15 Types of Common Fraud Attacks in Digital Lending

As the digital lending market continues to expand, it faces a growing number of fraud attacks, which are becoming more sophisticated each year. This rising tide of threats can impact both the security of financial transactions and the trust placed in digital lending platforms. This article examines the diverse landscape of digital lending fraud, detailing 15 common fraud attacks that lenders encounter today. We'll also explore innovative solutions designed to help lenders detect, prevent, and address these threats, ensuring both operational integrity and customer trust.

What is digital lending fraud?

Digital lending fraud encompasses various deceptive practices targeting online lending platforms and their users. These malicious activities range from identity theft to sophisticated cyberattacks, each designed to divert funds, compromise personal data, or inflict financial damage on lenders and borrowers alike.  

Common types of digital lending fraud

Digital lending, while providing convenience and accessibility, also opens the door to numerous fraud types. The 15 most prevalent fraud attacks in the sector are the following:

1. Identity theft: involves fraudsters stealing personal information, such as Social Security numbers, to impersonate legitimate borrowers. The criminals apply for loans and credit lines under these stolen identities, often leaving the victims with debt obligations and damaged credit scores. Caught between the demand for rapid approvals and accurate credit scoring, lenders struggle to verify identities reliably, making this one of the most pervasive challenges in digital lending.
2. Loan stacking: stacking occurs when a borrower secretly takes out multiple loans from different lenders within a short timeframe, without any intention of repaying them. This fraud is difficult to detect in real-time due to the lack of immediate data sharing between lenders, often resulting in significant financial losses.
3. Synthetic identity fraud: synthetic identity fraud involves creating new identities by blending real and fabricated information. These identities can pass initial verification checks, allowing fraudsters to accumulate credit and disappear without repaying. This complex fraud type challenges traditional detection methods and requires sophisticated data analysis tools.
4. Phishing and spear phishing: phishing attacks trick individuals into revealing sensitive information through seemingly legitimate emails or messages. In spear phishing, the attacks are more targeted, using information specific to the individual to appear more credible. These scams can lead to unauthorized access to lender platforms or borrower accounts.
5. Account takeover (ATO): in account takeover fraud, cybercriminals gain unauthorized access to a borrower’s existing lending account, often by leveraging stolen login credentials. They can redirect funds, change account details, or take out new loans, all under the guise of the legitimate account holder.
6. Data breach exploitation: this one is a bit more complex. Data breach exploitation occurs when fraudsters use personal information stolen from unrelated breaches to apply for loans or credit. This type of fraud is particularly challenging to guard against because it uses legitimately obtained information from external sources. Essentially, fraudsters exploit stolen personal information that, on the surface, appears legitimate because it belongs to a real person: 
   1. Illegally acquired but real data: fraudsters obtain sensitive personal information (like names, addresses, social security numbers, etc.) from data breaches, which are illegal. Despite being stolen, this information is genuine and tied to actual individuals.
   2. Appears authentic during verification: when fraudsters use this stolen data to apply for loans or credit, it can pass verification checks because it matches the records from legitimate sources (such as credit bureaus or government databases).
   3. Challenges in detection: because the data itself is real and not fabricated, detecting fraud becomes more complex. Fraud detection systems may fail to flag the activity as suspicious since the provided details match official records.
7. Document and application: document and application fraud involves the submission of forged documents or falsified information to deceive lenders during the loan application process. This may include altered payslips, fake bank statements, or doctored IDs, complicating the verification process for lenders.
8. Malware and ransomware attacks: malware and ransomware can infiltrate lender systems to steal credentials, intercept transactions, or encrypt critical data, demanding a ransom for its release. These attacks not only cause direct financial losses but can also undermine trust in the lender’s security measures.
9. Social engineering: this type of fraud manipulates people into performing actions or divulging confidential information. Fraudsters may pose as representatives of the lending institution to extract critical security information or initiate unauthorized transactions.
10. Deepfake and AI-driven scams: fraudsters use AI technologies to create deepfake videos or audio recordings, impersonating legitimate parties to authorize fraudulent transactions or manipulate loan processes. This emerging threat poses significant challenges due to its sophistication and believability.
11. Third-party fraud: involves external entities that facilitate or partake in fraudulent activities. This could include payment processors, loan brokers, or other intermediaries who exploit their access to the lending process to divert funds or data.
12. Fraudulent loan applications: in fraudulent loan applications, borrowers may intentionally inflate income, understate liabilities, or fabricate employment details to secure loans for which they would not typically qualify. Detecting these discrepancies requires robust cross-verification processes.
13. Collusion with insiders: insider fraud involves employees within the lending institution who misuse their access to systems for personal gain or to aid external fraudsters. This type of fraud is particularly insidious as it involves betrayal from within the organization.
14. Payment diversion fraud: redirects legitimate loan disbursements to fraudulent accounts. This may involve hacking into digital communication channels to alter transaction details or impersonating a borrower or lender to reroute funds.
15. Advance fee schemes: in advance fee fraud, borrowers are tricked into paying upfront fees for promised loans that never materialize. Fraudsters often target vulnerable individuals desperate for financing, making this scam particularly harmful and deceitful.

