Understanding First-Party Fraud
First-party fraud occurs when an individual deliberately defrauds a business or financial institution by misrepresenting information or falsely disputing transactions for financial gain. Unlike third-party fraud, where someone's identity is stolen, this type of fraud involves individuals misusing their own accounts or providing false information for financial gain.
Less commonly, first-party fraud may be the result of:
Transaction confusion, where consumers fail to recognize or remember a specific transaction
Additional cardholders or family members who make purchases without the primary cardholder’s knowledge
Common First-Party Fraud Schemes
Financial institutions face an array of first-party fraud tactics, each with distinct characteristics and challenges. Generally, the most prevalent forms seek to exploit different vulnerabilities across the customer relationship lifecycle.
Application Fraud
Application fraud represents a substantial portion of first-party fraud schemes. It occurs when individuals deliberately provide false information on credit (or non-credit) applications. For example, an individual may inflate their income on a mortgage application to borrow more money. Other examples include those who open accounts to commit financial crimes or accrue debts with no intent to repay them. Application fraud corrupts risk models and leads to inappropriate credit approvals that affect a lender’s loan or credit portfolio performance.
(Source: credolab)
Chargeback Fraud
Also known as friendly fraud, chargeback fraud happens when customers dispute legitimate transactions and falsely claim they never received goods or services. MasterCard predicts that global chargeback volume will soar to 337 million by 2026, with around three-quarters of these fraudulent. Aside from claims the item was never received, others may assert they never authorized the transaction or that the item was defective.
Bust-Out Fraud
In this scheme, first-party fraudsters exhibit good credit behavior initially but eventually max out their accounts and disappear. Bust-out fraud often involves synthetic identities and is difficult to detect because of the rapidity of the deception.
Sleeper Fraud
Sleeper fraud is a derivation of bust-out fraud where the bad actor maintains their accounts for extended periods before the fraud is committed. The approach makes detection difficult since suspicious patterns only emerge after months or even years of normal behavior. In the so-called “trust period”, the individual makes purchases and pays on time before activity on the account spikes. This spike tends to involve:
Detection and Prevention Strategies
As scams increase in sophistication, financial institutions must deploy equally advanced countermeasures. Effective prevention combines technology, process controls, and human expertise in a layered approach.
Identity Verification
Robust identity verification serves as an important first line of defense against first-party fraud. Financial services providers must implement multi-factor authentication (MFA), document verification and biometric checks to validate customer identities in the application process.
Predictive Analytics and Behavioral Profiling
Robust data analysis can identify fraudulent patterns by examining thousands of data points across transaction history, application details, and various behavioral indicators. Some solutions analyze over 500 such indicators to discern genuine users from human/non-human impostors. Analyzed patterns include:
Cognitive factors (e.g., device interaction, usage preferences, and hand-eye coordination)
Physiological factors (e.g., arm size, muscle usage, and left/right handedness)
Contextual factors related to transactions, networks, devices, and site or app navigation
Cross-channel pattern recognition also helps identify connections between seemingly unrelated activities that, when viewed holistically, can reveal calculated fraud attempts. CommBank’s recent partnership with Telstra has improved the bank’s ability to detect fraudulent accounts by 25% by analyzing mobile phone usage patterns as they relate to bank fraud.
Transaction Monitoring
Monitoring for application anomalies helps identify suspicious intent early. Namely, inflated or non-existent income, unverifiable employment, or identical data points used across multiple applications. Using data enrichment tools to cross-check submitted information improves accuracy and reduces false positives. It also helps finance teams identify fraud risk before credit or vendor access is approved.
Emerging Trends in First-Party Fraud
The fraud landscape continues to evolve as both the perpetrators and defenders adapt their approaches in a metaphorical arms race. With the above in mind, here are some notable developments that warrant close attention from financial security professionals and indicate where future vulnerabilities may arise.
Synthetic Identities
Traditional first-party fraud tended to involve individuals using their own identities to access credit, services, or benefits. However, an evolving threat combines elements of first and third-party fraud via synthetic identities. These are fictional personas based on a combination of real and fabricated data. Unlike traditional identity theft, blended identities are cultivated over extended periods and afford more anonymity to the perpetrator. It may also be easier to build trust over time since clean credit profiles are less suspicious. In addition, note that the fraudster controls the entire assumed identity and there is no victim to monitor the account or report suspicious activity.
Digital Channel Exploitation
As financial services migrate to digital platforms, first-party fraudsters increasingly exploit online vulnerabilities where human oversight is minimal. Chargebacks on goods and services are one example. Consumers would have once contacted the merchant if they had an issue with their order. Now, they bypass the merchant entirely and ask card issuers to solve the problem (even if the merchant has an attractive return or refund policy). Digital enrollment shopping is another worrying trend. This tactic involves the bad actor applying for multiple credit card products or financial services in a short period to identify entities with lax approval processes.
Generational Differences
One report found that 42% of Gen Z consumers admit to engaging in first-party fraud, compared to just 22% of millennials and 5% of baby boomers. The reasons for this are multifaceted:
Online presence: Gen Z is the most online generation, and the relative anonymity of the internet makes it easier to commit fraud.
Cost of living pressures: When money is short, many consumers see first-party fraud as less about deceit and more about survival.
Generational philosophy: Some individuals are happy to exploit profitable companies and see first-party fraud as a “hack” that doesn’t hurt anyone. This may be based on unfavorable views of capitalism.
Democratization of fraud: A new wave of social media influencers now teach individuals how to commit fraud, whether it be credit card abuse, travel loyalty point scams, or how to exploit food delivery service discounts. This trend has normalized fraud on platforms where Gen Z consumers spend their time.
In Summary
First-party fraud occurs when an individual uses their identity to deceive a business or financial institution for personal benefit. In some cases, it may also involve manipulated or synthetic identities.
Common types of first-party fraud include application fraud, chargeback fraud, bust-out fraud, and sleeper fraud—where the individual exhibits exemplary credit behavior for months or even years before they max out their accounts and disappear.
First-party fraud prevention requires a multi-faceted approach that combines robust identity verification, advanced analytics, and cross-industry intelligence sharing. However, addressing first-party fraud in younger consumers is more problematic.
