Calculator Inputs
Use the fields below to evaluate classification quality, operational workload, and financial impact for a fraud detection model.
Example Data Table
This sample shows how threshold changes can alter precision, recall, alerts, and cost posture.
| Threshold | TP | FP | TN | FN | Precision | Recall | F1 | Total Expected Cost |
|---|---|---|---|---|---|---|---|---|
| 0.45 | 158 | 116 | 4116 | 10 | 57.66% | 94.05% | 71.49% | $3,105.00 |
| 0.62 | 145 | 52 | 4180 | 23 | 73.60% | 86.31% | 79.45% | $5,313.50 |
| 0.78 | 119 | 21 | 4211 | 49 | 85.00% | 70.83% | 77.27% | $11,262.50 |
Formula Used
Accuracy = (TP + TN) / (TP + FP + TN + FN)
Precision = TP / (TP + FP)
Recall = TP / (TP + FN)
Specificity = TN / (TN + FP)
False Positive Rate = FP / (FP + TN)
False Negative Rate = FN / (FN + TP)
Negative Predictive Value = TN / (TN + FN)
F1 Score = 2 × Precision × Recall / (Precision + Recall)
Balanced Accuracy = (Recall + Specificity) / 2
MCC = (TP × TN − FP × FN) / √((TP + FP)(TP + FN)(TN + FP)(TN + FN))
Prevalence = (TP + FN) / Total Records
Alert Rate = (TP + FP) / Total Records
Lift = Precision / Prevalence
False Positive Cost = FP × (Review Cost + Customer Friction Cost)
Missed Fraud Cost = FN × ((Average Fraud Amount × Missed Loss Multiplier) + Chargeback Fee)
Total Expected Cost = False Positive Cost + Missed Fraud Cost + (TP × Review Cost)
Net Value = Caught Fraud Value − Total Expected Cost
These formulas blend model quality with operational economics, which is critical when fraud rates are low and manual review capacity is limited.
How to Use This Calculator
- Enter the confusion matrix counts from your model evaluation set: TP, FP, TN, and FN.
- Choose a threshold value that reflects your decision rule for flagging suspicious events.
- Add cost assumptions for analyst review, customer friction, average fraud amount, and chargeback exposure.
- Set a missed fraud multiplier if downstream losses exceed the direct stolen amount.
- Provide a planned scoring population to estimate alert volumes and fraud captured at scale.
- Press Calculate Metrics to place the results above the form, inspect the chart, and review the cost breakdown.
- Use the CSV button to export the metrics table and the PDF button to save the visible report.
- Compare multiple threshold scenarios manually to balance fraud capture, review effort, and customer experience.
Frequently Asked Questions
1. Why is accuracy often misleading in fraud detection?
Fraud datasets are usually imbalanced. A model can score high accuracy by predicting most transactions as legitimate, while still missing many actual fraud cases.
2. What metric matters most for fraud screening?
There is no single best metric. Precision, recall, lift, and expected cost should be read together because business goals differ across review capacity, loss tolerance, and customer experience.
3. What does lift mean here?
Lift compares model precision against the natural fraud rate. A lift of 5 means flagged cases are five times richer in fraud than random selection.
4. Why include customer friction cost?
False positives can block good customers, trigger support contacts, and reduce trust. Friction cost helps reflect those indirect losses, not just analyst review expense.
5. What does MCC add beyond F1 score?
Matthews Correlation Coefficient uses all four confusion matrix cells and stays informative under class imbalance. It is useful when you want a balanced correlation-style quality signal.
6. Should I always maximize recall?
Not always. Higher recall often raises false positives, increasing review workload and customer friction. The best threshold depends on the cost of misses versus the cost of extra alerts.
7. Can this calculator compare multiple thresholds?
Yes. Run the calculator several times with different confusion matrices or threshold assumptions, then compare precision, recall, alert rate, and expected cost across scenarios.
8. Is this suitable for transaction and account fraud models?
Yes. The same metric framework applies to card fraud, account takeover, claims abuse, merchant risk, and many other binary fraud detection workflows.