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How to Tackle First-Party Fraud Without Overwhelming Your Fraud Team

First-party fraud is a growing threat to businesses and a constant headache for fraud teams.

Unlike traditional fraud, it's committed by seemingly legitimate customers who exploit loopholes and policies to commit fraud. 

First-party fraud is particularly hard to detect and prevent due to its complexity, often overwhelming fraud teams who must identify suspicious patterns and coordinate with different departments, sometimes even involving legal action.

Fraudsters are getting smarter, so businesses need to understand their tactics and patterns in order to implement strong prevention strategies. This article explores:

  • Common types of first-party fraud
  • Warning signs to watch for
  • Best practices for tackling first-party fraud

What is First-Party Fraud?

First-party fraud happens when a legitimate customer intentionally deceives a business for personal gain. 

Unlike third-party fraud, which involves a fraudster using stolen identities or credit card information, first-party fraud is perpetrated by individuals who appear to be genuine customers. This type of fraud is particularly challenging to detect because it often involves customers with established relationships with the business, making their activities less suspicious.

The impact of first-party fraud is significant across various industries, including e-commerce, marketplaces, and fintech companies.

Why Friendly Fraud is So Challenging

Friendly fraud, a common form of first-party fraud, involves legitimate customers making purchases and then disputing the transactions, claiming they didn't authorize them. This makes it hard to distinguish between honest mistakes and fraud. Here are some reasons why friendly fraud overwhelms fraud teams:

  1. Ambiguity and Misunderstanding: Customers may not recognize a charge, forget about a purchase, or misunderstand the merchant's return policy. This ambiguity makes it hard to differentiate between accidental and intentional fraud.

  2. Legitimacy of Customers: Identifying and preventing fraud from customers who have a history of legitimate transactions is challenging because it makes their fraudulent chargebacks less suspicious. It’s quite common for businesses to avoid confronting or penalizing these customers out of fear that they’ll lose future business.

  3. Burden of Proof: In chargeback disputes, the burden of proof often falls on the merchant, although Visa CE 3.0 is working on improving this. To contest a chargeback, businesses need to provide compelling evidence that the transaction was legitimate and that the customer received the goods or services. This process is time-consuming, costly, and often favors the customer. And if you sell low-cost items, the dispute costs may even be higher than the amount you would recover if you won the dispute.

  4. Increased Operational Costs: Dealing with first-party fraud involves significant operational costs, including staff training, chargeback management systems, and legal resources to handle disputes and updating policies. These costs, combined with financial losses from fraudulent chargebacks, can significantly impact profitability.

  5. Evolving Tactics: Fraudsters constantly adapt their tactics to exploit vulnerabilities in the chargeback process. They use sophisticated tools to mimic legitimate customer behavior, emulate user devices, and hide their IP address and location. Staying ahead of these evolving tactics requires continuous investment in staffing and monitoring tools.

Common Fraud Types Across Different Industries

First-party fraud can take many forms, depending on the industry. Your business’ exposure to fraud is largely influenced by its customer interactions, transaction types, the amount of information you collect, and your operational processes.

Understanding how these risks vary across industries can make your fraud prevention strategy more effective. We’ve found it particularly useful to examine fraud patterns in high-risk industries such as crypto, fintech, and digital goods (ticketing, iGaming), where fraudsters often test out new tactics. These attack patterns eventually trickle down to other sectors like retail and banking, so this will give you insight into emerging threats.

Let’s look at some specifics and warning signs for each industry.

Ecommerce Fraud Types and Warning Signs

In ecommerce, the high volume of transactions combined with the anonymity and convenience of guest checkout increases the risk for return fraud, chargeback fraud, and promo abuse. Fraudsters know ecommerce companies are heavily focused on conversions, so they’ll exploit any weakness they can find in order to convince you to approve their transactions.


Wardrobing is a common type of return fraud where customers purchase items, use them, and then return them for a full refund. This practice is prevalent in the fashion industry but can affect other sectors as well.

Example: A customer has a long history of purchases with an online store. Their average purchase is under $100. However, every month, they purchase an item with an abnormally high price tag and return it a few days later, citing issues covered by the refund policy.

