First-party fraud refers to instances in which an individual intentionally misrepresents their identity in exchange for goods or services. Borrowers may falsify income, misrepresent employment or exploit credit systems without the intention of repaying. In the financial services industry, it’s often miscategorized as credit loss and written off as bad debt, which masks true fraud exposure and distorts credit risk forecasting.
Types of first-party fraud
First-party fraud can take many forms and occur at different stages of the customer lifecycle, from application and account opening to transactions, repayments and disputes. Understanding the most common types of first-party fraud can help organizations recognize suspicious behaviors, identify emerging risks and strengthen their fraud prevention strategies. Common types of first-party fraud include:
- Chargeback fraud: Also known as “friendly fraud,” chargeback fraud occurs when an individual knowingly makes a purchase with their credit card and then requests a chargeback from the issuer, claiming they didn’t authorize the purchase.
- Application fraud: This takes place when an individual uses stolen or manipulated information to apply for a loan, credit card or job. First-Party fraud comprised of 29% of application fraud cases in 2025,
- Fronting: Done to get cheaper rates, this form of insurance fraud happens when a young or inexperienced individual is deliberately listed as a named driver, when they’re actually the main driver of the vehicle.
- Goods lost in transit fraud (GLIT): This occurs when an individual claims the goods they purchased online did not arrive. To put it simply, the individual is getting a refund for something they actually already received.
- Bust-out: This occurs when an individual builds what appears to be good credit behavior over time, making small purchases and on-time payments, and then suddenly maxes out their credit lines or abandons repayment entirely. The account looks legitimate until the “bust-out,” making it one of the hardest forms of first-party fraud to detect.
- Credit washing: This happens when an individual falsely disputes legitimate accounts or debts to have them removed from their credit report. By portraying valid obligations as fraudulent, the individual can temporarily improve their credit standing or access new credit they wouldn’t otherwise qualify for.
A first-party fraudster can also recruit “money mules”, individuals who are persuaded to use their own information to obtain credit or merchandise on behalf of a larger fraud ring. This type of fraud has become especially prevalent as more consumers are active online, with money mule transfers accounting for an estimated $3 billion annually in financial losses in the U.S. alone.
How it impacts your organization
Firstly, first-party fraud can cause significant losses. According to our latest study, first-party fraud costs an average of $36.7 million annually.
An imperfect first-party fraud solution can also strain relationships with good customers and hinder growth. When lenders have to interpret actions and behavior to assess customers, there’s a lot of room for error and losses. Those same losses hinder growth when misclassification inflates credit-risk estimates and masks true fraud exposure.
This type of fraud isn’t a single-time event, and it doesn’t occur at just one point in the customer lifecycle. It occurs when good customers develop fraudulent intent, when new applicants who have positive history with other lenders have recently changed circumstances or when seemingly good applicants have manipulated their identities to mask previous defaults.
Finally, misclassified first-party fraud losses can impact how your organization categorizes and manages risk, and that’s something that touches every department.
AI-assisted first-party fraud
Artificial intelligence (AI) is making first-party fraud more sophisticated, scalable and difficult to detect. AI-assisted first-party fraud accounts for 51% of AI-enabled fraud.
Fraudsters can use generative AI and other automated tools to create convincing supporting documents, manipulate application information, fabricate employment or income details and generate realistic communications that help false claims appear legitimate. AI can also enable individuals and organized fraud rings to test different identities, applications or behaviors at greater speed and scale, while adapting their tactics to avoid traditional fraud controls. Because first-party fraud often involves a real person using their own identity or a mixture of genuine and manipulated information, AI-enhanced activity can be particularly challenging to distinguish from legitimate customer behavior.
As these capabilities become more accessible, organizations may need to combine identity, device, behavioral and credit insights to identify patterns and connections that individual transactions or applications may not reveal.
Solving the first-party fraud problem
First-party fraud detection requires a shift in how we think about the problem of fraud. It starts with the ability to separate first-party fraud and credit risk, since they are often indiscernible at origination.
To effectively combat first-party fraud, businesses should consider the following actions:
- Define first-party fraud as its own risk: Don’t blend it into credit loss. Build targeted models that use behavioral, identity and activity signals. Start with first-payment default as a key indicator.
- Use a longer risk window: A 12-month view helps surface early fraud patterns and supports stronger credit and fraud analysis.
- Unify fraud, credit and compliance decisions: Coordinated strategies reduce blind spots and improve customer experience.
- Upgrade your models: Apply machine learning and segment by factors like credit age or product type to better detect bust-outs and early defaults.
- Combine credit and non-credit data: Use device intelligence, identity velocity and behavioral data to help separate fraud from financial hardship.
- Benchmark against peers: Regular comparisons help assess exposure, validate performance and refine strategies.
How we can help
The fraud problem is complex. However, by leveraging fraud risk management strategies, you can better distinguish among types of fraud and determine the best course of action going forward.
Our robust fraud management solutions can be used for synthetic identity fraud and account takeover fraud prevention, which can help you minimize customer friction to improve and deepen your relationships while preventing fraud.
Connect with us to learn more about using our identity expertise, data and analytics to improve identity resolution and detect and prevent all types of fraud.
