Friendly fraud: enemy number one
Friendly fraud (first-party misuse in Visa’s terminology) is a chargeback filed by the legitimate cardholder. The purchase really happened, but the cardholder disputes it as fraudulent or not received. Industry estimates (Visa, Datos Insights) converge on 60% to 80% of e-commerce chargebacks. That share changes what anti-fraud work is about. Most of the “fraud” an online merchant sees comes from its own customers’ behavior, not from third parties using stolen cards.
SARL WEBCO 75). The cardholder doesn’t recognize their own transaction and checks “fraud.”Scoring: rules vs. machine learning
Fraud scoring assigns every transaction a risk score before or during authorization, and that score determines the outcome: approve, decline, 3DS challenge, or manual review. Two approaches coexist and complement each other. Expert rules are readable and take effect immediately. Machine learning models capture correlations humans can’t see (entity graphs, typing speed, device reuse), at the cost of less explainability.
| Criterion | Rules engine | Machine learning |
|---|---|---|
| Implementation | Immediate, by the fraud team | Training data + iterations |
| Explainability | Full (audit, compliance) | Partial (scores, approximate reasons) |
| New patterns | Blind until someone writes the rule | Early detection if the data network is large |
| False positives | High if rules are too broad | Generally lower at equal detection |
| Response to attacks | Excellent (a rule in 5 minutes) | Depends on how often the model is retrained |
| Best used for | Guardrails, regulatory cases, immediate response | Baseline score on 100% of traffic |
# Rules act as fast guardrails around the ML score.
rules:
- id: card-velocity
condition: distinct_cards_per_device_24h >= 3
action: decline
comment: testing stolen cards (card testing / enumeration)
- id: geo-mismatch
condition: ip_country != bin_country AND amount > 150
action: challenge_3ds
comment: targeted friction instead of a hard decline
- id: loyal-customer
condition: account_age > 180d AND delivered_orders >= 5 AND ml_score < 20
action: request_tra_exemption
comment: acquirer TRA exemption if the fraud rate is below the EBA thresholds
- id: gray-zone
condition: ml_score between 60 and 85
action: manual_review
queue: priority_if_amount > 300
comment: human review absorbs the model's zone of uncertaintyAlerts and deflection: stopping the dispute before the chargeback
Deflection means resolving the cardholder’s dispute before it becomes a formal chargeback. Between the two, there is a 24- to 72-hour window to settle the matter. The Verifi (a Visa subsidiary) and Ethoca (a Mastercard subsidiary) alert networks cover this ground, connected directly to participating issuers. A deflected dispute does not count as a chargeback in monitoring program ratios, with one exception described below. It costs a fraction of what a chargeback costs.
| Framework | Network | Mechanism | Indicative cost | Effect on ratios |
|---|---|---|---|---|
| Verifi CDRN | Visa (and other networks, depending on the issuer) | Alert sent to the merchant, which refunds within 72 hours to avoid the chargeback | $20–40 per alert | Dispute not counted as a chargeback |
| Verifi Order Insight | Visa | Order data sent in real time to the issuer or banking app when the cardholder has doubts | Per request / subscription | Deflects “I don’t recognize this” disputes before they are filed |
| Verifi RDR | Visa | Preset decision rules: automatic refund at the pre-dispute stage, based on amount/MCC/reason code | $12–40 per case | Not a chargeback; drops out of the VAMP ratio, except fraud already reported (TC40) |
| Ethoca Alerts | Mastercard (and Visa, depending on the issuer) | CDRN equivalent: fraud/dispute alert, refund within 72 hours | $20–40 per alert | Dispute not counted as a chargeback |
| Ethoca Consumer Clarity | Mastercard | Order Insight equivalent: order details in the banking app | Per request / subscription | Upstream deflection |
3DS: across the board or targeted?