Warning Signs:

  • Frequent returns of high-value items.
  • Returns of items that appear worn or used.
  • Customers who regularly purchase and return items within short time frames.
  • Consistent use of the same device or IP for multiple high-value returns.
  • High volume of returns from a single address or customer.
  • Abnormally high transaction amounts compared to purchase history.

Chargeback Abuse

Chargeback abuse, or friendly fraud, occurs when customers dispute legitimate transactions to get a refund while keeping the goods or services. 

Example: Customer who has made numerous purchases over the past year starts disputing charges shortly after confirming delivery. Each dispute is initiated from the same device used for previous purchases, and the customer's interaction patterns show no signs of unusual behavior during the transaction periods.

Warning Signs:

  • Customers frequently dispute charges across retailers.
  • High volume of chargebacks from the same customer.
  • Disputes raised soon after delivery confirmation.
  • Disputing multiple transactions over a short period.
  • Disputes on high-value items without contacting customer support first.

Promo Abuse

Promo abuse happens when customers exploit promotional offers or discounts beyond their intended use. This can include creating multiple accounts to take advantage of discount codes, using fake information to qualify for offers, or creating fake accounts to get referral payouts.

Example: A customer takes advantage of a discount on your first purchase by creating several new accounts, each with minor variations in username and email address. All of these accounts seem to have been created from the same IP address, and the activity across these accounts is strikingly similar.

Warning Signs:

  • Multiple accounts with similar information (e.g. name, IP address, shipping address).
  • Unusual redemption patterns of promotional offers.
  • Sudden spikes in promotional usage.
  • Large quantities of items purchased using promotional codes.
  • Fast navigation paths with a short time between account creation and purchase.

Marketplaces: Specific Fraud Types and Warning Signs

Marketplaces, which connect buyers and sellers, present unique first-party fraud challenges because you need to monitor and verify both parties involved in transactions. This section investigates common types of first-party fraud in marketplaces and warning signs.

Seller Collusion

Seller collusion occurs when multiple sellers collaborate to manipulate the marketplace system for their mutual benefit. This can involve inflating product ratings, generating fake sales, or coordinating to raise prices.

Example: Several seller accounts that have identical or very similar product listings receive a surge in positive reviews within a short period. Device fingerprinting shows these accounts are managed from the same device, and the abnormally short time spent on crafting each listing and review response indicates that there may be some type of bot or script in use.

Warning Signs:

  • Multiple sellers with interconnected accounts or similar product listings.
  • Unusual patterns in ratings and reviews that suggest manipulation.
  • Sudden spikes in sales or price changes for certain products.
  • Coordinated timing of product launches and price changes.
  • High volumes of transactions between a specific group of sellers.
  • Multiple seller accounts accessed from the same device.

Shill Bidding

Shill bidding involves sellers using fake bids to drive up the price of their own items in auction-based marketplaces. This practice creates a false sense of demand and can deceive legitimate buyers into overpaying.

Example: An auction item receives numerous bids from accounts with no transaction history. These bids are placed in quick succession from the same device, and the timing and pattern of these bids suggest a coordinated effort to inflate the item's price.

Warning Signs:

  • Bidders with no prior history or feedback on the platform.
  • Unusually high bidding activity on certain items.
  • Patterns of bidding that suggest coordination between the seller and bidders.
  • Bids placed in quick succession by new or inactive accounts.
  • Sudden withdrawal of high bids before the auction ends.
  • Similar bid amounts from different accounts on the same item.
  • Multiple bids from the same device but different accounts.

Phantom Goods

Phantom goods fraud occurs when sellers list items that do not exist or will never be delivered. This differs from fake listings by focusing on the promise of non-existent high-demand items, often with significant lead times to delay detection.

Example: A seller frequently lists high-demand electronics with delivery times of several weeks. Device analysis shows these listings are created from a single device, and behavioral signals reveal a pattern of quickly posting listings with very little detail provided, followed by numerous buyer complaints about non-delivery.

Warning Signs:

  • Listings with exceptionally long delivery times.
  • Lack of detailed product information or stock images.
  • Complaints from buyers about undelivered items after the expected delivery date.
  • Sellers who frequently change their contact information.
  • Listings for high-demand items with unusually low prices.
  • Sellers who avoid answering buyer inquiries about the product.

