E-commerce~8-10 hours to build$10K/Month goal

Chargeback Protection for Ecommerce Sellers

Predictive fraud screening and automated chargeback dispute responses for Shopify, Amazon, and eBay sellers losing revenue to buyer scams.

By John IseghohiPublished July 13, 2026

Opportunity 9/10
Pain 9/10
Timing 9/10
Confidence 8/10

The Problem

A jewelry seller on Shopify ships an $800 order of earrings. The buyer receives them, wears them to a wedding, then files a chargeback claiming the item never arrived. The seller spends eight hours pulling shipping records, delivery photos, and message threads into a response — and still loses, because the card network and payment processor default to believing the cardholder unless the merchant can produce airtight, correctly formatted evidence inside a tight filing window. Multiply that by every order over a few hundred dollars and a growing share of an online store's weekend gets eaten by unpaid dispute paperwork instead of product or marketing.

The pattern repeats across every seller-side forum. Reddit's r/SmallBusiness (1.6M+ members) has individual threads on fraud and chargebacks pulling 140+ comments; r/Scams sits at 1.1M+ followers trading blocklists and dispute templates; the smaller r/FraudPrevention exists specifically because generalist advice doesn't cut it. Facebook groups like "eBay Seller Group Helping," "Fraud Prevention & Scam Recovery Group," and "Ecommerce Fraud Prevention Best Practices" run active daily threads from sellers comparing notes on which evidence formats actually get disputes overturned. The volume and specificity of these conversations — not just "I got scammed" but "here's the exact wording that worked" — is a signal that the current toolkit (generic advice plus manual paperwork) doesn't scale with order volume.

The structural cause is that dispute systems were built to protect buyers from card fraud, not to adjudicate seller-side scams fairly. A buyer claiming "item not as described" or "never arrived" triggers a process weighted toward refunding the cardholder by default; the merchant has to affirmatively disprove the claim inside days, using evidence formats the processor dictates, and most small sellers have neither the templates nor the time to do this well every single time. Every hour spent fighting a dispute is an hour not spent sourcing, shipping, or marketing — and every lost dispute is direct margin erasure on an order that already shipped.

The Solution

A fraud and dispute layer that plugs into a store's order and payment data, flags high-risk orders before they ship, and auto-assembles evidence-backed responses the moment a chargeback is filed. Instead of a seller manually digging through shipping carriers, message logs, and photo folders under deadline pressure, the system has already indexed that evidence and can generate a formatted, processor-compliant response in minutes. The predictive layer catches the pattern that precedes most scam chargebacks — multiple high-value orders to different shipping addresses on the same payment method, mismatched billing/shipping geography, brand-new accounts ordering at the top of the catalog — and lets the seller hold or verify before the order ships, which is the cheapest place to stop fraud.

How it works:

Connect — Seller links Shopify, WooCommerce, Amazon Seller Central, or eBay via OAuth; the system ingests order history, shipping confirmations, and payment metadata on a rolling basis.
Screen — Every new order is scored against fraud signals (velocity across addresses, mismatched geolocation, device fingerprint reuse, historical dispute rate on the payment method) before fulfillment; high-risk orders get flagged for manual review or held automatically.
Respond — When a chargeback or "item not received"/"not as described" claim lands, the system auto-compiles tracking data, delivery confirmation, prior communication, and product photos into the specific evidence format each processor requires, then drafts the rebuttal narrative for seller review before submission.
Learn — Outcomes (win/loss, evidence type, dispute reason code) feed back into the scoring model and the response template library, so the next dispute of the same shape gets a stronger, pre-validated response.

Market Research

The category sitting closest to this product — brand and fraud protection software — was valued at $511.67 million in 2024 and is projected to reach $2.93 billion by 2032, a 24.38% CAGR, according to Global Growth Insights' brand protection software market report. The broader authentication and brand protection market is separately sized at $3.32 billion for 2025, growing at an 8.4% CAGR through 2037 per Research Nester. Security-as-a-Service, the infrastructure layer underneath fraud and risk tooling, is forecast at $17.0 billion globally for 2025 (The Business Research Company) — evidence that spend on software-driven risk prevention is accelerating well beyond the narrow chargeback-recovery niche.