In the EEA, strong customer authentication (SCA) is mandatory. The merchant’s choice is therefore not “with or without 3DS” but who requests exemptions, and which ones. An authenticated 3DS transaction shifts fraud liability to the issuer (liability shift). A TRA (transaction risk analysis) exemption keeps checkout smooth but leaves liability with whoever requested the exemption. Fine-tuning this setting has a direct impact on conversion.
| Strategy | Fraud | Conversion | When to choose it |
|---|---|---|---|
| Across-the-board 3DS (frequent challenges) | Minimal, maximum liability shift | Loss of 1 to 5 points, depending on the checkout flow and issuers | High-fraud industries (digital goods, travel), merchants in a monitoring program, large baskets |
| Frictionless-first 3DS (rich data, rare challenges) | Low, liability shift retained | Nearly neutral | Recommended default: send as much data as possible in the AReq so the ACS can decide without a challenge |
| Managed TRA/low-value exemptions | Merchant/acquirer liable for exempted transactions | Best (no redirect at all) | Low-risk repeat customers, if the acquirer’s fraud rate stays below the EBA thresholds |
- EBA thresholds for TRA: exemption possible up to €100 if the requesting PSP’s fraud rate is ≤ 13 basis points, €250 if ≤ 6 bps, and €500 if ≤ 1 bp. What counts is the PSP’s rate, not the merchant’s.
- Low value: exemption under €30, with cumulative counters (5 transactions or €100 in a row) managed by the issuer, which explains “surprise” challenges on small baskets.
- Measured impact: in France, the rollout of SCA came with a lasting drop in the fraud rate on remote payments, from about 0.27% in the early 2010s to ~0.16% in 2023 (OSMP).
Proactive refunds
A proactive refund returns the funds to the customer before their dispute turns into a chargeback. The point is the cost gap between the two outcomes. A refund costs only the amount, while a lost chargeback costs the amount + €15–50 in fees + the hit to the ratio. A proactive refund policy sets out in advance when the merchant refunds without investigating the case.
- Alert received (CDRN/Ethoca) on a losing case: always refund within 72 hours, since that is what the alert is for.
- Customer complaint before a dispute (“I’m going to my bank”) on a small amount: refund if fighting it costs more than what is at stake, even if you doubt the customer’s good faith.
- Confirmed merchant error (duplicate charge, credit note never issued, parcel lost with no proof of delivery): refund immediately. Any representment would be lost, with fees.
- Anti-abuse limit: cap per customer (cumulative amount, number of goodwill gestures) and track repeat recipients. Proactive refunds must not become a reward for repeat friendly fraud.
The fraud KPIs to track
Managing a fraud prevention setup is an economic trade-off: every euro of fraud avoided has a measurable cost. That cost takes two forms. One is false positives, legitimate sales declined because they could not be told apart from fraud. The other is the cost of the tools that make the distinction. A minimum dashboard therefore tracks four families of metrics: fraud losses, disputes, false positives, and total cost.
| KPI | Option | Indicative target | Measurement trap |
|---|---|---|---|
| Fraud rate (by value) | Confirmed fraud amount / volume processed | Under 0.10% (France card-not-present average: ~0.16%) | Count TC40/SAFE reports, not just chargebacks |
| Chargeback rate (by count) | Number of chargebacks / number of transactions in the month | Under 0.65% comfortable; warning at 0.9%, below the 1.5% VAMP/ECM thresholds | Replicate each scheme’s exact calculation (Mastercard uses a one-month lag) |
| Fraud decline rate | Transactions declined by fraud screening / attempts | 1% to 5%, depending on the industry | A low rate is good only if fraud doesn’t rise |
| False positive rate | Legitimate customers declined / fraud declines | Under 30% of declines (measured by sampling/review) | Invisible without test campaigns: a declined customer doesn’t come back to complain |
| Representment win rate | Representments won / filed | ≥ 40% with structured evidence | Cross-check with the recovery rate by value |
| Total cost of fraud | (Net losses + dispute fees + tools + team) / revenue | Under 0.3% of revenue | Include estimated revenue lost to over-declining |