How to counter these types of fraud in digital lending

The increasing sophistication of fraud in digital lending requires equally advanced countermeasures. Trustfull offers a range of innovative solutions that help lenders not only detect but also prevent various forms of fraud, ensuring the integrity of the lending process and safeguarding customer information.

Advanced fraud prevention for digital lenders

Trustfull's identity intelligence platform enhances the underwriting process by integrating over 500 alternative data points, enabling lenders to build highly accurate predictive models. Relying on this comprehensive data catalog allows for effective detection of “No Intent to Repay” scenarios, where borrowers may not have the financial means or intention to fulfill repayment obligations. Early identification of high-risk borrowers helps prevent significant financial losses and reduces default rates without impacting the overall approval rates.

Operational cost reduction

Implementing a fraud prevention solution can significantly lower operational costs associated with traditional credit bureau queries, ID verification checks, chargeback management, and manual reviews of false positives. By using Trustfull to identify and flag suspicious loan requests from the outset, businesses can avoid the downstream costs associated with processing fraudulent applications, such as unnecessary credit checks, ID verification expenses, and the labor-intensive management of fraudulent cases. This allows lenders to allocate resources more efficiently, prioritizing growth and customer service over fraud mitigation.

Real-time risk assessment and predictive analytics

Another important addition to a comprehensive fraud prevention strategy is real-time assessment of applicant risk profiles, which speeds up the credit decisioning process. This capability ensures that the most promising loan requests are fast-tracked, minimizing the time and resources spent on high-risk clients. The use of predictive analytics further improves the quality of the customer base by foreseeing potential defaulters early in the loan cycle.

Balancing security and user experience

One of the key challenges in online lending is maintaining a frictionless user experience without compromising security. Trustfull’s advanced fraud detection systems operate seamlessly in the background during the loan application process. This silent operation ensures that checks on users' digital footprint and the analysis of their associated risk are performed without disrupting the customer journey, providing a smooth and user-friendly experience at every step.

Enhancing fraud detection rates

After implementing digital signals and risk scoring models, lenders typically observe a significant uplift in identity fraud detection rates. The Trustfull platform helps identify potential defaulters early in the process without impacting overall approval rates, contributing to a more secure lending environment. Lenders working with Trustfull benefit, on average, from a 45% increase in detecting borrowers with no intent to repay at the loan request phase, ensuring a healthier portfolio and reduced default rates.

Selected digital signals for online lenders

Trustfull’s platform utilizes specific digital signals that are particularly effective for online lenders, including:

• Phone number status: validates if the number of the applicant is currently active and connected to the network.
• Disposable numbers: detects and flags single-use disposable phone numbers often used in fraud schemes.
• Porting history: monitors if the phone number was recently ported, which represents a sign of trust and an indicator that the number has been in use for a long time.
• Email domain analysis: checks if the email domain is associated with a legitimate, actively used website.
• Online presence: determines if the email address or phone number is linked to credible online services, enhancing trust.
• Image analysis: utilizes advanced algorithms to analyze profile pictures from various digital platforms to uncover risk factors.
• Data breach history: assesses whether the provided contact details have been involved in previous data breaches.
• VPN, TOR, and proxy detection: identifies tools used to mask a user’s true location, a red flag in many fraud scenarios.

Strengthening fraud prevention measures with Trustfull

Leveraging Trustfull's rich suite of tools and technologies enables digital lenders to fight back against the most common threats targeting their sector and services. 

Advanced data analytics, predictive modeling, and real-time checks can easily be integrated into fraud detection and prevention processes. This makes it simpler for digital lenders to balance more sophisticated security measures and an optimal customer experience, ultimately fostering a trustworthy digital lending environment.

To explore the latest innovations in digital lending fraud prevention, talk to our team of anti-fraud experts.