Trust forms the foundation of any successful peer-to-peer marketplace. Yet as these platforms grow, so do the sophisticated schemes designed to exploit them. From chargebacks and stolen payment methods to fake listings and account takeovers, marketplace fraud creates significant challenges for operators trying to maintain platform integrity.
The financial impact can be substantial: you may lose revenue through chargebacks, payment processing fees, and customer compensation, while your users suffer direct financial losses and diminished trust. Once trust erodes, both buyers and sellers migrate elsewhere, creating a downward spiral that can devastate marketplace growth.
In this article, we’ll examine the most common fraud schemes targeting P2P marketplaces today, breaking them down by buyer-side and seller-side tactics, and explore proven strategies to detect, prevent, and mitigate these threats before they damage your platform’s reputation and bottom line.
Seller-Side Fraud: When Vendors Turn Villains
Seller-side fraud poses unique challenges for marketplace operators as it directly threatens the trust buyers place in your platform. Let’s explore the most common schemes and how to protect your marketplace from these threats.
Fake Listings and Non-Delivery
The Problem: Fraudsters create seller accounts to list items they don’t possess, collect payments, then vanish without delivering anything. These scammers often use stolen identities to pass initial verification checks, post attractive listings (typically high-demand items at suspiciously low prices), and disappear once payments are received.
Example: A scammer lists the latest smartphone model at half the retail price on your marketplace. Multiple buyers rush to purchase, sending payment through your platform. The seller provides fake tracking numbers or no information at all, then disappears, leaving buyers empty-handed and your platform managing the fallout.
Solution:
- Implement rigorous seller verification (KYC) before allowing high-value listings
- Use algorithms to flag listings with prices significantly below market rates
- Hold payments in escrow until delivery confirmation
- Create a buyer protection program that guarantees refunds for non-delivery
- Develop risk scoring for new sellers based on listing patterns and account attributes
- Track and analyze seller behavior for sudden changes in listing volume or pricing
Solve it with a Filtyr AI moderation agent: Create a Filtyr agent that automatically reviews new listings, comparing prices against market benchmarks to flag suspiciously low-priced items. Configure the agent to analyze seller account history, examining factors like account age, previous listing patterns, and verification status to identify potential fake listing scams before they reach buyers.
Counterfeit and Misrepresented Goods
The Problem: Some sellers deliver products, but not as advertised. They list brand-name or high-quality goods but ship cheap knockoffs, used/broken items, or entirely different products. These fraudsters often use stolen images and descriptions from legitimate listings to appear genuine.
Example: A seller lists “authentic designer sunglasses” with professional product photos, but ships counterfeit replicas. When buyers complain, the seller might claim the items are “inspired by” the brand or simply ghost the platform.
Solution:
- Partner with brands for authentication programs on commonly counterfeited goods
- Implement image recognition to identify stolen product photos
- Create specialized moderation for high-risk categories (luxury items, electronics, etc.)
- Develop a post-purchase review system that specifically asks about authenticity
- Establish clear return policies for items “not as described”
- Monitor repeat offenders and quickly remove their accounts
- Use machine learning to detect patterns in counterfeit listings across your platform
Solve it with a Filtyr AI moderation agent: Deploy a Filtyr agent trained to detect counterfeit listings by analyzing product images, descriptions, and pricing patterns. The agent can scan for copied images from legitimate retailers, identify language patterns common in counterfeit listings, and flag suspicious items in high-risk categories like luxury goods and electronics for human review before they go live.
Bait-and-Switch Scam
The Problem: The seller deliberately sends a different item than what was advertised after securing payment. Victims believe they’re getting a great deal but receive something worthless or inferior, such as an empty box, cheap knockoff, or just accessories instead of the main product.
Example: A buyer purchases what appears to be a new gaming console from a marketplace listing but receives only the console’s box with no device inside or a similar-weight random object. Some crafty scammers have even sold photos of items (cleverly described in fine print) rather than the items themselves.
Solution:
- Encourage in-person exchanges for high-value items where practical
- For shipped items, require tracking and delivery confirmation
- Allow buyers to flag significant discrepancies between listings and received items
- Create a dispute resolution process specifically for bait-and-switch claims
- Ban sellers with verified bait-and-switch complaints
- Use AI to detect listing descriptions with potentially deceptive language
Solve it with a Filtyr AI moderation agent: Implement a Filtyr agent that reviews listing content for deceptive language or fine print that could indicate bait-and-switch tactics. Configure the agent to scan for misleading terms like “photo of item” or suspiciously vague descriptions. The agent can also monitor buyer complaints post-purchase to identify potential bait-and-switch patterns from specific sellers for proactive investigation.