Item Not As Ordered Fraud

Item not as ordered fraud happens when buyers falsely claim that the items they received were significantly different from what was described in the listing. This type of fraud is aimed at receiving a refund or replacement without returning the original item.

Example: A buyer repeatedly claims that expensive items do not match descriptions and demands refunds. Device intelligence shows these complaints are filed from the same device used for making the purchases, and behavioral analysis indicates the buyer's interactions during both the purchase and complaint processes are consistent and deliberate.

Warning Signs:

  • Frequent complaints from the same buyer about item discrepancies.
  • Claims of missing or incorrect items from high-value orders.
  • Buyers who refuse to provide evidence of discrepancies or return the items.
  • Buyers who consistently demand refunds.
  • Discrepancies between the buyer's complaint details and the original order description.

Fintechs: Specific Fraud Types and Warning Signs

Fintech companies, offering financial services through digital platforms, face unique fraud risks due to the sensitive nature of financial data and transactions. The regulatory environment adds another layer of complexity, requiring fintechs to comply with stringent anti-fraud and anti-money laundering regulations. 

This section explores some of the most common types of first-party fraud in fintech companies and the warning signs that can help identify them.

Loan Stacking

Loan stacking involves legitimate customers applying for multiple loans or lines of credit from different lenders within a short period, without the intention of repaying them. This exploits the delay in credit reporting, allowing the fraudster to receive funds before the loans are registered on their credit profile.

Example: An individual applies for several loans from different financial institutions within a few days. All applications originated from the same device, and behavior signals reveal consistent patterns in the submission of information. The individual immediately withdrawals funds once a loan has been extended and the account becomes inactive shortly thereafter.

Warning Signs:

  • Multiple loan applications from the same individual in a short time frame.
  • Applicants with a history of recent credit inquiries.
  • Inconsistent financial information across different loan applications.
  • Applicants using the same documentation for different loan applications.
  • Spike in inactive accounts becoming active.

ACH Fraud

ACH fraud involves legitimate account holders initiating unauthorized ACH transactions to withdraw funds from their own or other linked accounts. Fraudsters may exploit the system to make unauthorized transfers, then claim these transactions were made without their consent.

Example: A customer suddenly initiates a series of high-value ACH transfers to several newly linked accounts. These transfers occur late at night and over weekends, times when the customer typically has no transaction activity. Device intelligence shows these transactions are initiated from the customer’s usual device, and the customer’s behavior patterns are consistent with their normal behavior, indicating this doesn’t look like an account takeover.

Warning Signs:

  • Unusual or unexpected ACH transactions on customer accounts.
  • Sudden changes in the frequency or amount of ACH transfers.
  • Accounts with multiple failed ACH transaction attempts.
  • High-value ACH transfers shortly after opening a new account.
  • ACH transfers to accounts with no prior transaction history.
  • Multiple ACH transactions initiated outside of usual business hours.

Ghost Funding

Ghost funding involves customers using fake businesses or exaggerated claims to obtain loans or lines of credit. These loans are often defaulted once the funds are disbursed, with no intention of repayment. This is very similar to loan stacking.

Example: A business applies for a large line of credit. Upon approval, the business quickly draws down the entire credit line within a few days. Investigations reveal that the company has no verifiable business history and a minimal online presence. Additionally, the user’s device was associated with other accounts with denied loan applications, suggesting the use of falsified information across multiple attemts.

Warning Signs:

  • Loan applications from entities with no verifiable business history.
  • Applicants providing limited or unverifiable contact information.
  • Rapid drawdown of funds from newly established credit lines.
  • Businesses with no online presence or verifiable contact details.
  • Businesses with acquiring volumes that are lower than what’s stated in paperwork.
  • Loan applications with identical financial statements.

Application Fraud

Application fraud occurs when individuals provide false or misleading information on financial applications to obtain credit, loans, or other financial services. This can include using altered documents, falsified income information, or fake business credentials.

Example: A customer submits a loan application claiming a high income and stable employment. But the income and employment details provided in the application contain discrepancies when compared to other sections. The applicant also hesitates to provide additional documentation when requested. 

