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Bectran Product Team
I
December 10, 2025
6 minutes to read
Credit managers are often asked to act as detectives. When a new credit application lands in the queue, the immediate goal is to determine if the business is legitimate, if they can pay, and if they are who they say they are. In a high-volume environment, the pressure to approve accounts quickly can strip this investigative process down to its bare minimum.
For many teams, the primary tool for verifying a new customer's existence is a simple web search. While internet searches are useful, relying on them as a primary defense against fraud leaves credit departments exposed. Sophisticated bad actors know exactly how to create a digital footprint that looks legitimate on the surface, meaning a manual search often confirms the lie rather than exposing it.
Moving beyond basic searches requires a structured, consistent framework for validation. This shift reduces the reliance on gut feelings and manual detective work, replacing them with a repeatable process that catches discrepancies before goods leave the dock.
The reliance on manual tools stems from time constraints and limited resources. When credit teams are measured by how fast they can turn around an application, deep forensic dives into every new customer become impossible.
Credit professionals describe their current method for checking the legitimacy of a new account: just Google it. If a company has a website, a phone number, and a physical address that appears on a map, they often pass the initial sniff test. Teams are aware this approach is becoming insufficient. Even if fraud hasn't caused a major loss yet, the risk remains a constant background concern. Fraud protection is one of the things credit departments are looking at in new systems. The gap exists between awareness of risk and a concrete strategy that prevents it.
Understanding why the "Google method" fails helps build a better defense. The root causes usually stem from process gaps rather than human error.
Fraudsters today are adept at creating synthetic identities. It costs very little to set up a professional-looking website, register a domain, and even list a business on map services. A manual search confirms that a digital presence exists, but it does not confirm that the business is operational or solvent. Relying on the visual appearance of a business online creates a false sense of security.
In a manual workflow, a credit analyst might check the Secretary of State website for one application, a credit bureau report for the next, and a bank reference for a third. There is rarely a single source of truth. This inconsistency makes it easy to miss red flags. If the Secretary of State filing says the business started two months ago, but the credit application claims they have been in business for ten years, a manual review might miss that discrepancy if the analyst is rushing to meet a one-day SLA.
When a team manages hundreds of applications a month, decision fatigue sets in. If the first fifty "Google checks" turn out to be legitimate customers, the brain naturally begins to assume the fifty-first is also legitimate. Scammers rely on this volume-based complacency to slip through standard checks.
Ad-hoc searches need to be replaced with a framework that triangulates data. Instead of looking for one sign of legitimacy (like a website), a robust process looks for consistency across three distinct layers. If these layers do not align, the application requires a halt.
Verify that a business is legally valid before assessing its legitimacy. This goes beyond a credit bureau report and involves verifying the exact legal name and status with state registries.
Discrepancies here are often the first sign of a synthetic identity or business identity theft.
Fraudsters often use residential addresses, empty lots, or mail drops as business addresses. A simple map search isn't enough. The goal is to verify the type of location.
This is the hardest layer to fake. It involves verifying that the bank account provided actually belongs to the business entity applying for credit.
Implementing a stricter verification framework does more than stop fraud. It protects the operational health of the department.
Reducing Bad Debt Exposure
The most obvious benefit is financial. Preventing a single fraudulent shipment can save tens of thousands of dollars. In low-margin industries, the revenue required to offset a $50,000 fraud loss is significant. Stopping it at the front door is the only efficient way to manage this risk.
Protecting the Customer Master
Clean data at the entry point ensures clean data downstream. When fake or duplicate accounts enter the ERP, they pollute reports, skew aging analysis, and create confusion for collections teams later. A rigorous entry process acts as a filter, ensuring the Customer Master remains reliable.
Empowering the Credit Team
When credit managers have a defined fraud process rather than just "Googling it," they can defend their decisions to Sales with data. Instead of saying, "I have a bad feeling about this," they can say, "The Secretary of State registration doesn't match the bank account holder." This changes the conversation from an opinion to a factual risk assessment.
Fraud prevention is about ensuring the sales that go through are profitable and real. If your current process relies heavily on manual web searches, consider this short checklist to identify gaps in your defense.
Questions to Ask Your Team:
By answering these questions, you can move from a reactive stance (hoping the Google search is enough) to a proactive framework that secures the business against modern threats.
Ready to automate fraud detection? Bectran's application processing automatically validates Secretary of State registrations, flags residential shipping addresses, and cross-references bank account ownership before the application reaches your desk. See how fraud prevention works.
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