What is first-party fraud? 

Common First-Party Fraud Schemes 

Financial institutions face an array of first-party fraud tactics, and each has distinct characteristics and challenges. In general, 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.

No matter the context, application fraud corrupts risk models and leads to inappropriate credit approvals that affect a lender’s loan or credit portfolio performance.

The key differences between first and third-party fraud. As we’ll discuss later, the line between the two is becoming increasingly blurred.

(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 authorised the transaction or that the item was defective.

Bust-out fraud

In this scheme, first-party fraudsters exhibit good credit behaviour initially. But at some point, they 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. We’ll delve more into the use of synthetic identities below.

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 behaviour.

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:

• Maxing out credit limits.

• Taking out large loans, and
• Purchasing high-value items with no intention of repayment.

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 defence 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 behavioural profiling

Robust data analysis can also identify fraudulent patterns by examining thousands of data points across transaction history, application details and various behavioural indicators.

Some solutions analyse over 500 such indicators to discern genuine users from human/non-human impostors.

Analysed 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), and
• 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%. This was achieved by analysing 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 unfavourable views of capitalism.
• Democratisation 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 normalised 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 behaviour 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.