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.

What Is Loan Fraud?

Loan fraud, or lending fraud, refers to any deceptive action intended to gain a financial advantage during the loan process. This type of fraud can take various forms, affecting both lenders and borrowers.

Common types of loan fraud include mortgage fraud, payday fraud, account takeover (ATO) in online lending, and loan scams. In all cases, one party suffers a financial loss while the fraudster profits and vanishes. The problem is particularly concerning in the US, where an estimated 1 in 164 mortgage applications is flagged as suspicious, according to CoreLogic. Online, the risk is even greater as startups prioritize seamless and fast loan approvals to attract customers.

It’s important to recognize that the roles of victim and perpetrator vary depending on the type of fraud. In some cases, creditors engage in fraudulent practices, while in others, debtors exploit the system for personal gain.

How Does Lending Fraud Work?

All lending fraud is built on deception. At some point in the money lending process, someone is pretending to be someone they are not. 

In most cases, the debtor provides false details. They use stolen or synthetic IDs, which stitch legitimate people’s details together with made-up information. Once their loan has been approved, they disappear without repaying it. The same applies to business loans.

More sophisticated fraudulent debtors repay their loans diligently to build a better credit history. Then, they take out a huge loan and then disappear.

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.

Mortgage Fraud

Mortgage fraud is a form of first-party fraud where the borrow 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 borrow 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 concerns the provision of exaggerated salary details to obtain a larger mortgage.

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

Payday Loan Fraud

Payday loans are short-term, high-interest loans provided by companies that need to minimise friction as part of their business model. Payday loan fraud is when criminals take advantage of that minimal friction to obtain loans, then disappear into the ether with their ill-gotten gains.

First-Party Loan Fraud

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

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.

Third-Party Loan Fraud 

Fraudsters use stolen or synthetic identities to secure credit with no intent to repay. Synthetic identity fraud, in particular, is a growing issue: criminals create fake personas by blending real and fabricated data, building credit profiles over time before cashing out with large loans. This form of fraud is especially problematic in digital lending, where frictionless onboarding makes detection harder. McKinsey estimates synthetic identities account for 10-15% of lender losses annually.

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.

Using Digital Footprint Analysis to Stop Loan Fraud

Traditional identity verification alone is no longer enough to prevent loan fraud. Fraudsters exploit weaknesses in KYC (Know Your Customer) checks by using stolen identities, synthetic profiles, and burner emails or phone numbers. To combat this, lenders are turning to digital footprint analysis, which evaluates an applicant’s online presence to assess their legitimacy in real time.Unlike static PII checks, digital footprint analysis checks dynamic data points — such as email, phone and IP activity — to 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: By checking for digital and social profiles associated with the email address, it can be assessed whether an email is actively used. A fresh or unlinked email may indicate a fraudulent application.
• Phone insights:  Messaging apps and social media presence reveal whether a phone number belongs to a real user or is a disposable VoIP number.
• IP & device tracking:  Identifies users masking their location with VPNs or proxies, helping detect fraudsters early.

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.

Beyond fraud prevention, digital footprinting also reduces KYC costs by pre-screening applicants before expensive ID verification steps. It enables:

• Stronger risk profiling based on alternative data
• Lower onboarding costs by filtering out fraud early
• More inclusive lending with better decision-making for thin-file borrowers

As fraud tactics evolve, lenders must move beyond basic identity checks. Digital footprint analysis offers a scalable, real-time solution to detect risk, improve credit assessments and optimize KYC spending — all while enabling safer, more efficient lending.

More Sources of Intel on a Loan Applicant

Moving beyond the digital footprint analysis we’ve presented above, which allows us to create a near-impossible-to-fool profile of your customer, SEON’s anti-fraud solution makes use of data to do with someone’s device, location and behavior.

Part of SEON’s Fraud API, this examines available data on the borrowers’ devices used to access your lending, allowing you to:

• flag suspicious devices like emulators and VPNs
• connect users who are using shared devices, which can be tied to fraudulent practices
• see users’ activities across time and monitor when they are suspicious as a whole

Meanwhile, the system can also flag default and custom fields and make them add or subtract risk scores from an applicant, alerting you to those elements that you have discovered are important. In fact, SEON can do this for you if you choose to leverage the whitebox machine learning risk rules.

In addition, sophisticated banking fraud detection software will also consider the user’s actions in relation to time. For a simple example, dozens of separate applications from the same device within a single day can hint at a fraudulent scheme.

Compliance & Regulatory Concerns

Staying in legislators’ good books is important for businesses in lending, especially so when they are part of the startup scene and the fintech boom. When considering a fraud prevention solution that works for you, don’t neglect compliance. Always confirm if using a product will expose your company to non-compliance risks. 

At SEON, we are ISO 27001-certified in addition to meeting GDPR and SCA requirements.

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 COVID-19 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 resulting from COVID-19. 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

• GDS Link: How Banks & Credit Unions Need to Treat Fraud Management in 2022
• Credit Connect: Bank account and loan fraud soars in pandemic
• 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
• CoreLogic: Mortgage Fraud Trends Report
• 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