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Top 7 Steps for Preventing Loan Application Fraud

Guest Post | Fraud.net | Jun 30, 2021

Fraud.net stop loan application fraud - Top 7 Steps for Preventing Loan Application Fraud

While some loan applicants default for legitimate reasons, some criminals apply for loans and lines of credit with the intent of maxing them out and not making any payments. Taking steps toward preventing application fraud can reduce the cost of debt default to save you money.

In May 2020, two men were charged with submitting a fraudulent Paycheck Protection Program loan application for a restaurant that had been closed for over two years. As financial institutions process large numbers of applications and work on issuing funds within short deadlines in the context of the PPP, it’s important to remember that loan application fraud is a costly issue.

Consumer debt reached almost $14.9 trillion during 2020 after steadily increasing over the five previous years. With increased activity, comes increased fraud. So, as the market continues growing, implementing new measures to lessen the impact of debt default linked to fraudulent applications should be a priority.

What is loan application fraud?

Many applicants default for legitimate reasons, but some criminals apply for loans and lines of credit with the sole intent of maxing them out and skirting payments.

Some criminals use their own identities to apply for loans, then go off the grid to avoid repaying them. However, stolen or synthetic identities — also known as third-party fraud — remain the preferred method. With third-party fraud, scammers use multiple identities to open different credit lines.

In 2020, identity theft accounted for more than 29% of all fraud reports received by the FTC. Their data revealed a sharp increase in loan application fraud, with auto-loan and lease fraud increasing by 105% between 2018 and 2019. Similarly, business and personal-loan fraud rose by 116% over the same time period.

Federal student loan fraud rose by 188% between 2018 and 2019, representing the fastest-growing identity fraud type.

Furthermore, it’s estimated that more than 10% of accounts considered bad debt by banks are actually fraudulent accounts.

Top 7 steps for detecting and preventing loan application fraud

Complying with Know Your Customer (KYC) and anti-money laundering best practices reduces fraud risk. Despite this, your loan application fraud prevention and detection efforts should go beyond these baseline requirements. Here are seven measures you can take to reduce risk further.

1. ID verification and facial recognition

A simple step like asking for two or more forms of ID helps prevent application fraud by introducing an additional barrier for fraudsters.

For online applications, you can implement an automated ID verification solution in real-time that requires the user to snap a picture of an ID along with a selfie. Liveliness technology verifies that the selfie is an image of the person and not a snapshot of a photograph. Following that, facial recognition technology verifies that the selfie and ID match.

A simple video call offers another possible alternative. Loan officers ask a few questions to verify the applicant’s identity during the call and make sure they look like their ID photo.

2. Identity data validation

On average, synthetic identity (ID) fraud costs $6,000 per fraudulent account. Criminals who commit synthetic ID fraud use data from different real or fake identities to create a new identity that looks legitimate.

See:  How Verifiable Digital Identity Will Protect Your Post-Pandemic Privacy

Cross-referencing identity data with public and private databases helps reveal some inconsistencies and flag synthetic identities. Often, data like a name, date of birth, address, or SSN won’t match records.

3. Financial documents from a bank or employer

Asking applicants to upload bank statements, pay stubs, and other financial documents themselves means there is a risk that they could tamper with these proofs.

Instead, request permission to contact their bank or employer to verify their claims. It offers a reliable way of preventing application fraud since you can validate income, assets, and other financial details.

4. Bank account verification

Bank account verification ensures that applicants can access the bank account they plan on using to make payments and protect personal information.

Ask for the account information and make a micro-deposit, usually ten to fifty cents. This step verifies the existence of the bank account. Furthermore, asking the applicant for the exact amount of the micro-deposit ensures they have access to the account.

5. Knowledge-based authentication

Knowledge-based authentication assists with application fraud prevention by going beyond the data points a criminal could steal or spoof.

See:  6 lessons on online privacy and digital authentication

Use an applicant’s credit report to generate multiple-choice questions only they would know the answer to. For example, include questions about previous addresses, other lines of credit, or past vehicle purchases.

6. Phone and social media verification

You can send a push notification through your app to perform out-of-band verification. This step verifies that the phone is a physical device registered to a mobile network and not a VoIP number.

Moreover, social media verification helps confirm an identity’s legitimacy based on social media activity and connection with other users. If an applicant uses a fake identity, their social media presence might be nonexistent. Or, they lack the usual kind of pattern for profile connections in the same geographic area.

7. Identity risk scoring

Machine learning-based solutions, like the one Fraud.net offers, analyze multiple data points and cross-reference information with different databases. This approach helps flag identities that have been stolen in the past and detect synthetic identities with data points taken from multiple stolen identities.

In addition, machine learning solutions look at past account activities, establish links with other loans or credit lines that were defaulted on, and analyze geolocation data to spot any discrepancies in fraud detection.

The Fraud.net machine learning solution helps with preventing application fraud by generating a risk score that indicates the likelihood that the application is fraudulent so loan officers can prioritize high-risk applications and perform additional identity verification steps if needed.

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