Security protocols around online digital-payments infrastructure continue to advance and strengthen. As a result of more sophisticated guardrails, financial cybercriminals are shifting their efforts towards a slightly more penetrable landscape: application fraud and identity theft. Compared to transactional fraud, online application- and identity-related fraud can pose greater risk to financial institutions (FIs) and to their customers.
In this deep-dive into the next key influencer in payments this year, Visa payments advisors share their expert insights on the rapidly evolving online-fraud landscape, overview various risk typologies and outline tactical risk-mitigation considerations for FIs.
Due to several factors—like market maturity, different levels of model availability globally, and minimal recourse to recoveries—application fraud and identity theft are on the rise. Application fraud involves the use of stolen credentials or false information by “threat actors” to apply for or enroll in financial services.
Threat actors typically approach FIs applying for credit, which contributes to an increase in identity theft events, because obtaining credit allows them to access funds and valuable resources under false identities. This exploitation of credit systems enables fraudsters to make unauthorized purchases, withdraw cash, or commit other financial crimes before the fraud is detected. Manifesting itself in the form of either first-party or third-party fraud, the threat from application fraud is expected to grow rapidly, particularly as the payments-industry continues implementing and embracing digital-acquisition models.
Surveying cybercriminal tactics: application fraud, identity theft, and online data scams
Why the surge in application fraud and identity theft is a growing concern for FIs
Three main implications for FIs:
- Increased identity theft fraud – External data breaches and compromise events are leading to a rise in identity theft fraud, often orchestrated by organized crime syndicates. This results in more fraudulent applications being submitted to FIs.
- Higher credit losses due to undetected application fraud – Many FIs are struggling to detect a substantial amount of application fraud. This undetected fraud is often absorbed and obscured as credit losses, impacting risk management efforts.
- Digital lending due diligence challenges – The rapid growth of digital lending has made it difficult for FIs to perform robust due diligence. This challenge hampers their ability to implement effective straight- through processing, making it easier for fraudulent applications to slip through the cracks.
The advent and widespread adoption of digital banking further exacerbate these risks. While digital banking platforms provide convenience and accessibility to legitimate users, they also make it easier for fraudsters to engage in identity theft, application fraud, and exploit weaknesses through online scams. This amplifies the challenges faced by financial institutions in detecting and preventing fraudulent activities. The nature of digital transactions and the vast amount of personal information accessible online often make it easier for fraudsters to commit cybercrimes with greater impact and anonymity.
The taxonomy and modus operandi (MO) of application fraud
Application fraud is a pervasive global issue affecting FIs as they expand their digital offerings and outreach. Fraudsters swiftly adapt to preventative measures, employing various malicious techniques to perpetrate application fraud. Understanding the taxonomy of application fraud is critical to effectively identify, mitigate, and prevent financial losses.
There are three main types of application fraud:
First-party fraud
Individuals or entities misrepresent their personal information as part of an application or transaction request with the intent of committing fraud.
The typical MO includes:
- Fraudulent financials
- False information
- Collateral fraud
- Employment fraud
Third-party fraud
A third party takes a victim’s identity and/or details without their knowledge, or creates a new identity using stolen credentials, with the purpose of perpetrating fraud.
The typical MO includes:
- Stolen identity
- Synthetic identity
- Mixed identity
- Fake/set up companies
Collusion fraud
Individuals allow their identities or accounts to be used for fraud, acting in collusion with criminal syndicates or employees of FIs (e.g., sales agents).