Warning Signs:

  • Discrepancies in the information provided on different sections of an application.
  • Applicants unwilling or unable to provide additional documentation when requested.
  • Sudden influx of applications from similar IP addresses or physical locations.
  • Applicants using temporary or unverifiable addresses.

How to Tackle First-Party Fraud Like an Expert

Although tackling first-party fraud can be challenging, there are various tools and tactics that can help your fraud team make a big impact. These strategies can balance the customer experience with effective fraud prevention, providing your team with the necessary resources to address first-party fraud effectively.

Device intelligence

Device intelligence plays a crucial role in identifying potential fraud by analyzing data from the devices used to access your website and mobile apps. 

This includes capturing device fingerprints, which gather unique identifiers such as IP addresses, browser types, and operating systems. But device intelligence goes beyond just fingerprints; it also provides visibility into a user's accelerometer and gyroscope data, and can detect the use of mobile emulators, VPNs, residential proxies, remote access tools, and common bot tools like Selenium or BlueStacks.

By combining all these data points, device intelligence helps unmask the user behind the device and provides insights into their intent. You can determine their true IP address and location, check for suspicious history or connections to other users, and identify if someone is spoofing a device or performing a social engineering scam, such as fake tech support.

Example: Your team is investigating multiple high-value purchases disputed by a customer who claims they never made those transactions. Using device intelligence, your team analyzes the devices used to make the purchases. 

  • Device fingerprinting reveals that all transactions were made from a single device previously associated with the customer's account. 
  • Additionally, the analysis detects the use of a residential proxy and emulation software. 
  • Anomaly detection shows no unusual behavior from this device during the transaction period, and the risk scoring system indicates that the device has a low risk score based on its history. 

This comprehensive evidence suggests that the transactions were likely made by the customer, supporting a case against the friendly fraud claim.

Behavior Biometrics

Behavior biometrics involves monitoring and analyzing how users interact with your platform to identify patterns indicative of fraud. By tracking unique behavioral characteristics such as typing patterns, mouse movements, and touchscreen interactions, businesses can detect anomalies that may indicate fraudulent activity.

Behavioral solutions not only capture how a user types or moves their mouse but also monitors their interaction speed, rhythm, and even how they hold their device. This data helps in creating a behavioral profile for each user, allowing the detection of deviations from their typical behavior. Additionally, behavior biometrics can flag rapid navigation, repeated failed login attempts, and inconsistent interaction patterns, which are often indicators of fraudulent activity.

Combining behavioral data points with device signals can drive exponentially better results in your fraud prevention efforts. For example, while device intelligence can identify if a device is suspicious, behavior biometrics can confirm whether the user interaction is consistent with past legitimate behavior, adding another layer of security and accuracy in fraud detection.

Example: An investigator on your team is reviewing a customer who has several disputed transactions, claiming their account was hacked. The fraud team uses both behavior biometrics and device intelligence to analyze the interactions. 

  • Behavioral biometrics reveal that the typing patterns, mouse movements, and touchscreen interactions during the disputed transactions match the customer's typical behavior. 
  • Device intelligence confirms that all transactions were made from a known device previously associated with the customer's account, with no signs of emulators or proxies. Session monitoring shows no signs of rapid navigation or repeated failed login attempts. 
  • Machine learning models confirm that the behavior during the transactions was consistent with the customer's usual patterns. 

These combined findings indicate that the account was not hacked and that the customer likely made the transactions, pointing to a case of first-party fraud.

Transaction Monitoring

Transaction monitoring involves observing customer transactions in real-time or retroactive (in batch) to identify trends and red flags. This process is crucial for detecting and mitigating risks associated with first-party fraud.

Real-time monitoring systems can flag suspicious transactions as they happen, allowing for immediate action. Retrospective analysis, on the other hand, involves looking back at past transactions to identify patterns that might indicate financial crime. These methods combined offer a comprehensive approach to fraud detection.

Example: While your team is busy, you receive a high-risk escalation for review. A customer frequently disputes transactions, claiming unauthorized purchases. 

  • The fraud team uses transaction monitoring tools to investigate. 
  • Rule-based systems flag the disputed transactions because they match known fraud patterns of high-value purchases within a short period. 
  • Real-time alerts had previously been generated for these transactions, but no immediate action was taken. Historical analysis shows a pattern of similar transactions by the same customer, indicating a recurring behavior.