Advance Payment & Off-Platform Payment Scams
The Problem: Fraudulent sellers pressure buyers to pay outside your platform’s official channels, often via wire transfers, gift cards, or peer-to-peer payment apps that offer limited buyer protection. They typically create urgency with stories about “other interested buyers” or offer discounts for alternative payment methods.
Example: A scammer lists an apartment rental, claiming there’s high demand. They ask potential renters to send a security deposit via a cash app to “secure” the property and receive keys. After payment, the lister disappears — there was never a real apartment.
Solution:
- Monitor messaging for payment method discussions and flag suspicious patterns
- Implement clear warnings about off-platform payment risks
- Create automated detection for phone numbers, emails, and payment app handles in messages
- Educate users through safety tips and warnings during the transaction process
- Consider penalizing accounts that repeatedly attempt to move payments off-platform
- Develop secure on-platform payment flows that are convenient enough to discourage workarounds
Solve it with a Filtyr AI moderation agent: Set up a Filtyr agent to monitor all messaging between buyers and sellers, detecting attempts to move transactions off-platform. When detected, the agent can trigger immediate warnings to buyers about payment scams and alert your trust and safety team to investigate the seller.
Reputation Fraud
The Problem: Sellers manipulate your marketplace’s trust indicators through tactics like shill bidding (where they or accomplices bid on their own items to drive up prices) or fake reviews (creating dummy accounts to leave positive feedback or purchasing their own items to boost ratings).
Example: On an auction marketplace, a seller’s friends repeatedly bid on a vintage item, artificially inflating the price until genuine bidders top them. Alternatively, a shady seller might maintain dozens of sock-puppet accounts to create a wall of perfect 5-star reviews with generic comments.
Solution:
- Implement algorithms to detect suspicious bidding patterns, such as multiple bids from related accounts
- Prohibit bidding by seller associates and enforce with IP/device tracking
- Require verified purchases for leaving reviews
- Use natural language processing to identify fake review patterns
- Track relationships between accounts through device fingerprinting and behavioral analysis
- Create “verified buyer” tags for authentic reviews
- Allow buyers to sort reviews by verification status
Solve it with a Filtyr AI moderation agent: Deploy a Filtyr agent dedicated to reputation fraud detection that analyzes review content, timing, and patterns. The agent can identify suspicious clusters of positive reviews with similar writing styles or posted in rapid succession. For auction platforms, configure an agent to monitor bidding behaviors, flagging potential shill bidding based on account relationships, bid timing, and historical patterns between specific buyers and sellers.
Seller Account Takeover
The Problem: Rather than create new profiles, some fraudsters hack legitimate seller accounts with established reputations. Through phishing or credential stuffing, they gain control of trusted accounts, change payout details, post fraudulent listings, or message buyers to redirect payments.
Example: A reputable electronics seller’s account suddenly starts listing unusually cheap TVs and requests wire transfers to different accounts. In reality, scammers have compromised the account and are trying to exploit the seller’s hard-earned trust before the real owner regains control.
Solution:
- Enforce strong security measures like two-factor authentication (this is the best and simplest solution)
- Generate alerts for unusual account activity (new listing patterns, price changes, new payment methods)
- Implement IP-based location alerts when accounts are accessed from new locations
- Develop fast-track processes for account recovery requests
- Temporarily freeze accounts showing signs of compromise
- Verify major changes to account details through secondary confirmation channels
- Educate sellers about phishing tactics targeting marketplace accounts
Solve it with a Filtyr AI moderation agent: Implement a Filtyr agent that monitors account behavior to detect potential takeovers.
Buyer-Seller Collusion & Money Laundering
The Problem: Sophisticated fraud rings control both buyer and seller accounts to exploit your platform. In closed-loop schemes, fraudsters “purchase” non-existent goods from themselves using stolen payment methods, effectively laundering money through your marketplace. They may also manipulate your incentive systems through fake transactions to earn referral bonuses or hit sales targets for rewards.
Example: Criminals set up multiple buyer-seller pairs on your platform. The “buyers” use stolen credit cards to pay the “sellers” for fictitious items. The sellers quickly withdraw funds before chargebacks occur, leaving your platform to cover the losses while the fraudsters have successfully converted stolen card data into cash.