The typical MO includes:
- Employee collusion
- Syndicate fraud
- Complicit money mule
Common variations and techniques
- Income inflation and asset misrepresentation: misstating income or assets to qualify for higher credit limits or loans
- Bust-out fraud: establishing creditworthiness over time before maxing out credit limits and defaulting
- Address manipulation: providing incorrect addresses to evade detection or facilitate subsequent fraud
- Mule accounts: opening accounts using legitimate credentials but intending to launder illicit funds
Risk and impact across geographies
- Developed markets: fraudsters exploit gaps in credit history data, misrepresenting financial history to access credit
- Emerging markets: with alternative data used for underwriting (e.g., mobile payments, digital lending, etc.), fraudsters fabricate digital identities to manipulate lending models
- Regions with strong privacy regulations: in regions like Europe, institutions may struggle to verify financial claims due to limited data-sharing capabilities
Key challenges for FIs
Owing to its inherent nature, application fraud poses several distinct challenges to FIs when compared to transactional fraud. Some of them include:
How FIs can strengthen defenses against application fraud
As fraud evolves with advancements in AI, digital identity solutions, and financial technology, each of the three fraud types outlined above presents distinct challenges requiring tailored strategies for effective prevention and risk mitigation. FIs are advised to implement multi-layered defenses, fraud intelligence sharing, real-time monitoring, and other measures to prevent financial losses.
Here are examples of effective mitigation and control layers:
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Multi-layered identity verification
- Verify customer identity using government-issued ID documents.
- Utilize digital ID proofing, biometric authentication, and behavioral analytics.
- Combine biometrics, device intelligence, and machine learning (ML) driven risk scoring.
Advanced credit modeling and real-time monitoring
- Use AI and ML to detect patterns indicative of fraudulent applications.
Income validation
- Authenticate submitted income documents for legitimacy from independent sources.
Innovative technologies
- Analyze device information during the application process to identify suspicious devices or those associated with known fraudsters.
- Identify high-risk patterns such as location inconsistencies.
- Utilize ML to detect anomalies in application data and behavior.
Intelligence repository
- Cross-reference applicant information with known fraud databases and participate in industry-wide fraud prevention networks.
- Strengthen fraud intelligence sharing between financial institutions.
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Transaction monitoring and early-warning indicators
- Monitor transactions in real-time for suspicious patterns or behaviors.
- Use AI/ML algorithms to detect anomalies Identify irregular repayment patterns, address inconsistencies, and synthetic identity traits.
- Review First Payment Default (FPD) cases to determine instances of customer not found/contactable.
Behavioral analysis and device intelligence
- Analyze customer behavior patterns over time, high-risk patterns, sudden changes in spending habits or account usage.
Account activity reviews
- Authenticate submitted income documents for legitimacy from independent sources.
Continuous risk assessment
- Regularly update risk models based on new fraud trends and pattern; adjust parameters based on evolving threats.
- Conduct customer awareness programs to highlight the risks and consequences of facilitating fraud.
Understanding and learning from global best practices
Tactical controls
- Intensify new client onboarding due diligence protocols
- Augment fraud prevention controls focused on digital channels
- Perform robust identity checks using advanced digital solutions
- Augment detection capabilities (e.g., with facial biometrics, liveliness checks, and independent database validations)
Strategic controls
- Deploy a cutting-edge acquisition fraud detection platform
- Augment capabilities utilizing integrated case management, ML-based fraud risk scoring, and link analysis
- Implement advanced list management capabilities equipped with pioneering phonetic match algorithms
How Visa can help
In the face of sophisticated application fraud risks, Visa has enhanced its capabilities by integrating Featurespace’s advanced AI into its fraud prevention and risk-scoring offerings. This enhancement provides real-time detection of sophisticated fraud attacks, ensuring businesses stay safe without adding friction to the user experience.
Furthermore, VCA is ideally positioned to work with clients to assess risk vectors, potential impact, level of preparedness, and risk mitigation infrastructure, along with deployable strategies and the best methods for implementation. VCA’s capabilities encompass performing comprehensive diagnostics of the client’s current control environment, tailored to the threat landscape. This approach allows VCA to enhance its application fraud risk management strategy and framework to deliver optimal performance and future-proof risk mitigation.
Beyond advisory services, VCA provides risk solutions using a defense-in-depth model in the acquisition domain. These solutions complement the client’s existing risk-mitigation capabilities, offering proactive risk management augmented with advanced prevention and detection strategies.
About Visa Consulting and Analytics (VCA)
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¹ Entrust Cybersecurity Institute, 2025 Identity Fraud Report.
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