Benchmarks (Operator Field Manual)
- Auth rate: CNP 85-90% typical, CP 98-99%; Apple/Google Pay 92-97%
- Fraud rate: CNP e-commerce 0.05-0.2%, digital goods 0.2-0.5%, travel 0.5-1.5%
- Chargeback ratio: Under 0.5% healthy, 0.75-0.9% danger, over 0.9% crisis (Visa threshold)
- Refund rate: 2-5% typical; refund-to-CB ratio should be 3-5:1
- Segment everything (CP vs. CNP, BIN, geo); trend over 4-8 weeks beats snapshots
Use anchors, not absolutes. Compare to yourself over time and by segment (CP vs CNP, method, BIN, geo). Your baseline matters more than industry averages.
Last verified: Dec 2025. Benchmarks shift with market conditions; recalibrate annually.
What Matters (5 bullets)
- Segment everything. CP vs CNP, card brand, BIN/issuer, country, device. Aggregate numbers hide problems.
- Trend beats snapshot. Direction over 4-8 weeks matters more than any single week.
- Read metrics together. Auth, fraud, chargebacks, refunds, and alerts are interconnected.
- These ranges assume US domestic, mainstream MCCs. High-risk verticals run hotter.
- Date your thresholds. "Last verified" on any number you publish or operationalize.
Authorization Rate Benchmarks
Card-Not-Present (CNP)
| Performance | Auth Rate | Notes |
|---|
| Poor | Under 80% | Major issues: fix fraud rules, 3DS, or issuer relations |
| Below average | 80-85% | Room for improvement |
| Typical | 85-90% | Standard for US e-commerce |
| Good | 90-93% | Well-optimized stack |
| Excellent | 93-95% | Best-in-class; network tokens, retry logic, issuer work |
Card-Present (CP)
| Performance | Auth Rate | Notes |
|---|
| Poor | Under 95% | Investigate terminal issues, connectivity |
| Typical | 98-99% | Expected for retail |
| Excellent | 99%+ | Fully optimized |
By Payment Method
| Method | Typical Auth Rate | Notes |
|---|
| Cards (CNP) | 85-90% | Varies heavily by BIN/issuer |
| Cards (CP) | 98-99% | Chip/PIN highest |
| Apple Pay/Google Pay | 92-97% | Tokenized, higher than raw cards |
| PayPal | 95-98% | Account-to-account |
| ACH | 99%+ | But watch returns |
By Issuer Geography
| Region | Typical Auth Rate | Notes |
|---|
| US domestic | 88-93% | Baseline |
| UK/EU | 85-90% | SCA friction may impact |
| LATAM | 70-85% | Higher decline rates common |
| APAC | 75-88% | Varies widely by country |
| Cross-border | 5-15% lower | vs domestic acquiring |
Fraud Rate Benchmarks
By Transaction Type
| Type | Typical Fraud Rate | Alert Level |
|---|
| CNP e-commerce | 0.05-0.2% | Over 0.3% = tighten rules |
| CP retail | 0.01-0.05% | Over 0.1% = investigate |
| Digital goods | 0.2-0.5% | Higher baseline expected |
| Subscriptions | 0.1-0.3% | Monitor initial vs recurring |
By Industry (CNP)
| Industry | Typical Fraud Rate | Notes |
|---|
| General retail | 0.1-0.2% | Baseline |
| Luxury/high-ticket | 0.3-0.8% | Attractive target |
| Digital goods | 0.3-0.6% | No physical verification |
| Travel | 0.5-1.5% | High-risk category |
| Gaming/gambling | 0.5-2%+ | Varies with regulation |
| Crypto/forex | 1-3%+ | Extreme high-risk |
Fraud Detection Metrics
| Metric | Good | Concerning |
|---|
| False positive rate | Under 1% | Over 2% hurts conversion |
| Catch rate (true positive) | 60-80% | Under 50% = rules too weak |
| Manual review rate | Under 5% | Over 10% = automation gaps |
| Review-to-block rate | 20-40% | Too high = rules too loose |
Chargeback Benchmarks
Chargeback Ratio Thresholds
| Ratio | Status | Action |
|---|
| Under 0.5% | Healthy | Monitor normally |
| 0.5-0.75% | Caution | Increase monitoring |
| 0.75-0.9% | Danger | Active remediation needed |
| 0.9-1.0% | Crisis | Risk of program enrollment |
| Over 1.0% | Threshold breach | VDMP/ECP enrollment likely |
Network Program Thresholds
| Network | Standard Threshold | Enhanced Threshold |
|---|
| Visa (VDMP/VAMP) | 0.9% AND 100 disputes | ~1.5% merchant excessive |
| Mastercard (ECM) | 1.5% AND 100 disputes | 3.0% AND 300 disputes (HECM) |
| American Express | Varies by relationship | - |
| Discover | 1.0% | - |
Chargeback Composition
