Fraudsters Exploit Overlooked Weaknesses as SMB Lending Surges Toward $7 Trillion Boom
By Will Tumulty, CEO at Rapid Finance
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Executive Summary
- The global SMB lending market is expanding rapidly, creating new opportunities for financial institutions and fraud networks alike.
- Fraudsters are exploiting gaps in identity validation and fragmented data to mimic legitimate borrowers and infiltrate lending systems.
- Lenders must adopt holistic, data-driven fraud detection strategies to keep pace with evolving schemes and protect their portfolios.
Small-to-medium sized businesses (SMBs) have long been the growth engine of the U.S. economy. Whether built over decades or just starting out, their success comes from the people who drive them. Beyond creating jobs, these businesses and their leaders anchor communities and represent a significant, and often overlooked, untapped opportunity for financial institutions.
The global SMB business lending market is booming, exceeding $2.46 trillion in 2023 and projected to reach $7.2 trillion by 2032. Financial institutions, large and small, see SMB loans as an attractive addition to their lending portfolios. At the same time, few institutions are able to extend capital to SMBs quickly, without increasing their risk. Therefore, it’s no surprise that fraudsters and fraud networks have also caught on to this opportunity and are aggressively targeting this sector, with fraud rates growing even faster than lending growth.
Experian data found that financial fraud against small businesses increased 70% compared to pre-pandemic levels — costing billions annually. Fraud tactics continue to become more sophisticated, particularly as digital channels have become more popular.
From a cursory glance, a fraudster can look nearly identical to a legitimate small business borrower. This comes down to two factors: First, identity validation and fraud detection processes for most lenders are either consumer-focused or commercial-focused, but small businesses exist in the overlapping space between the two. Second, fraudsters have become proficient at exploiting this gray area by blending real identities for both people and companies, obscuring connections and manipulating data to blend in with legitimate applications.
Effectively defending and fighting back against these threats requires a firm understanding of the fraud currently impacting the SMB lending landscape.
Types of Fraud Impacting SMB Lending Landscape
According to Experian, 65% of financial institutions reported an increase in fraud incidents last year, with 46% of small business loan applications showing signs of first-party fraud. Fraudsters are also turning to AI as a tool to further enhance the efficacy of their schemes.
Alongside first-party fraud and AI-related scams, identity fraud has become a favored practice among fraudsters.
These types of schemes include:
Since SMB owners are intertwined with their business, financial institutions must perform KYC and KYB checks during the application process to verify the identity of the borrower and the business. Financial institutions are in a challenging situation since they must verify not only the legitimacy of the business entity and the personal identities of the owners but also validate the relationship between the two.
Dig deeper:
Lenders should also augment reviews by integrating third-party data to create a more complete view of a borrower and help spot data inconsistencies that may be indicative of fraud. However, the data available in the typical small business underwriting process often lacks complete coverage that would assist lenders in identifying fraudulent applications.
The reality is small business financing is a complex tangle of corporate entities, SSNs, EINs, DBAs, bank accounts, addresses, phone numbers, payment histories, principals, etc. Institutions that still rely on outdated manual and reactive identity validation and fraud detection tactics won’t be able to sift through the vast sea of data to find evidence of fraud networks across their historic application base.
Connecting Data Points Across Silos
When it comes to fraud prevention, financial institutions need to connect the dots by taking a more holistic view of their data. This enables institutions to identify patterns across both individual and business attributes. These overlaps in data can provide insights that were previously obscured due to blind spots from the incomplete and fragmented data limitations of traditional underwriting.
It’s common for fraud detection and credit decisions to be isolated from one another. However, when customers’ information is scattered across departments, platforms or product lines, it’s challenging for institutions to identify risk trends and take quick action.
Connecting data-driven insights enable lenders to catch early signs of fraud, such as mismatched borrower histories or unusual application behavior. Breaking down these silos and integrating fraud prevention solutions help lenders create a more unified view of each applicant.
External Data Strengthens Origination and Risk Insights
Many financial institutions successfully integrate internal data into their loan decisioning, but blind spots still remain, especially when a small business applies to multiple lenders at once. Prevalent fraud schemes like loan stacking, synthetic identify fraud and application duplication will continue to grow in different channels.
These risks can be mitigated by incorporating external data sources and shared intelligence networks. Integrating anonymized third-party data, stripped of personal identifiers but rich in behavioral insights, allows institutions to validate applicant information, compare patterns across lenders and improve early decisioning. A few examples of these key data points include public records, transactional data and payments processors, metadata and an applicant’s digital footprint.
For instance, an applicant might pass a lender’s KYB and KYC check using traditional documentation but trigger alarms when cross-referenced against a broader network of lenders. AI-powered tools are becoming more common and can detect usage of a shared IP address across dozens of geographically dispersed applicants, identifying fraud rings that operate under the radar.
Combining these internal and external insights enables proactive fraud management and predictive decisioning; both of which can improve a lender’s approval rate and portfolio quality.
Strategies for Building a Stronger Fraud Defense
As a lender, quickly identifying fraudulent applications even before they reach the credit underwriting phase should be a top priority for mitigating losses, saving time, money and providing a better application experience for legitimate customers.
Fraud is constantly evolving and extremely varied, requiring a proactive multilayer approach to fraud prevention. Manually reviewing all applications and other documents bogs down the lending process. Instead, lenders can use systems that analyze data and documents in real-time, allowing FIs to flag suspicious patterns quickly and incorporate a pass-fail-review framework to determine which applications are passed forward to credit underwriting.
Since fraudsters constantly switch tactics, implementing a customizable rules engine allows lenders to adapt their fraud detection settings based on loan types and application risk profiles. Additionally, by partnering with a trusted lending network that shares anonymized applicant data, lenders can make smarter decisions by banding together.
Spotting the difference between a genuine borrower and a fraudster requires going beyond traditional underwriting data. By embracing a layered, digital strategy, financial institutions can scale their SMB lending, while staying ahead of fraudsters.