Demand-side signals point the same direction. Ecommerce fraud losses have climbed every year as order volume shifts online, and dispute systems have not kept pace with the sophistication of "friendly fraud" — buyers who receive goods and dispute anyway, knowing processors default to their side. Keyword research around this space shows high commercial intent across "fraud prevention," "fraud detection," "ecommerce fraud prevention," and "ebay seller protection," all flagged as high-commercial-intent, moderate-volume, low-competition terms — a gap between search demand and dedicated seller-side supply. The market is still fragmented: most fraud tooling (Signifyd, Sift, FraudLabs Pro) screens transactions before approval, and most dispute-recovery tooling (Chargebacks911) is built for high-volume card-not-present merchants generally, not for the specific evidence formats and appeal mechanics of marketplace platforms like eBay and Amazon. That gap — platform-aware, seller-first defense combining pre-shipment screening with automated post-dispute response — is where a small team can wedge in before larger players extend downmarket.

Competitive Landscape

Chargebacks911 — The category's best-known dispute-recovery player. Automated evidence collection, case management, and a "your cost is always less than the recovery" positioning aimed at high-volume card merchants. Pricing runs on a customized model reported between $99 and $200 per month depending on integration and volume (Capterra, SourceForge), often layered with a performance fee on recovered amounts. Gap: built for general card-not-present disputes, not marketplace-specific policy and evidence formats — a seller fighting an eBay "item not as described" claim gets generic tooling, not platform expertise.
Signifyd — Enterprise-grade order-approval fraud screening; decides whether to approve or decline a transaction before it ships, and backs approved orders with a financial guarantee. Pricing is a custom percentage of approved order value, scaled by vertical and volume, with no published flat rate (signifyd.com/pricing). Gap: Signifyd's job ends at "should we ship this" — it does not fight chargebacks or marketplace disputes after the fact, and its enterprise pricing model prices out most sub-$1M sellers.
FraudLabs Pro — The most accessible of the transaction-screening tools, with published pricing starting at $29.95 per user per month and a free tier for very low volume. Gap: real-time fraud scoring only; no dispute response automation, no marketplace-specific evidence formatting, and support drops off outside its core scoring API.
eBay/marketplace-native seller protection — Bundled into every seller's account at no extra cost, with an automatic appeal path for a narrow set of qualifying cases. Gap: sellers across Reddit and Facebook communities consistently describe the appeal process as opaque and biased toward buyers, with limited recourse once a claim is denied — the free tool is also the one sellers trust least.

Your Opportunity

None of the transaction-screening players (Signifyd, FraudLabs Pro) touch post-dispute response, and the dispute-recovery specialist (Chargebacks911) isn't built around marketplace-specific evidence and appeal mechanics. That leaves a wedge for a product that does both — screen before shipping, respond automatically after a claim — priced for the $10K–$200K/month seller who is priced out of Signifyd's enterprise model and underserved by Chargebacks911's generic card-dispute focus. Winning on platform-specific templates (eBay, Amazon, Shopify/PayPal each have different evidence formats and appeal windows) is a moat larger players won't build until the category is already validated.

Business Model

Subscription SaaS with an optional performance fee, so pricing scales with the size of the problem it solves rather than penalizing a seller for growing.

Starter ($79/mo) — For stores under $50K/mo revenue: pre-shipment fraud screening, up to 25 automated dispute responses/month, standard evidence templates for one connected platform.
Growth ($199/mo) — For $50K+/mo revenue stores: unlimited automated dispute responses, predictive order-holding, multi-platform evidence formatting (Shopify + Amazon + eBay), priority review queue.
Scale (from $499/mo + 3% of recovered funds) — Multi-store or agency accounts: dedicated fraud analyst review, API access for custom fulfillment stacks, quarterly fraud-pattern reporting.

Path to $1M ARR: roughly 420 stores on the blended $199 Growth tier, or a mix skewed toward Starter with a smaller Scale cohort carrying the performance fee upside — either path is reachable inside a single vertical (Shopify + eBay sellers) before expanding to Amazon FBA and multi-channel resellers.

Unit Economics

$45 — Target CAC (Facebook group + agency partnership channels are low-cost relative to paid search on "fraud prevention")
$165/mo — Blended ARPU across Starter/Growth
~75% — Gross margin (evidence generation costs run on LLM API calls plus carrier/tracking API lookups, both cheap relative to subscription price)
~$2,400 — LTV at a 15-month average retention (dispute tools are sticky once a store's evidence history lives inside them)

Recommended Tech Stack

The hard engineering problem isn't the AI layer — it's reliable ingestion from three very different platform APIs and evidence formatting that matches what each processor actually requires.

