C-Store POS Fraud — Impact & Detection Calculator

1

Portfolio Parameters — set your store baseline; all downstream figures update automatically

Daily Revenue per Store

$

—

Number of Stores

stores

Pilot: 200 stores · Full rollout target: 13,000

Fraud Rate (% of Revenue)

1.5 %

Industry range: 0.5–2% for C-store & quick-serve. Calibrate after distribution analysis.

Baseline Detection Rate

80 %

% of fraud currently caught without ML

ML Detection Rate

85 %

Expected % of fraud caught with ML active. Must be ≥ baseline. Drives "Additional Fraud Found by ML" directly.

2

Portfolio Overview

Show:

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3

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 ($)

4

Detection Status per Category — Annual Dollars — baseline detected · ML additional · still undetected

Baseline Detected ML Additional Detection Still Undetected

5

Financial Impact & Program ROI

Monthly System Cost per Store

$

Compute, licensing, AP analyst time — per store, per month

Total Portfolio Cost

—

per month across all stores

Period:

Baseline Detected

—

Fraud flagged without ML system

ML Additional Detected

—

Additional fraud flagged above baseline

Net Program Benefit

—

ML detected minus system cost

System Cost

—

Program ROI

—

net benefit ÷ system cost

Payback Period

—

months to break even

Per-Store Net

—

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.