Fraudsters know that banks are working feverishly to process Paycheck Protection Program (PPP) loan applications and have therefore bumped up their game in their hunt for weaknesses in banks’ controls and systems. In fact, fraud committed in connection with PPP is estimated to rise to as high as 12%, according to a recent American Banker article. In this post, I list three PPP-related fraud scenarios banks need to be on the alert for and strategies to mitigate them.
Fraud is on the rise
Fraudsters are pinning their hopes on the possibility that banks will short cut prudent fraud mitigation strategies as they scramble to process high volumes of loan requests. In addition, the forgiveness aspect for the PPP loans may be in jeopardy for those loans that were extended with red flags but were not investigated. As such, banks will need to become more diligent in the processing of PPP loans to prevent fraud.
Here are three fraud scenarios to watch for:
- Inflating the PPP loan need. Applicants fraudulently increase the number of employees or actual payrolls to receive larger PPP payouts, then use the funds for non-payroll related expenditures, i.e., rent, equipment, etc.
- Using stolen identities to apply for loans. Banks have reported that fraudsters take over the ID of legitimate business owners and apply for proceeds. In other cases, nefarious individuals apply for a PPP loan after their business has become insolvent with no intention of using the funds to reopen their business.
- Using Synthetic IDs to apply for PPP loans. A unique tactic wherein a fraudster creates a fictitious business by using synthetic IDs to open false bank accounts, tax documents, etc., to receive funds. After the funds are paid out, the lender discovers the information provided cannot be verified.
Compounding the problem, we have repeatedly seen organized rings are conspiring to commit fraud. They share information about vulnerabilities at a single institution, move quickly against those entry points, and typically cause significant financial damage before detection.
Addressing these scenarios
Due diligence and analysis of the current and historical deposit relationship with the business is a sure way to determine if payroll inflation is occurring and the business is still solvent and active. Business credit reports track not only the business performance but negative information as provided by suppliers to that business. Initially, financial institutions provided PPP loans to existing customers only to mitigate against this type of fraud. However, in subsequent PPP allocations, banks were required to offer loans to non-customers, making it impossible for banks to gauge payroll by account activity. Annual revenue data can be used to determine the potential for inflated payroll figures, as reported by the applicant. Industry, geographical, and tax record data analysis can be accelerated with intelligent automation and AI to identify and mitigate against this activity.
Synthetic ID creation is the newest tactic widely used by fraudsters, which is proving to be more difficult to detect. New, innovative modeling tools and strategies have proven successful in the initial detection of synthetic IDs. By developing detection modules based on the profile of “good” customers, as opposed to known fraud accounts, banks can significantly increase detection efforts while reducing false positives, which increases the customer friction factor and reputational damage to good customers.
Find the right partners
NTT DATA (in partnership with software vendors) has developed an end-to-end solution for the monitoring, detection, and mitigation of fraudulent activity by criminals who are trying to take advantage of this current volume of applications from the CARES Act packages. Our suite of tools, accelerators, and applications, along with the guidance of seasoned fraud SMEs, can be customized for our clients based on their unique needs regarding population segments, customer behavior, and product offerings.
We can also help you navigate the Federal Reserve’s FraudClassifier Model, which the government recently introduced to help financial institutions better classify and understand the magnitude of fraud regarding payments.
Date de la publication : 2020-08-20