Solution:
- Monitor for unusual patterns like repeated transactions between the same accounts
- Implement velocity limits on new seller withdrawals
- Apply extra scrutiny to new sellers with immediate high-value orders
- Use graph analysis to identify networks of potentially related accounts
- Adhere to KYC (Know Your Customer) and AML (Anti-Money Laundering) regulations
- Implement holding periods for payouts on suspicious transactions
Solve it with a Filtyr AI moderation agent: Implement a Filtyr agent designed to detect potential collusion and money laundering activities across your marketplace. Configure the agent to prioritize the strongest indicator of fraud: newly created buyers purchasing high-valued items from newly created sellers. The agent can automatically flag these high-risk transactions for immediate review, while also analyzing secondary signals like circular transactions, unusual pricing, and suspicious account relationships. By focusing on account age in combination with transaction value, your team can efficiently target the most likely instances of collusion before funds are released to potentially fraudulent sellers.
Seller Side Conclusion
By implementing targeted solutions for each type of seller fraud, marketplace operators can significantly reduce risk exposure while maintaining a smooth experience for legitimate users. The key is balancing security with convenience through smart technology deployment, clear policies, and continuous seller education.
Using Filtyr’s AI moderation agents to review listings and seller behaviors gives you comprehensive coverage to detect and prevent these fraud schemes before they damage your marketplace’s reputation and financial health.
Buyer-Side Fraud: When Customers Become Scammers
Buyer-side fraud can be particularly damaging as it exploits the trust marketplaces place in their customers. Let’s examine the most common schemes and how to protect your platform against them.
Chargeback Fraud (Friendly Fraud)
The Problem: This occurs when buyers make legitimate purchases, receive the items, then dishonestly dispute the charges with their bank or credit card company, claiming they never authorized the transaction or never received the product. The result? The seller, loses both the payment and merchandise, while your marketplace faces chargeback fees and increased fraud rates. In some cases, like PangoBooks, the marketplace carries the loss for the seller.
Example: A buyer purchases a high-end smartphone, receives it, then claims to their credit card company that the transaction was unauthorized. The bank reverses the charge, leaving the seller without payment while the buyer keeps the device.
Solution: Fighting this effectively comes down to having the proper proof of sale to dispute chargebacks.
Here are some tips:
- Your platform should maintain detailed transaction and communication records to use when needed for chargeback disputes
- Create seller protection programs
- Keep track of accounts that file chargebacks
- If necessary, implement delivery confirmation systems that capture proof of receipt
Solve it with a Filtyr AI moderation agent: Create a Filtyr agent that reviews transaction patterns and user behaviors to identify potential chargeback fraud risks. Configure the agent to flag accounts with suspicious purchasing patterns, such as rapid high-value purchases from new accounts or buyers with a history of disputes. When high-risk transactions are identified, the agent can automatically collect and organize evidence (transaction records, communication history, delivery confirmations) to help your team quickly respond to chargeback disputes
Stolen Card Purchases
The Problem: Fraudsters use stolen credit card information to make purchases on your marketplace. When the real cardholder discovers the unauthorized charges, they initiate a chargeback. The seller ships the product but never receives payment, and your marketplace faces penalties from payment processors.
Example: A scammer uses stolen card details to buy a laptop. The seller ships the item, but weeks later receives a chargeback notification when the actual cardholder discovers and reports the fraudulent purchase.
Solution: This is ideally prevented at the point of sale. Stripe Radar for example, allows you to set rules to block transactions for suspicious behavior like card testing. If you can’t block it at the point of sale, have a review queue for finding and refunding these immediately. You can easily look for fraud like this with Filtyrs AI moderation agents.
- Use 3D Secure authentication for high-risk transactions
- Detect and block “card testing” where multiple cards are being tested at transaction time
- Create risk scoring models based on transaction characteristics
- Flag suspicious shipping addresses that don’t match billing information
Solve it with a Filtyr AI moderation agent: For those sales that get through point of sale detections, have a Filtyr agent review all orders for red flags such as multiple card attempts, mismatched billing and shipping addresses, unusual purchasing velocity, and order values that deviate from typical user behavior. When suspicious transactions are detected, the agent can immediately flag them for your team’s review, allowing you to contact the buyer for verification or cancel orders before chargebacks occur.
Triangulation Fraud
The Problem: This complex scheme involves three parties: a legitimate buyer, a fraudster, and an unsuspecting seller. The fraudster lists items they don’t own, then when a legitimate buyer purchases from them, they buy the item from a real seller using stolen payment information. The seller ships to the legitimate buyer, and the fraudster pockets the difference. When the stolen card is reported, the seller faces a chargeback.