| Reason Type | Typical Share | Red Flag |
|---|
| Fraud disputes | 50-70% | - |
| Friendly fraud | 20-40% | Over 50% = evidence problem |
| "Unrecognized" | 5-10% | Over 20% = descriptor issue |
| Service/quality | 10-20% | Over 30% = product/CX problem |
| Recurring billing | 5-15% | Over 25% = cancellation issue |
Refund Benchmarks
Refund Rate
| Rate | Interpretation |
|---|
| Under 2% | May be too restrictive; could increase disputes |
| 2-5% | Typical for most merchants |
| 5-10% | Higher but may be appropriate for some models |
| Over 10% | Investigate product/CX issues |
Refund-to-Chargeback Ratio
| Ratio | Interpretation |
|---|
| Under 2:1 | Refunding too little; disputes filling the gap |
| 3-5:1 | Healthy balance |
| 5-10:1 | Acceptable; strong refund policy |
| Over 10:1 | May be over-refunding; investigate |
Ethoca/CDRN/Verifi Metrics
| Metric | Good | Target |
|---|
| Alert match rate | 30-50% | As high as possible |
| Response time | Under 2 hours | Ideally automated |
| Refund vs ignore | 80%+ refund | Depends on ticket size |
| Prevented disputes | 20-40% reduction | Track before/after |
Real-Time Alert Response
| Response Time | Performance |
|---|
| Under 1 hour | Excellent |
| 1-4 hours | Good |
| 4-24 hours | Acceptable |
| Over 24 hours | Missing value |
By Business Model
Subscriptions
| Metric | Benchmark |
|---|
| Initial auth rate | 80-85% |
| Recurring auth rate | 90-95% |
| Involuntary churn | Under 3% monthly |
| Dunning recovery | 10-30% of failed |
| Subscription fraud | 0.1-0.3% |
Digital Goods
| Metric | Benchmark |
|---|
| Auth rate | 80-88% (more 3DS step-up) |
| Fraud rate | 0.3-0.6% (higher baseline) |
| Dispute win rate | 30-50% (harder to prove) |
Physical Goods
| Metric | Benchmark |
|---|
| Auth rate | 85-92% |
| Fraud rate | 0.1-0.2% |
| Dispute win rate | 50-70% (delivery proof helps) |
| "Not received" share | 20-40% of disputes |
B2B
| Metric | Benchmark |
|---|
| Auth rate | 90-95% |
| Fraud rate | Under 0.1% |
| ACH return rate | Under 1% |
| Invoice payment | Net-30 to Net-60 typical |
Keyed Transaction Benchmarks (CP)
| Keyed % of CP Volume | Status |
|---|
| Under 2% | Normal |
| 2-5% | Investigate |
| 5-10% | Problem |
| Over 10% | Major red flag |
High keyed rates may indicate:
- Terminal issues
- Card-not-present masquerading as CP
- Employee fraud
- Training gaps
How to Use These Benchmarks
Step 1: Establish Your Baseline
Before comparing to industry, know your own numbers:
- Calculate each metric for last 90 days
- Segment by CP/CNP, geography, method
- Document as your baseline
Step 2: Compare to Benchmarks
- Are you above/below industry norms?
- Which segments are problems?
- Where are quick wins?
Step 3: Track Trends
- Weekly: Auth rate, fraud rate, chargeback ratio
- Monthly: All metrics, segmented
- Quarterly: Deep dive, re-baseline
Step 4: Set Alerts
| Metric | Alert When |
|---|
| Auth rate | Drops over 0.5pp from baseline |
| Fraud rate | Rises over 0.05pp |
| Chargeback ratio | Approaches 0.75% |
| Refund rate | Changes over 1pp |
Where This Breaks
- Mixing CP and CNP. Never combine in single number.
- Seasonality. Holiday, promo weeks skew baselines. Compare like periods.
- High-risk MCCs. Travel, gaming, crypto run above these ranges. Set your own bands.
- International traffic. Expect lower auth, higher fraud. Get local acquiring before judging.
- Low volume. Small sample sizes create noise. Need 1000+ transactions for reliable rates.
Next Steps
Establishing your baseline?
- Follow the 4-step process - Baseline, compare, track, alert
- Segment by CP vs CNP - Never combine in one number
- Set internal alert thresholds - Catch problems early
Comparing to industry?
- Check auth rate benchmarks - CNP 85-90%, CP 98-99%
- Review fraud rate by industry - Know your vertical
- Understand chargeback thresholds - Under 0.5% healthy
Improving metrics?
- Optimize auth rate - Network tokens, retry logic
- Reduce fraud rate - Risk scoring, 3DS
- Lower chargeback ratio - Alerts, descriptors