Chargeback fraud prevention that works on both ends: a model trained on your own orders scores risk before capture, and an evidence agent assembles dispute packs from Stripe, Adyen, Shopify, carrier and login data.
Chargeback fraud prevention is the combination of fraud scoring before an order is captured and structured responses after a dispute is raised. It outputs an approve, review or decline decision with reasons for each transaction, alerts that let you refund before a chargeback lands, and a complete evidence pack per dispute, matched to the card network's reason code, ready to submit through your processor.
Each chargeback brings fees, lost goods, analyst hours and monitoring-programme risk. Tighten rules too far and you decline good customers, which never shows up in the fraud report but does show up in revenue.
Every $1 of fraud costs US merchants $4.61. LexisNexis Risk Solutions, 2025; Canadian merchants report $4.52.[1]
Of chargebacks are identified as fraudulent by merchants. That includes both third-party and first-party ('friendly') fraud, per Mastercard.[2]
Of North American merchants still rely on manual fraud processes. Only 6% of US ecommerce firms report full automation.[1]
Visa's 'Excessive' merchant threshold under VAMP from June 2025. Fraud plus disputes over settled transactions, with a further reduction scheduled for April 2026.[5]
Trained on your orders, refunds and chargebacks, not a generic consortium score, so it knows what a normal customer looks like for you.
When an issuer alert arrives, or a customer shows 'item not received' patterns, the system recommends refund, contact or fight.
An agent gathers the evidence each reason code requires and drafts the response; an analyst approves before submission.
Read-only access to 12+ months of transactions, refunds, chargebacks and outcomes from your processor and store. We agree one metric: fraud-plus-dispute rate, or dispute win rate.
The model scores past orders it never saw. You see how many chargebacks it would have caught, and how many good orders it would have wrongly declined.
Scores run in shadow beside your current rules. The evidence agent drafts real dispute responses for your team to approve and submit.
Catch rate, false-decline estimate, analyst hours saved and dispute pack quality, plus the thresholds we recommend going live with.
| Criterion | Manual rules and review | Off-the-shelf fraud tool | Stratgik build + run |
|---|---|---|---|
| Model trained on | Analyst intuition | Vendor consortium data | Your transactions, plus consortium signals if you have them |
| False-decline visibility | None | Limited | Measured in backtest and shadow mode |
| Dispute evidence | Copy-paste per case | Often a separate product | Agent-built packs by reason code |
| Pricing model | Analyst salaries | Per transaction or share of revenue; guarantees cost more | Flat monthly Run fee |
| Best fit | Very low volume, low risk | High-volume merchants wanting a chargeback guarantee | Mid-market with subscriptions, B2B or unusual order patterns |
| Control over thresholds | Full | Partial | Full, with documented reason codes |
First-party misuse is rising, chargeback volume is forecast to keep climbing, and Visa's monitoring programme now combines fraud and disputes into one ratio.
Two levers: chargebacks prevented before they happen, and more of the remaining disputes won. Both percentages are assumptions the pilot measures on your data.
Illustrative estimate using your inputs and stated assumptions, not a quote or guarantee. The pilot measures the real figure against your baseline.
Fraud model backtest, shadow scoring and live dispute packs for 21 days
Live scoring and dispute automation for one merchant account
Multiple brands, regions, processors or subscription products
Cloud hosting, model usage and any third-party data lookups (device, email or phone intelligence) billed at cost with no markup. Card network and processor fees remain yours. Taxes excluded. GBP and AED prices are indicative conversions from USD.
Machine learning chargeback fraud prevention scores each transaction using patterns learned from your past orders, refunds and chargebacks. Signals include device, velocity, address and email history, basket content and behaviour. The model returns approve, review or decline with reasons. After purchase, it flags likely disputes early and assembles evidence for chargebacks you choose to fight.
Friendly fraud, or first-party misuse, is when a genuine cardholder disputes a purchase they made or received. AI cannot stop it at checkout as reliably as stolen-card fraud, because the buyer looks legitimate. It helps by spotting serial disputers, sending clear renewal reminders, and building strong evidence. Mastercard reports merchants identify 45% of chargebacks as fraudulent, including first-party cases.
A Stratgik chargeback fraud prevention pilot costs $18,000 for 21 days, covering a backtested model, shadow scoring and live dispute packs. Running it costs $4,500 a month per merchant account, and Scale starts at $12,000 a month. Third-party data lookups and cloud usage are billed at cost. We do not charge a percentage of revenue or of recovered disputes.
Not always. Stripe Radar and guaranteed-chargeback tools are strong for standard ecommerce at high volume. A model on your own data tends to help when orders are unusual, for example subscriptions, B2B, high-value goods, or multiple processors. You can keep your existing tool and add our score and dispute agent on top; the pilot shows whether that is worth it.
Yes. The evidence agent pulls what the card network reason code requires, such as login and IP history, delivery confirmation, usage logs, communications and prior undisputed orders, and drafts a response in your processor's format. An analyst reviews and submits it. Automation removes the copy-paste work, while a human still decides which disputes are worth fighting.
It can, which is why false declines are measured from the start. In backtesting we count how many legitimate past orders each threshold would have blocked, and in shadow mode we compare the model's decisions with your current rules before anything goes live. Thresholds are set per order value and customer type, not as one blanket cut-off.
A 30-minute call to confirm your processors and data access, then a fixed-price 21-day pilot measured on one number your finance team already tracks.