Guide to Loan Fraud: Business Risks for Lenders

Lending money always carries risks, but when fraudsters enter the equation, those risks multiply. From synthetic identities to loan stacking and application fraud, bad actors continuously evolve their tactics to exploit vulnerabilities in the lending process.

In this article, we’ll break down the most common types of loan fraud, how they impact lenders, and, most importantly, how to protect your business from financial losses and reputational damage.

Let’s take a closer look at how fraudsters operate — and how to stop them.

Key Takeaways

• Loan fraud is evolving fast: Fraudsters use tactics like synthetic identities, stolen credentials and loan stacking to exploit weak verification processes.
• Early detection is critical. Recognizing red flags such as inconsistent personal data, multiple applications or unusual device activity can prevent major losses.
• Digital footprint analysis and device intelligence are game changers: These modern tools analyze online behavior and device data to uncover hidden risks and validate borrower legitimacy.
• Loan fraud impacts more than finances: Beyond monetary losses, it can harm reputation, increase regulatory scrutiny and erode customer trust.
• Proactive fraud prevention works: Combining real-time data analysis, machine learning and identity verification helps lenders stop fraud before it happens.

What Is Loan Fraud?

Loan fraud, also known as lending fraud, refers to any deceptive action aimed at gaining a financial advantage during the loan process. It can take many forms, affecting both lenders and borrowers alike.

Common examples include mortgage fraud, payday loan scams, account takeovers (ATO) in online lending, and application misrepresentation. In all cases, one party suffers a financial loss while the fraudster profits and disappears. The issue remains significant in the United States, where Cotality’s National Mortgage Application Fraud Risk Index reported a 7.3% year-over-year increase in Q1 2025, with transaction-related risks, such as hidden sales concessions and rapid property flipping, showing the sharpest rise. Online lenders face even higher exposure as they prioritize fast, frictionless approvals to attract customers.

It’s worth noting that the roles of victim and perpetrator can vary. In some instances, creditors engage in deceptive practices, while in others, borrowers manipulate the system for personal gain.

How Does Lending Fraud Work?

All lending fraud relies on deception. At some stage in the lending process, someone is pretending to be someone they are not. Understanding how each step unfolds helps lenders spot warning signs early and strengthen defenses.

1. Identity theft or false representation: Fraud starts when someone assumes another identity, either a stolen real one or a synthetic blend of real and fabricated details. These hybrid profiles often slip past basic checks because individual data points appear valid, even though they don’t match a real person.
2. Creating false documentation: To make the fake identity believable, fraudsters forge or alter documents such as IDs, utility bills, pay slips or business papers. The goal is to satisfy both automated and manual reviews with convincing but counterfeit details.
3. Application submission: The fraudster submits a loan application using a fabricated identity and forged documentation. They often rely on VPNs, burner emails or bots to scale attacks and conceal their location or connection patterns.
4. Loan approval and disbursement: If the lender’s checks fail, the loan is approved, and funds are disbursed — typically to prepaid cards or mule accounts. Fraudsters then move money quickly through several layers to obscure its final destination.
5. Fraud completion and post‑fraud laundering: After securing the funds, criminals may cash out immediately or launder money through multiple transfers and purchases. Some repay smaller loans first to build trust, then default on a much larger one.

What Are the Types of Loan Fraud?

Let’s have a look at those types of lending fraud that are on the rise.

There are various types of lending fraud that financial firms need to look out for, as the fraud landscape continues to evolve. Let’s look at some common types of loan fraud.

1. Mortgage Fraud

Mortgage fraud is a form of first-party fraud where the borrower provides false information or misrepresents their financial position in order to obtain a mortgage. There are various types of mortgage fraud:

• Occupancy fraud is where the borrower purchases an investment property with the intention of renting it out, but claims they will live in the property or use it as a second home. This can result in them obtaining a lower interest rate for their mortgage.
• Employment fraud can involve misrepresenting employment status.
• Income fraud involves providing exaggerated salary details to obtain a larger mortgage.

The omission of information, such as failing to disclose liabilities, is also considered as mortgage fraud.

