First-Party Fraud in Banking: The Hidden Threat in Plain Sight

What Is First-Party Fraud?

First-party fraud in banking is an increasingly critical issue that occurs when real customers use their own identity to commit fraudulent acts. It is a major contributor to financial losses, particularly in U.S. institutions. First-party fraud happens when a legitimate customer uses their own identity to deceive a financial institution for personal gain. Unlike third-party fraud, which involves stolen credentials, first-party fraud is conducted by people who appear trustworthy at first. Key examples include:

• Bust-out fraud: Making small payments to build trust, then maxing out credit lines before disappearing.
• Chargeback fraud (aka friendly fraud): Disputing valid purchases to get refunds while keeping the product or service.
• Loan stacking: Applying for multiple loans across institutions at once without disclosing existing obligations.
• Application fraud: Falsifying income, employment, or identity information.
• Credit washing: Claiming identity theft to erase legitimate debts from credit reports.

First-party fraud shares similarities with account takeover fraud in terms of detection complexity, but the origin and intent are quite different.

Including first-party fraud in banking in enterprise fraud detection programs is critical to addressing this evolving risk.

Why First-Party Fraud in Banking Is Growing

The rise of first-party fraud in the U.S. is driven by several converging trends:

• Economic hardship is pushing more individuals to rationalize fraudulent behavior.
• Digital onboarding and fintech platforms have reduced in-person verification, making deception easier.
• Underreporting: Many institutions categorize these losses as credit risk, not fraud, hiding the true scale.
• Cultural normalization: Over 35% of U.S. adults admit to engaging in or knowing someone who has committed first-party fraud.
• Technological accessibility: Fraud-as-a-service offerings and AI-generated documents have lowered the barrier for individuals to commit this type of fraud at scale.

The Scope and Impact of First-Party Fraud

Recent estimates suggest:

• $100 billion+ in annual losses across U.S. financial institutions.
• 40% of card fraud claims may stem from friendly fraud.
• 10–20% of credit losses might actually be hidden fraud.
• Synthetic identity fraud, a related issue, cost U.S. lenders $35 billion in 2023 alone. Learn more about synthetic identity fraud here.

First-party fraud undermines credit risk models, inflates delinquency rates, and increases operational costs for banks and fintechs. Its hidden nature makes it harder to tackle through conventional means.

New Tools & Tech

Financial institutions are using a range of advanced tools and platforms:

• Behavioral biometrics: Companies like BioCatch monitor typing patterns, navigation behavior, and device use to flag suspicious activity.
• Machine learning: ML models evaluate a variety of features (e.g., document submission speed, IP address consistency) to identify fraud attempts.
• Document verification: Tools like Inscribe catch manipulated bank statements and paystubs using AI-driven image forensics.
• Consortium data sharing: Socure, Early Warning Services, and others pool risk signals across institutions to catch loan stackers or synthetic IDs.
• Alternative data sources: Verification via payroll APIs, phone/email reputation tools, and device fingerprinting help vet authenticity.

Academic and Industry Research on First-Party Fraud in Banking

Academic and private research efforts are bringing forward novel techniques:

• Graph analytics: Using network connections (shared devices, IPs, addresses) to detect collusion and synthetic identities.
• Semi-supervised and anomaly detection models: Designed to detect new fraud types without prior labels.
• Generative AI countermeasures: Institutions are testing image analysis tools to detect deepfakes and AI-generated IDs.

Reports by the Federal Reserve and SAS Institute stress the growing role of behavioral and multi-source analysis in distinguishing bad actors from legitimate users.

Strategies to Combat First-Party Fraud in Banking

To address first-party fraud, institutions need layered, proactive strategies:

1. Improve verification at onboarding using real-time data, behavioral cues, and document analysis.
2. Categorize fraud separately from credit loss to understand true risk exposure.
3. Join consortiums and share fraud data signals with peers.
4. Combine AI with human review to catch nuanced cases.
5. Foster internal collaboration between fraud, credit, underwriting, and collections teams.
6. Educate customers on the seriousness of friendly fraud and include consequences in policy language.
7. Establish feedback loops between collections and fraud teams to detect bust-outs and dispute abuse earlier.

First-party fraud in banking demands attention across all stages of the customer lifecycle, from onboarding to collections. First-party fraud is no longer a niche issue – it’s a mainstream threat costing billions annually. It thrives in the gray space between legitimate customer behavior and malicious intent, making detection difficult with traditional tools. Fortunately, with AI, consortium networks, behavioral analytics, and better reporting standards, the financial industry is better equipped than ever to identify and prevent it.

As fraudsters evolve, so must banks. Addressing first-party fraud requires both cutting-edge technology and a collaborative industry mindset—one that views fraud risk as a shared challenge rather than a siloed issue.

Explore more through Socure, BioCatch, Inscribe, and recent insights from the Federal Reserve and SAS.