This evidence builds a strong case for identifying the customer as committing first-party fraud.

Link Analysis

In cases where there are numerous disputed transactions from multiple customers, the fraud team can use link analysis (or cluster analysis) to identify patterns and connections between the accounts. Link analysis groups similar data points together, helping to uncover relationships that may not be immediately obvious.

Example: If multiple customers are disputing transactions and claiming fraud, link analysis might reveal that these customers all signed up within the same time period, used similar email addresses, or conducted transactions with the same vendors. 

This insight can suggest organized fraud activity rather than isolated incidents of friendly fraud, allowing the fraud team to take targeted action against a broader scheme.

By now, you are well-versed in the challenges and complexities of detecting first-party fraud. Although one of these tactics alone won’t solve your friendly fraud problem, By applying these tools—device intelligence, behavior signals, identity verification, network intelligence, transaction monitoring, data enrichment, and cluster analysis—fraud prevention teams can make a real impact against first-party fraud.

How Sardine thinks about Tackling First-Party Fraud Without Overwhelming Your Fraud Team

At Sardine, we specialize in fraud prevention, providing solutions designed by operators for operators. We understand the unique challenges and pressures that first-party fraud places on your business and your fraud team.

Our comprehensive suite of tools, including APIs, dashboards, rules, and machine learning models, analyze a wide range of pre-authentication signals. By processing billions of data points through behavior-based ML models, we can accurately discern user intent and differentiate legitimate users from fraudsters.

Here's how Sardine can help you effectively tackle first-party fraud:

  1. Real-time Fraud Detection: Our device and behavior solution captures unique identifiers from devices accessing your platform, as well as behavioral biometrics that track user interactions like typing patterns and mouse movements, we can swiftly identify and prevent fraudulent activities. We uniquely combine these two solutions in one SDK, so you only have to integrate one solution.

  2. Identity Verification: During user onboarding, our tools authenticate users with document verification, biometric checks, real-time data validation, and deeper KYC if needed. We use a progressive approach to identity verification where we start with friction-less checks to provide a better experience to your good users, and only introduce step up verifications when a higher risk pattern or anomaly is detected.

  3. Transaction Monitoring: We continuously analyze transactions using rule-based systems, custom machine learning models, and real-time anomaly detection to detect suspicious activities. This allows for immediate action when potential fraud is detected. However, we also support traditional batch monitoring and queues to help compliance officers manage their AML program requirements.

  4. Data Enrichment: Sardine comes fully integrated with 40+ leading data providers for phone, email, network, geolocation, bank, card, credit bureau, and more. This saves significant development and integration resources, makes investigations easier because all your data is in one dashboard, and provides you with the most comprehensive view of user and transaction risk.

  5. Advanced Analytics and Reporting: Our platform also includes detailed insights into rule performance, enabling your fraud prevention teams to refine and adjust their strategies effectively. With Sardine, you can segment sessions and create targeted verification flows for high-risk activities before checkout. This approach ensures that legitimate users pass through seamlessly, while enhanced scrutiny is applied only where needed.

Sardine can help your team manage first-party fraud efficiently without being overwhelmed, maintaining a balance between robust security and a smooth customer experience.

Summary of Key Takeaways

First-party fraud poses significant challenges for e-commerce, marketplaces, and fintech companies. Understanding its various forms and implementing effective strategies are crucial yet often overlooked steps in robust fraud prevention.

  1. Understand First-Party Fraud: Recognize the various types of first-party fraud and how they impact different industries. Awareness of these fraud schemes is the first step in combating them effectively.

  2. Implement Advanced Tools: Leverage technologies such as device intelligence, behavior biometrics, and transaction monitoring. These tools help detect and prevent fraudulent activities by providing deep insights into user behavior and transaction patterns.
  3. Enhance Fraud Prevention Efforts: Integrate comprehensive fraud prevention solutions into your strategy. This includes real-time detection, accurate identity verification, and detailed fraud analytics. Such integration enables your team to respond swiftly to potential fraud while maintaining a seamless customer experience.

By adopting these approaches, businesses can significantly reduce the risk of first-party fraud, ensuring both security and trust in their operations.

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About the author
Eduardo Lopez
Head of Marketing