Banks, credit unions, and other financial services companies prioritize convenience over almost everything else. Because technology has unlocked so many unique ways to deliver relevant, engaging customer experiences, financial institutions are rapidly offering their services in the form of new digital products that are both instantaneous and accessible 24/7. Whether applying for mortgage loans, car loans, or credit cards, consumers seek swift and uncomplicated application and approval processes. This demand for convenience has prompted the majority of banks to offer digital lending services featuring rapid approval and digital onboarding.

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While digital financial services certainly cater to consumer convenience, they simultaneously open avenues for fraudulent activities on a large scale. Fraudsters employ a wide variety of technology-based and behavioral tactics to orchestrate extensive fraudulent application attacks against financial institutions during the application process. These attackers can generate hundreds, and often thousands, of applications simultaneously through digital channels.

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For more information about how you can eliminate application fraud while enhancing your customer experience, contact us to speak with an expert.

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We know that fraudsters employ advanced tools and techniques to mimic the behavior of legitimate borrowers, rendering their actions increasingly challenging to identify and thwart. As a result, preventing fraudsters from gaining access to your organization and its data, processes, and resources is a top priority for financial services organizations. To fortify their environments, they employ a variety of tactics and controls to swiftly, flexibly, and efficiently eliminate fraud attacks before they can do damage to customers and potentially put the organization in a bad light. 

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Detecting fraud in its early stages demands a vigilant enterprise-wide approach, backed by a deep understanding of an individual's behavior. Effective identity verification can facilitate the reciprocal exchange of intelligence, enabling insights gained at the point of application to inform subsequent strategies for managing your member base, identifying transactional fraud, and prioritizing debt collection efforts.

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Data teams know that knowledge acquired downstream, such as data on compromised identities and charge-off details, can offer invaluable input for upstream decisions, such as tackling application fraud and creating a continuous feedback loop. However, the question arises: where within your fraud prevention measures should you incorporate post-booking controls? And how can you collaborate more effectively with risk partners to minimize losses?
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What is Credit and Loan Application Fraud?

As more businesses shift towards online application processes, they find themselves increasingly susceptible to application fraud. Application fraud occurs when an applicant submits inaccurate information to a creditor, property manager, or any entity relying on personal data for customer approval. Such deceptive practices encompass:
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• Falsifying employment history
• Inflating income
• Providing counterfeit identification documents
• Misrepresenting credit history

Individuals engaging in application fraud might seek credit cards, loans, or other credit products using fictitious identities or supplying false details about their financial status, employment, or other pertinent information. This deceptive behavior can lead to the acquisition of credit or loans for which they are ineligible or secure credit under favorable terms based on fraudulent information.

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“While a surge in new user acquisition may deliver great bottom-line numbers, if these users turn out to be application fraudsters, they can do irreparable damage…”

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Much like other identity-based attack types, application fraud aims to manipulate legitimate and fabricated information. However, a significant challenge with application fraud lies in the rapid acceptance of online applications by financial companies. The metric of new user acquisition is often a key indicator used to validate growth, encouraging a more lenient approach to fraud risk management. However, this leniency during the application acceptance stage typically proves to be a bad strategy. While a surge in new user acquisition may deliver great bottom-line numbers, if these users turn out to be application fraudsters, they can do irreparable damage.

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Too many financial services companies view this as a trade-off between fast onboarding vs. fraud mitigation. As we’ll see, it doesn’t have to be a choice.
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How Fraudsters Commit Credit and Loan Application Fraud 

As we’ve seen, fraudsters exploit systems and processes by completing applications under someone else's identity or with fabricated identities, evading detection by the organization or individual. If the fraudster possesses sufficient information about the victim, the system fails to discern the true identity of the applicant. Subsequently, the scammer can exploit the credit line to its maximum and vanish, a tactic known as bust-out fraud.

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Fraudsters are astute about what types of information tend to get flagged, so they operate with workarounds via technical maneuvers to exploit vulnerabilities in digital systems. One common tactic involves manipulating personally identifiable information (PII) into a variety of components; they may use some details about themselves or others that are accurate, but combine that information with falsified data. Or, they may tweak PII with minor adjustments so it might pass thresholds that might be more lenient. By meticulously collecting and falsifying data, criminals create a facade of legitimacy during the application process, seamlessly slipping through the initial layers of scrutiny.

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These fraudsters often capitalize on the demand for swift application processes. Leveraging automation tools such as bots, fraudsters orchestrate large-scale attacks on financial institutions. They inundate the system with a barrage of applications, overwhelming the defenses in place. This inundation not only diverts attention but also complicates the task of distinguishing fraudulent applications from legitimate ones. By the time the company identifies the occurrence of application fraud, the fraudster has already bailed. 

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A sophisticated component of application fraud involves the emulation of genuine user behavior. Fraudsters meticulously study the patterns of legitimate borrowers, deploying advanced tools and techniques to replicate their interactions with the system. This behavioral manipulation, coupled with their use of fabricated data, creates a scenario where fraudulent activities blend seamlessly into the normal flow, eluding conventional detection methods.

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The phenomenon of "bust-out fraud" adds another layer of complexity to the landscape. In this scenario, fraudsters successfully attain credit or loans by assuming false identities or manipulating their financial standing. By the time the organization discerns the deception, the fraudster has vanished, leaving behind a trail of unauthorized credit usage.