Example: A fraudster lists a discounted designer handbag. When a buyer purchases it, the fraudster uses a stolen credit card to buy the same bag from a legitimate seller, having it shipped directly to the buyer. The seller eventually faces a chargeback while the scammer keeps the profit.
Solutions:
- Monitor for mismatched shipping and billing addresses
- Implement customer identity verification for high-value purchases
- Track connected accounts through device fingerprinting
- Look for off-platform communication that facilitates these schemes
- Create marketplace-specific payment flows that are harder to manipulate
- Build algorithms to detect unusual price discrepancies across similar listings
Solve it with a Filtyr AI moderation agent: Create a Filtyr agent that reviews orders for telltale signs of triangulation fraud, such as shipping addresses that don’t match billing information, accounts making purchases for diverse shipping destinations, or unusual patterns between specific buyers and sellers. The agent can correlate data across your platform to identify potential fraud networks and alert your team to investigate suspicious relationships before shipments are processed.
False Claims and Refund Abuse
The Problem: Dishonest buyers abuse your platform’s buyer protection policies by falsely claiming items weren’t delivered or arrived damaged, or by returning counterfeit/different items than what they received.
Example: A buyer purchases a smartphone, then returns an older broken model while keeping the new one. Alternatively, they might claim a perfect delivery was damaged to get a partial refund while keeping the item.
Solutions:
- Require photographic evidence for damage claims
- Monitor accounts with frequent return or refund activity
- Implement serial number verification for electronics and valuable items
- Create detailed return inspection processes
- Use AI to analyze patterns in customer complaint language
- Build trust scores that limit high-risk customers’ ability to make claims
- Develop specialized handling for categories with high return fraud
Solve it with a Filtyr AI moderation agent: Implement a Filtyr agent that analyzes return and refund requests to identify potential abuse. Configure the agent to examine customer claim language, account history, purchase patterns, and timing of refund requests to assign a risk score. For high-risk claims, the agent can prompt buyers to provide specific evidence (photos, videos) and generate detailed reports for your support team to make informed decisions about approving or denying claims.
Buyer Identity Theft & Social Engineering
The Problem: Fraudsters pose as interested buyers to trick sellers into revealing sensitive information or verification codes, which can then be used to hijack accounts, steal identities, or facilitate other scams.
Example: A scammer contacts a seller expressing interest in an item, then claims they’ll send a verification code to the seller’s phone to confirm they’re a real person. This code is actually for setting up a Google Voice account in the seller’s name or for accessing their accounts.
Solutions:
- Keep all communications on your platform to monitor for scam patterns like this
- Create automated detection for phone numbers and verification codes in messages
- Implement clear warnings about common social engineering tactics
- Never ask users to share verification codes with other users
- Educate users about these schemes through platform notifications
- Provide a secure identity verification system that doesn’t expose personal data
- Build robust account recovery processes that don’t rely solely on SMS codes
Solve it with a Filtyr AI moderation agent: Set up a Filtyr agent to monitor all platform messages for social engineering attempts and phishing patterns. The agent can identify when conversations include requests for verification codes, payment information, or personal details by scanning for specific phrases and question patterns. When detected, the agent can automatically send warnings to users about potential scams and alert your trust and safety team to investigate the suspicious account.
Buyer Side Conclusion
By implementing targeted solutions for each type of buyer fraud, marketplace operators can significantly reduce risk exposure while maintaining a smooth experience for legitimate users. The key is balancing security with convenience through smart technology, clear policies, and continuous user education.
Using Filtyr’s AI moderation agents to review all orders prevents overly aggressive point of sale rules that can block legitimate transactions while also giving you 100% coverage so you can quickly handle true positives.
Conclusion
Protecting your P2P marketplace from fraud requires a multi-layered approach that addresses both buyer and seller-side threats. As fraudsters develop increasingly sophisticated tactics, marketplace operators must stay vigilant by implementing robust monitoring of orders, listings, messages, and accounts.
Filtyr’s AI moderation agents offer a powerful solution by providing comprehensive coverage across all aspects of your platform. These intelligent agents can automatically detect suspicious patterns in listings, messages, and transactions before they result in financial losses or reputational damage. By deploying customized Filtyr agents to monitor high-risk activities, you can protect legitimate users while efficiently identifying and stopping fraudulent behavior — all without creating friction in the user experience that might drive away honest customers. With Filtyr’s help, you can build a trusted marketplace where buyers and sellers can transact with confidence.