Next.js (App Router) + Vercel — Merchant dashboard, OAuth connection flows for Shopify/Amazon/eBay, and the API routes that receive webhook events for new orders and dispute notifications.
Postgres (Supabase or Neon) — Core tables: stores, orders, fraud_scores, disputes, evidence_items, response_templates. Row-level security scoped to store ownership; disputes table indexed on filed_at for SLA tracking against processor response windows.
Shopify Admin API + Amazon SP-API + eBay Trading API — Platform-specific connectors for order data, tracking status, and (where available) native dispute/case webhooks; each platform's evidence format gets its own template mapper.
Claude or GPT-4o for evidence drafting — One prompt classifies dispute reason codes and required evidence types; a second drafts the rebuttal narrative from structured order/shipping/communication data, with the seller reviewing before submission.
BullMQ + Redis (or Inngest) — Background queue for fraud scoring on order-create webhooks and for the evidence-compilation job that runs the moment a dispute notification arrives — both need retry logic since a missed SLA window loses the case outright.
Stripe Billing — Subscription tiers plus a metered add-on for the performance fee on Scale accounts; customer portal for self-serve upgrades as a store's monthly revenue crosses tier thresholds.

AI Prompts to Build This

Copy and paste these into Claude, Cursor, or your favorite AI tool.

1. Project Setup

Create a Next.js 14 (App Router, TypeScript, Tailwind) project called "SellerShield." Provision Postgres (Supabase) with these tables: stores (id, platform TEXT CHECK platform IN ('shopify','amazon','ebay'), access_token, revenue_tier TEXT default 'starter'), orders (id, store_id, external_order_id, buyer_email, shipping_address, billing_address, payment_method_hash, total_cents, fraud_score FLOAT, status TEXT), disputes (id, order_id, reason_code, filed_at TIMESTAMPTZ, response_due_at TIMESTAMPTZ, status TEXT default 'open', outcome TEXT), evidence_items (id, dispute_id, type TEXT, url, extracted_text). Enable row-level security scoped to store ownership. Wire Stripe with three products: Starter $79/mo, Growth $199/mo, Scale $499/mo + metered 3% performance fee. Add env vars for SHOPIFY_API_KEY, AMAZON_SP_API credentials, EBAY_CLIENT_ID, and ANTHROPIC_API_KEY.

2. Fraud Scoring + Dispute Response Engine

Build two background jobs using BullMQ.
 
Job 1 — Order scoring: triggered on every new order webhook from Shopify/Amazon/eBay. Score the order 0-100 on: shipping/billing address mismatch, order velocity to the same payment method across a 7-day window, multiple high-value orders to different addresses in one session, and whether the buyer account was created in the last 24 hours. If score exceeds 70, hold fulfillment and notify the seller with the specific signals that triggered the flag.
 
Job 2 — Dispute response: triggered on a dispute/chargeback webhook or manual entry. Pull the order's tracking history, delivery confirmation, prior buyer messages, and product photos. Send this structured data to Claude with a prompt that (a) classifies which evidence format the specific platform/processor requires for this reason code, and (b) drafts a rebuttal narrative referencing the specific evidence items by name. Save the draft to evidence_items and surface it in the seller dashboard for one-click review and submission — never auto-submit without seller confirmation.

3. Landing Page

Design a single-page marketing site for SellerShield. Hero headline: "Stop losing chargebacks you should win." Subhead: "Predictive fraud screening before you ship, automated evidence-backed dispute responses after a claim is filed." Sections: a before/after stat block (dispute win rate improvement, hours saved per dispute), how it works (the 4-step flow: connect, screen, respond, learn), platform logos (Shopify, Amazon, eBay) to signal multi-channel support, pricing table (Starter $79 / Growth $199 / Scale from $499 + 3%) anchored against "what one lost $800 order costs you," and an FAQ covering data security, which platforms are supported, and average time-to-response. Use a dark near-black background, one accent color (emerald or amber), Geist for type, and a primary CTA "Connect your store — takes 3 minutes."