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These tactics by themselves may not seem incredibly complex. However, the orchestration of multiple elements of these fraudulent efforts adds layers of necessary scrutiny for fraud prevention measures, and it underscores the evolving challenges faced by institutions in safeguarding their application processes. Anticipating the ingenuity of fraudsters drives the need for continuous innovation in threat detection and mitigation strategies within the continuously changing nature of application fraud.
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How to Identify and Eradicate Credit and Loan Application Fraud

To effectively identify application fraud, banks should apply deep visibility into a range of critical indicators and implement robust fraud detection mechanisms. Here are key aspects and indicators that banks should closely monitor:
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• Behavioral Analysis: Analyze the behavioral patterns of applicants during the application process. Sudden deviations from typical behavior could indicate fraudulent activity.
• IP Address and Device Analysis: Monitor IP addresses and devices used during the application. Multiple applications from a single IP or suspicious devices can be red flags for potential scams.
• Use of Proxy Servers or VPNs: Identify the use of proxy servers or Virtual Private Networks (VPNs) which could be an attempt to hide the true identity or location of the applicant.
• Employment and Income Verification: Validate the employment and income information provided by the applicant to ensure accuracy and legitimacy.
• Application Velocity: Monitor the speed and frequency of applications. Rapid and repeated applications using the same or similar details may indicate fraudulent activity.
• Negative files: Check applicant details against internal and external negative files and lists of known fraudsters or suspicious individuals.
• Machine Learning Models: Develop and deploy machine learning models trained on historical fraud data to predict and flag potential fraudulent applications.

As we’ll see in the next section, there is a considerable advantage provided by using Prove’s Phone-Centric Identity™ to drive digital identity verification. This approach relies on phone intelligence – or mobile intelligence – because it delivers a robust set of phone and mobile signals that can be analyzed and synthesized to provide insight for the purposes of identity verification, identity authentication, and fraud prevention. Examples of telecom signals that feed phone intelligence include:
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• Phone line tenure
• Phone behavior such as calls, texts, logins, ad views
• Phone line change events as ports, snap-backs, win-backs, true disconnects, phone number changes
• Phone number account takeovers such as SIM swaps
• Velocity and behavior of change events

Let’s learn more about this approach. 

The Role of Phone-Centric Identity Verification in Combatting Application Fraud

Digital identity verification plays a distinct role for banks and financial institutions in identifying and eliminating application fraud. By identifying and validating individuals as customers, these organizations can identify fraudulent actors before they have the opportunity to enter and operate within a bank’s systems. Prove uses a phone-centric approach to deliver the most accurate identity verification results, and it’s helping our financial services and banking customers achieve remarkable results.

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By combining persistent orchestration of identity verification data and possession-based verification of personal phones and other devices, banks can create layered defenses that significantly heighten the complexity of fraudsters attempting to gain access to systems. This multifaceted approach enhances the reliability of identity validation, making it considerably more challenging for adversaries to impersonate legitimate users.

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Prove’s Phone-Centric Identity™ employs real-time data from authoritative sources, comprising billions of signals, to emerge as a potent proxy for digital identity and trust. Why does this work so effectively? Well, the ubiquity of mobile phones, coupled with the duration and frequency of their use by individuals, underscores the high correlation between Phone-Centric Identity™ signals and the notions of identity and trustworthiness.

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Profiles characterized by greater depth (the historical extent of data) and consistency (the frequency of encountering the same data) exhibit a diminished risk of fraudulent activity. Phone-Centric Identity™ signals encompass a spectrum of factors, including phone line tenure, behavior metrics like calls, texts, logins, and ad views, as well as events such as phone line changes (ports, snap-backs, true disconnects, and number changes), phone number account takeovers (e.g., SIM swaps), and the velocity and pattern of change events. These signals manifest both high depth and high consistency.

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For example, Phone-Centric Identity™ signals for a given consumer typically span many years (high-depth), reflecting the trend of individuals acquiring phone accounts at a young age. In terms of consistency, these signals offer a robust perspective on identifying potentially suspicious behavior by assessing deviations from regular consumer activity.

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This starkly contrasts with traditional identifiers like social security numbers or passwords, which can be easily procured on the dark web and exploited by hackers to infiltrate consumer accounts. Breaking Phone-Centric Identity™-based verification and authentication necessitates a fraudster to acquire a phone in the victim's name, sustain its usage over years, and replicate the victim's behavior through calls and app logins. While theoretically possible, such a method lacks scalability and is not deemed worthwhile by most criminals.

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Maybe the key element to understanding how banks and financial institutions can reduce credit and loan application fraud with Phone-Centric Identity™ is to understand how this approach uniquely employs the mobile device as a "what you have" factor. This enables companies to definitively ascertain whether they are engaging with their legitimate customer. This validation, often termed a "possession" check, yields a binary result rather than a probabilistic score. By discerning whether a consumer is physically in possession of their mobile device, Phone-Centric Identity™ technology provides a clear yes or no response regarding the legitimacy of the interaction between a company and its customer.
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Conclusion

Credit and loan application fraud is on the rise, but by recognizing the patterns used to perpetrate it, fraud teams can use the strategies outlined in this blog to detect and eradicate it. By implementing these measures, you can confidently expand your user base without concerns about the infiltration of this type of fraud and cybercrime into your systems.

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‍For more information about how you can eliminate application fraud while enhancing your customer experience, contact us to speak with an expert.