Interactive Calculator · C-Store POS Fraud Detection Program

Fraud Impact & Detection ROI

Model fraud exposure, detection rates by category, and the financial impact of the ML-assisted detection program. All calculations update as you adjust parameters — start with your store parameters, then tune each fraud category.

Architecture: IForest + LOF + Rules (v1) CV: Phase 2 (reserved in formula) Scoring: Per transaction Pilot: 200 stores
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Portfolio Parameters — set your store baseline; all downstream figures update automatically
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stores
Pilot: 200 stores  ·  Full rollout target: 13,000
1.5 %
Industry range: 0.5–2% for C-store & quick-serve. Calibrate after distribution analysis.
80 %
% of fraud currently caught without ML
85 %
Expected % of fraud caught with ML active. Must be ≥ baseline. Drives "Additional Fraud Found by ML" directly.
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Portfolio Overview
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Fraud Category Breakdown — adjust share, baseline detection %, and ML detection % per category
Category Share Annual $ Detection   Baseline → ML
Share sliders are proportional — categories auto-normalize to their relative weight. ML detection % represents total caught with the ML system active (must be ≥ baseline).
Annual Fraud Exposure by Category ($)
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Detection Status per Category — Annual Dollars — baseline detected · ML additional · still undetected
Baseline Detected ML Additional Detection Still Undetected
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Financial Impact & Program ROI
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Compute, licensing, AP analyst time — per store, per month
Total Portfolio Cost
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per month across all stores
Period:
Baseline Detected
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Fraud flagged without ML system
ML Additional Detected
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Additional fraud flagged above baseline
Net Program Benefit
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ML detected minus system cost
System Cost
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Program ROI
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net benefit ÷ system cost
Payback Period
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months to break even
Per-Store Net
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About the estimates
Detection rates are starting-point estimates for program planning — calibrate against real data distributions before setting targets. ML detection % per category reflects total caught with the system active, not just the incremental lift. Sweethearting shows limited ML lift in v1 — CV (Phase 2) is required for meaningful detection of that scheme.
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3-Year Fraud Cycle Projection

Fraud is not static. As ML detection flags incidents, fraud occurrence drops (deterrence effect). Over time, patterns adapt and re-emerge in new forms — then ML catches up again. The cycle repeats. The shaded area below represents the cumulative deterrence benefit over 36 months vs. a no-ML static baseline. Vertical markers ↻ show each adaptation cycle reset.

Dynamic Fraud Cycle Model
30 %
% drop in fraud occurrence when ML is active — fraudsters get caught and modify behavior. Does not reduce over time; only occurrence rate drops.
12 mo
Months before fraud patterns fully re-emerge in new forms — completing one detect → deter → adapt cycle.
Monthly fraud exposure · shaded area = deterrence benefit from ML · ↻ = fraud adaptation cycle reset
Estimates only — calibrate with real data before program reporting