1st Party Fraud

What Is First Party Fraud?

1st party fraud occurs when a legitimate customer misrepresents themselves to obtain credit, goods, or services without intending to repay. Unlike identity theft or third-party fraud, where a criminal impersonates someone else, in first-party fraud the perpetrator is the legitimate customer – using their own name, account, or identity documents.

Typical examples include exaggerating income on a loan application, disputing valid charges to trigger chargebacks, or taking out credit with no intent of repayment. For lenders and digital platforms, this type of fraud is especially difficult to detect because it originates from a real customer, often with seemingly legitimate credentials.

Why First Party Fraud Matters in Finance

For banks, BNPL providers, microfinance organizations, and digital lenders, 1st party fraud represents a hidden but systemic risk. Industry studies show it can account for a significant percentage of credit losses, often masked as “bad debt” rather than fraud. This misclassification inflates non-performing loans and complicates accurate risk reporting.

With the rise of digital lending, real-time credit approvals, and e-commerce financing, opportunities for such abuse have grown. In BNPL, for example, consumers may intentionally overextend across multiple providers, knowing repayment is unlikely. In microfinance, overstated household income or misrepresented collateral can have cascading effects – not only on individual lenders but on broader financial inclusion efforts.

How 1st Party Fraud Works

First-party fraud often starts with misrepresentation during onboarding or application:

• False income or employment information on loan or credit applications.

• Using legitimate accounts to make purchases and later initiating chargeback abuse (also called “friendly fraud”).

• Opening multiple accounts with the same institution to exploit promotions or evade repayment limits.

• Applying for products with no intention to honor contractual repayment.

Because these cases involve authentic identities and valid KYC documents, they slip past traditional fraud filters. Institutions often detect them only after default or dispute, making them costly and hard to remediate.

Real-World Impact of First Party Fraud

• Banking and lending: Defaults classified as “credit losses” reduce profitability and distort risk models.
• BNPL and e-commerce: Chargeback abuse and repayment evasion increase operational costs and may trigger regulatory scrutiny.
• Microfinance: Fraudulent applications erode trust and can destabilize community lending ecosystems designed for financial inclusion.

According to industry estimates, 1st party fraud is one of the leading types of fraud worldwide. Mastercard estimated that the annual cost of first party fraud is approximately $50 billion. It represented 36% of all reported fraud in 2024 – this figure will only continue to grow as more financial activity shifts online.

How to Detect and Prevent First Party Fraud

Traditional fraud detection systems often fall short because they rely on stolen identity signals. Preventing first-party fraud requires a multi-layered, intelligence-driven approach:

• Device Intelligence: Evaluating whether the device itself shows risk signals such as emulators, rooted devices, or use of anonymization tools. JuicyScore’s device intelligence helps flag hidden risks even when personal data looks legitimate.

• Alternative Data and Behavioral Analytics: Assessing patterns like repayment consistency across multiple providers, browsing behavior, or device/account history can reveal intent mismatches not visible in credit bureau data.

• Fraud Scoring Models: Assigning a risk score to every application or transaction allows lenders to weigh the likelihood of first-party fraud in real time.

• Cross-institution Data: Identifying repeated behavior across lenders – such as simultaneous BNPL applications – helps reveal abuse that would otherwise be invisible in silos.

For institutions aiming to maintain growth while controlling fraud losses, the ability to distinguish credit risk from fraud risk is essential.