2. Payday Loan Fraud

Payday loans are short-term, high-interest loans provided by companies that need to minimize friction as part of their business model. Payday loan fraud occurs when criminals exploit the minimal friction to obtain loans, then disappear with their gains.

3. First-Party Loan Fraud

First-party loan fraud scheme or personal loan fraud occurs when an applicant intentionally provides false information — such as exaggerated income — to obtain credit they wouldn’t otherwise qualify for. Because these cases often appear as credit defaults, lenders may underestimate their true fraud-related losses.

4. Second-Party Loan Fraud 

In this scheme, an individual willingly provides their personal details to someone else to commit fraud. The accomplice may be a friend or family member — or in some cases, the borrower may be unaware their details are being misused. Since the provided information is often legitimate, this type of fraud can be difficult to detect.

5. Third-Party Loan Fraud 

Third-party loan fraud occurs when criminals use either stolen or partially fabricated identities to secure credit or loans they never intend to repay. Both tactics — identity theft and synthetic identity creation — fall under the same umbrella of deception but differ in how the false identity is built.

This type of fraud is particularly challenging for digital lenders that prioritize seamless, low-friction onboarding. With fewer manual checks, synthetic identities can slip through undetected until significant losses occur. According to McKinsey, synthetic identity fraud now accounts for 10–15% of lender losses each year.

6. Loan Stacking 

Fraudsters exploit delays in credit reporting by applying for multiple loans in a short period before lenders can detect overlapping applications. This tactic is particularly damaging for microlenders, fintech startups, and digital-first lenders.

To combat these evolving threats, lenders need robust fraud detection tools that go beyond traditional credit checks, leveraging real-time data to identify risky applicants before they cause financial losses.

How Dangerous Is Loan Fraud?

Loan fraud presents a danger to individuals and businesses. For individuals whose details are stolen and used by fraudsters, the experience can ruin their credit rating and prove immensely stressful. A poor credit rating can impact a person’s life in myriad and major ways, from preventing them from obtaining a mortgage to ruining their chances of running their own business.

Loan fraud can result in financial and reputational damage for lending businesses. It costs lenders not only in loans that aren’t paid back but also in the time taken to investigate fraudulent loans, report losses, liaise with regulators, and more.

Red Flags for Loan Fraud for Businesses

Businesses can also be targets of lending fraud. Recognizing early warning signs helps lenders and compliance teams act before fraud escalates.

• Inconsistent or unverifiable personal information: Conflicting or incomplete data, such as mismatched addresses, phone numbers or tax IDs, can signal falsified or synthetic identities. Repeated verification failures or frequent resubmissions are strong indicators of synthetic identity fraud.
• Multiple loan applications in a short period: Several loan submissions across different lenders or platforms within a short timeframe may indicate an attempt to secure multiple approvals before fraud detection systems can react.
• Unusually high income claims: Income or revenue figures that appear inflated compared to company size, industry averages or historical performance often point to falsified financial documents or exaggerated business activity.
• Falsified employment or business information: Unverifiable employer details, fake business registrations or inconsistencies between stated operations and the company’s online presence (such as missing websites or inactive social channels) can reveal intentional misrepresentation.
• Unusual device or location patterns: Applications submitted from unexpected geographic regions, reused IP addresses or devices previously linked to fraud indicate attempts to conceal the applicant’s true origin or automate multiple submissions.
• Rapid post-approval activity: Immediate withdrawals, wire transfers or sudden requests to modify repayment terms after approval often signal premeditated fraud and should prompt an investigation.

How to Stop Loan Fraud

Traditional identity verification alone is no longer enough to prevent loan fraud. Fraudsters exploit weaknesses in Know Your Customer (KYC) processes by using stolen identities, synthetic profiles and disposable emails or phone numbers. To counter this, lenders should use digital footprint analysis to examine an applicant’s online presence and behavioral signals to catch suspicious behavior early.

Using Digital Footprint Analysis to Stop Loan Fraud

Digital footprint analysis evaluates dynamic data points, including email, phone and IP and device intelligence, to uncover risk indicators and assess borrower credibility. By examining how an applicant interacts across digital channels, lenders can uncover fraud risks and support alternative credit scoring. This approach is particularly valuable in underbanked regions where traditional credit history is limited.

