Hudson Data

Use cases · Marketplace & e-commerce

Trust the two sides. Decide every transaction.

Marketplaces face risk from both sides of the listing — buyer abuse, seller fraud, payment fraud, promo abuse, and reputation manipulation. Centurion plugs into every point of that journey, on a single decisioning fabric, with every decision audit-logged.

By customer journey

Five lifecycle stages. Every dollar of marketplace risk flows through them in order.

From buyer acquisition to seller payout — Centurion plugs into every stage where marketplace risk gets decided. Find your stage. Find your use case.

  1. 01

    Attract

    Acquire buyers & sellers

    • Seller-acquisition lead scoring
    • Buyer-acquisition channel risk
    • Affiliate / referral channel quality
    • Marketing-spend risk weighting
  2. 02

    Acquire

    Sign up & onboard

    • Buyer account opening
    • Seller onboarding & KYC / KYB
    • Document & business verification
    • Mule-seller and shell-storefront detection
    • Synthetic-identity defense at signup
  3. 03

    Engage

    Authenticate, list & transact

    • Login & session-trust scoring (ATO, bots)
    • Listing creation risk (counterfeit, off-platform, illicit)
    • Buyer-seller messaging-fraud signal
    • Checkout & payment authorization
    • Cross-border transaction screening
  4. 04

    Grow

    Service & expand

    • Promo, coupon & referral-abuse defense
    • Loyalty-point cash-out detection
    • Seller-velocity & wash-trading detection
    • Fake-review-ring and reputation manipulation
    • Seller-account takeover (storefront hijack)
    • Account-change risk (payout details, banking)
  5. 05

    Resolve

    Disputes & recovery

    • Friendly-fraud & policy-abuse classification
    • Refund-abuse and returnless-refund exploit
    • Chargeback representment
    • Triangulation-fraud unwinding
    • Seller payout hold / claw-back routing

The fraud surfaces

Eight surfaces every marketplace has to defend.

Marketplaces concentrate risk in ways single-sided platforms don't. The defense has to read the buyer, the seller, the listing, and the payment — together, at decision time.

Listing fraud

Counterfeit goods, off-platform redirection, illicit categories, brand-jacking. Surface listings before they convert — graph signal across seller, image, IP, and language pattern.

Seller account takeover

Storefront hijack via credential stuffing, session replay, payout-detail change. Behavior + device + telephony coherence catches the changeover before funds redirect.

Fake-review rings

Coordinated review manipulation across buyer cohorts. Population-level graph detection — identifier overlap, behavioral homogeneity, timing clustering across reviewers.

Promo & coupon abuse

Sign-up bonus farming, coupon stacking, referral self-loop, loyalty-point cash-out. Cohort-level identifier-overlap scoring at redemption time.

Refund & return fraud

Refund abuse, returnless-refund exploit, return-of-empty-box, friendly-fraud chargebacks. Disputed-transaction classification with behavioral-trajectory signal.

Triangulation & payment fraud

Card-not-present fraud, triangulation rings using stolen cards through marketplace listings, BIN-attack pattern, gift-card laundering.

Buyer-side abuse

INR (item-not-received) abuse, SNAD (significantly-not-as-described) gaming, dispute-velocity rings. Buyer-trust scoring with cross-merchant consortium signal.

On-platform value exchange

Money laundering via marketplace listings, gift-card recycling, off-platform value transfer disguised as listings. AML-grade transaction monitoring.

What changes

Higher buyer trust. Lower seller fraud. Fewer chargebacks. The same approval rate on legitimate transactions — without the manual-review queue you've been building around.

For marketplace risk leaders

See it on your traffic.

We'll run a benchmark on a slice of your historical listings, transactions, and disputes — quantified lift, before you ship anything.