0,0.0,0.0,0.0,0.0,0.0,74.55,0.0,76.0,0.7926224010731053,0.0,0.0,0.0,34.0,0.0,1.0,11,14,0.00627520507156345,0.3708018676786843,low -demo-011574,347,PAYMENT,79.27,C0000414,M0001055,0,0,0.0,0.0,0.0,0.0,0.0,160.165,0.0,27.0,0.49492710642150284,1.0,1.0,196.9,8.0,0.0,1.0,11,14,0.003962390510539655,0.31409869577047844,low -demo-011575,347,PAYMENT,95.19,C0001448,M0001468,0,0,0.0,0.0,0.0,0.0,0.0,87.49,0.0,31.0,1.0880100582923764,0.0,0.0,0.0,35.0,0.0,1.0,11,14,0.0038524207481611824,0.36671193101746297,low -demo-011576,347,PAYMENT,47.02,C0000401,M0000862,0,0,0.0,0.0,1.0,82.26,82.26,92.385,1.0,11.0,0.5089570817773448,0.0,0.0,0.0,72.0,0.0,1.0,11,14,0.006568273704855385,0.30884022960230023,low -demo-011577,347,CASH_IN,155.14,C0000216,M0000475,0,0,0.0,0.0,0.0,0.0,0.0,148.74,0.0,25.0,1.0430281027295951,0.0,0.0,0.0,82.0,0.0,1.0,11,14,0.0019717846700175834,0.30590267370652785,low -demo-011578,347,PAYMENT,11.01,C0000143,M0001764,0,0,0.0,0.0,0.0,0.0,0.0,115.875,0.0,94.0,0.09501618122977346,0.0,0.0,0.0,59.0,0.0,1.0,11,14,0.0014844784723990103,0.016344107981113104,low -demo-011579,347,DEBIT,138.66,C0000101,M0000833,0,0,0.0,0.0,0.0,0.0,0.0,239.43,0.0,134.0,0.5791254228793384,0.0,0.0,0.0,54.0,0.0,1.0,11,14,0.0009480037805999437,0.1314502042179882,low -demo-011580,347,CASH_OUT,1860.99,C0000529,M0000018,1,0,0.0,0.0,1.0,2768.68,2768.68,52.21,1.0,12.0,35.64432101130052,0.0,0.0,0.0,180.0,0.0,1.0,11,14,0.11098534651050004,206.5426200025755,guarded -demo-011581,347,TRANSFER,92.69,C0001224,M0000771,0,0,0.0,0.0,1.0,49.42,49.42,160.49,1.0,12.0,0.5775437721976446,0.0,0.0,0.0,99.0,0.0,1.0,11,14,0.020325626102912515,1.883982283478961,low -demo-011582,347,DEBIT,88.62,C0000231,M0000821,0,0,0.0,0.0,0.0,0.0,0.0,104.07,0.0,30.0,0.851542231190545,1.0,1.0,1630.12,24.0,0.0,1.0,11,14,0.0006152246832218149,0.05452121142711724,low -demo-011583,347,CASH_OUT,145.33,C0000006,M0000482,0,0,0.0,0.0,0.0,0.0,0.0,101.11,0.0,50.0,1.4373454653347841,0.0,0.0,0.0,30.0,0.0,1.0,11,14,0.07702602727964038,11.194192544550138,low -demo-011584,347,CASH_OUT,148.42,C0000794,M0001755,0,0,0.0,0.0,0.0,0.0,0.0,218.51,0.0,111.0,0.67923664820832,1.0,1.0,119.94,23.0,0.0,1.0,11,14,0.014665018221699064,2.1765820044645747,low -demo-011585,347,TRANSFER,54.72,C0000842,M0000983,0,0,0.0,0.0,1.0,76.78,76.78,70.14,1.0,12.0,0.7801539777587682,0.0,0.0,0.0,81.0,0.0,1.0,11,14,0.03313001209872623,1.8128742620422993,low -demo-011586,347,PAYMENT,994.03,C0000845,M0000740,0,0,0.0,0.0,0.0,0.0,0.0,97.475,0.0,96.0,10.197794306232367,0.0,0.0,0.0,87.0,0.0,1.0,11,14,0.038909305909184254,38.677017352906425,low -demo-011587,347,CASH_IN,133.56,C0000002,M0001395,0,0,0.0,0.0,1.0,696.68,696.68,99.06,1.0,13.0,1.3482737734706238,0.0,0.0,0.0,25.0,0.0,1.0,11,14,0.0026107446878558763,0.34869106051003085,low -demo-011588,347,PAYMENT,35.65,C0000304,M0001279,0,0,0.0,0.0,0.0,0.0,0.0,188.95,0.0,77.0,0.1886742524477375,0.0,0.0,0.0,78.0,0.0,1.0,11,14,0.006674948897898577,0.23796192821008424,low -demo-011589,347,CASH_OUT,74.22,C0000522,M0000957,0,0,0.0,0.0,0.0,0.0,0.0,80.41,0.0,25.0,0.9230195249347096,0.0,0.0,0.0,50.0,0.0,1.0,11,14,0.014295868931923273,1.0610393921273453,low -demo-011590,348,PAYMENT,89.05,C0001109,M0001949,0,0,0.0,0.0,0.0,0.0,0.0,245.18,0.0,80.0,0.3632025450689289,0.0,0.0,0.0,241.0,0.0,1.0,12,14,0.004315537372941972,0.3842986030604826,low -demo-011591,348,PAYMENT,5.49,C0000976,M0001948,0,0,0.0,0.0,0.0,0.0,0.0,126.6,0.0,160.0,0.04336492890995261,0.0,0.0,0.0,48.0,0.0,1.0,12,14,0.002417374079186651,0.013271383694734715,low -demo-011592,348,CASH_OUT,737.9,C0001221,M0000952,0,0,0.0,0.0,0.0,0.0,0.0,489.72,0.0,73.0,1.5067793841378745,1.0,1.0,330.02,11.0,0.0,1.0,12,14,0.3388634236807321,250.0473203340122,guarded 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