How digital footprint analysis works:

• Email intelligence: Checks for active digital and social profiles linked to an email address. A fresh or unlinked email may indicate a fraudulent applicant.
• Phone insights: Evaluates whether a phone number is connected to messaging apps or social platforms, revealing whether it belongs to a real user or a disposable VoIP line.
• IP & device tracking: Detects VPNs, proxies and other masking tools used to hide true locations, helping identify potential fraud early.

Digital footprint analysis delivers several key benefits:

• Stronger fraud prevention through real-time identity assessment
• Reduced KYC costs by pre-screening applicants before document verification
• More inclusive lending by supporting alternative credit scoring, particularly in underbanked regions

More Sources of Insight: Device Intelligence

Device intelligence complements digital footprint analysis by focusing on the devices applicants use during the loan process. It analyzes device attributes and behavioral signals to detect suspicious activity and link accounts tied to fraudulent behavior.

• Device identification: Determines whether a device is new, reused or linked to past fraudulent accounts.
• Detecting VPNs or emulators: Flags applicants attempting to hide behind anonymization tools or fake environments.
• Device fingerprinting: Creates a unique identifier based on hardware and software traits to track repeat fraud attempts.
• Device-link analysis: Connects multiple applications or accounts that share the same device, revealing potential fraud rings.

This approach has proven effective in real-world lending. FairMoney, a digital bank serving Nigeria’s vast unbanked population, integrated SEON’s digital footprint analysis and device intelligence to assess applicants beyond traditional credit scores. By verifying digital presence, they successfully filtered out fraudsters while approving more legitimate borrowers.

Together, digital footprint analysis and device intelligence give lenders a scalable, data-driven defense against modern loan fraud — balancing robust protection with smoother, more efficient onboarding.

Online Loan Fraud Trends in 2025

Allied Market Research projects that the global digital lending market will grow reach $71.8 billion by 2032. The market has already grown immensely, driven by the digital revolution and then shifts in lending patterns for small and medium businesses (SMBs) during the pandemic.

As the online lending market has evolved, so have loan fraud trends. Some of the latest trends to watch include:

Synthetic ID Fraud Keeps Growing

According to the Federal Reserve, synthetic ID fraud is the fastest-growing type of fraud in the US, accounting for billions of losses annually. This trend is particularly challenging in the US, which relies heavily on static personally identifiable information, such as Social Security numbers.

Government-Backed Business Loan Fraud Will Continue

Business loan fraud became a growing problem following the wave of government emergency loans issued during the pandemic. Continuing economic woes in many countries make this an ongoing risk. 

Digital Customer Onboarding Must Evolve

Deepfakes, massive data breaches biometrics hacking — fraudsters have no shortage of modern techniques designed to beat KYC checks. They will continue to embrace new methods and technologies, so digital customer onboarding needs to evolve to account for this.

SEON’s Prevention Against Loan Fraud Risks

SEON’s comprehensive fraud detection suite integrates digital footprint analysis, device intelligence, machine learning and customizable risk scoring to effectively prevent and detect loan fraud. By examining applicants’ online behaviors and device interactions, SEON identifies suspicious activities and evolving fraud patterns in real time, enabling lenders to mitigate risks proactively. The platform’s machine learning capabilities adapt to emerging threats, enhancing detection accuracy over time. 

Additionally, customizable risk scoring allows lenders to tailor fraud detection criteria according to their specific requirements, ensuring a balance between security and user experience. Real-time monitoring further strengthens fraud prevention efforts by providing immediate insights into potential fraudulent actions, safeguarding both lenders and borrowers throughout the lending process.

Sources:

• Banking Exchange: COVID-19 and Synthetic Identity Fraud: The Importance of Lenders Mitigating Risks
• BusinessWire: Federal Reserve Releases Synthetic Identity Fraud Mitigation Toolkit to Educate, Fight Fraud
• Credit Connect: Bank account and loan fraud soars in pandemic
• PR Newswire: The Digital Lending Market is expected to register a CAGR of approximately 11.9% during the forecast period (2020 – 2025)
• Financial Times: Small UK businesses consider insolvency to escape state Covid loans

Frequently Asked Questions