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Bectran Product Team
I
December 1, 2025
9 minutes to read
You have a credit application from a new distributor. The bank statement shows a healthy six-figure balance. The D&B report is clean. Everything looks legitimate. Then you notice something: this exact bank statement appeared in a different application three weeks ago. Same transactions. Same dates. Same ending balance. The only difference is the company name at the top.
This isn't a sophisticated cyberattack. It's a cut-and-paste job. And it almost worked.
In the high-stakes world of B2B credit, we often prepare for complex financial distress: bankruptcy, slow pay, liquidity crunches. We build models to predict insolvency. But increasingly, Credit Managers are facing a different threat: blatant, low-tech forgery.
The barrier to entry for B2B fraud has effectively vanished. A fraudster doesn't need to be a hacker. They just need a PDF editor and a lack of moral compass. They aren't trying to trick an algorithm. They are trying to trick you, relying on the sheer volume of applications you process to hide their activity.
Credit teams are encountering cases where fraudsters submit the same bank statement for multiple entities, simply changing the company name on the header while keeping the transaction history identical. They are betting that your team is too busy to notice that Company A and Company B have the exact same spending habits down to the penny.
When a Credit Manager catches this, it feels like a victory. But it also raises a terrifying question: How many slightly better forgeries slipped through because the font matched a little better?
Why are we seeing a spike in this specific type of document fraud now? It is not just that fraudsters are getting bolder. It is that the B2B credit infrastructure has shifted in ways that unintentionally favor the forger.
Fifteen years ago, bank statements might have arrived via mail, or required a fax directly from the institution. Today, everything is a digital upload. We accept PDFs as the standard of truth. However, a PDF is an editable container. The moment a document is digital, it is mutable. We have optimized our intake processes for speed and convenience (allowing customers to upload files directly into portals), but we haven't always matched that speed with forensic verification tools.
A quick search on the dark web, or even just Reddit or Telegram, reveals marketplaces selling "aged" shelf corporations and "editable" utility bill and bank statement templates. These aren't just blank forms. They are sophisticated templates with the correct logos, fonts, and layout structures of major banks (Chase, Wells Fargo, BoA). A fraudster can buy a template for $50, input a fake balance, and export a PDF that looks 99% authentic to the naked eye.
Credit teams are under immense pressure to approve customers faster. Sales teams want credit limits established in hours, not days. When you are processing 50 applications a week, you cannot perform a forensic pixel-peep on every page of every bank statement. Fraudsters know this. They rely on "notification fatigue." They know that if the D&B score looks okay (because the shell company has no negative history yet) and the bank statement shows cash, the human reviewer is likely to click "Approve" to clear the queue.
The biggest vulnerability is often the lack of memory within the credit department. If a fraudster hits you with five different company names, your ERP likely treats them as five distinct customers. Unless you have a system that links guarantors, IP addresses, or recognizes the visual hash of a document, you have no way of knowing that the applicant for "Alpha Logistics LLC" is the same person as "Beta Supply Inc."
Since we cannot rely on the inherent truth of a PDF anymore, Credit Managers need to adopt a forensic mindset. You don't need to be a cybersecurity expert, but you do need a framework for validating documents that goes beyond "does this look right?"
Here is a four-part framework for spotting the fake without slowing down legitimate business.
The most careless fraudsters change the visible text but forget the invisible data. Every PDF carries metadata, information about how and when it was created. Open the PDF properties and check the "Producer" or "Creator" field. A legitimate bank statement is usually generated by an automated mainframe system. If it says "Microsoft Word," "Canva," or "Adobe Photoshop," it is a forgery. Banks do not draft monthly statements in Microsoft Word. Also check the modification date. If the statement covers the period of January 1 to 31, but the file creation date is today with a "Modified" timestamp minutes after the "Created" timestamp, that often indicates tampering. Finally, perform the layer test. In some PDF viewers, you can click on the text. If you click on the balance and a text box outline appears that is slightly misaligned with the rest of the row, you are looking at an edit.
Fraudsters are often bad at math. They will change the ending balance to show $500,000, but they won't bother to adjust the running balance of the transactions listed above it. Pick three random transactions in the middle of the statement. Take the previous balance, apply the debit/credit, and see if it equals the new balance. In forged documents, these numbers frequently do not sum up because the forger only changed the big number at the bottom. Also check the interest calculation if the statement shows an interest-bearing account. If the account claims to have $1,000,000 but the monthly interest earned is $0.05, the numbers are fabricated.
Even with good templates, formatting errors occur. Zoom in on the numbers and check font consistency. Is the font for the account balance exactly the same as the font for the date? Often, fraudsters use a standard Arial or Times New Roman to replace a number, failing to match the proprietary or specific font the bank uses. Look at the column alignment. In automated bank statements, decimals align perfectly. In a manual edit, the cents often drift slightly left or right compared to the row above. Finally, look for artifacting like "halos" or fuzzy pixels around the text of the name or balance, especially if the rest of the document is sharp. This suggests that part of the image was erased and typed over.
The document itself is compromised. Therefore, you must verify the truth outside of the document. Instead of relying on the PDF, use bank account verification tools (like Plaid or similar) that log into the bank data directly. If the customer refuses to connect their bank and insists on sending a PDF, that is a red flag. Also perform the "Google Earth" drive-by. Look at the address on the bank statement. Put it into Google Street View. Is it a residential house? A UPS store? A vacant lot? If they claim to be a major distributor with $500k in the bank, but their address is a strip mall mailbox rental, the bank statement is likely fiction.
Catching a forged bank statement is about more than avoiding a single bad write-off. It is about protecting the structural integrity of your credit department.
There is a distinct difference between a customer who fails and a customer who steals. A failed customer might pay you 10 cents on the dollar in bankruptcy. A fraudster will pay you zero. They will max out the credit line, divert the goods, and vanish. The loss on fraud is 100%, often compounded by the fact that the inventory is gone and cannot be resold.
When fraud creates heavy losses, companies react by tightening credit policies across the board. This hurts your good customers. By surgically identifying and removing the fraudsters (the "bad actors"), you can afford to remain lenient and flexible for the legitimate businesses that need your support.
Nothing demoralizes a credit team faster than realizing they were duped by a simple trick. Seeing the same bank statement used over and over with just a different company name is insulting to the professionalism of the credit manager. Stopping these attacks empowers the team and reinforces that their role is strategic defense, not just data entry.
The ultimate goal is to move from "spotting the fake" to "preventing the submission."
If you are manually reviewing every PDF, you are already losing the efficiency battle. The fraud landscape has evolved beyond simple copy-paste jobs. Fraudsters are now using AI to generate realistic-looking financial documents that pass basic visual inspection. They can create synthetic bank statements with perfect formatting, correct fonts, and even plausible transaction histories.
This is where automated document intelligence becomes critical. Modern credit platforms like Bectran can analyze documents at a level impossible for human review:
Metadata Analysis: Automatically flagging PDFs created in Word processors instead of bank systems, or documents with suspicious modification timestamps.
Duplicate Detection: Cross-referencing submitted documents against your entire application history to catch the exact scenario from our opening: the same bank statement reused across multiple fake companies.
AI-Generated Image Detection: Bectran's advanced fraud prevention can identify when a document or supporting image has been created by generative AI rather than originating from a legitimate financial institution. This catches the next generation of fraud that traditional OCR and metadata checks would miss.
The fraudster who "just changes the name" relies on you being asleep at the wheel. But they also rely on your systems being unable to connect the dots across applications, or detect when a document was synthetically generated rather than genuinely issued. Automation closes both gaps.
To immediately tighten your defense against forged bank statements, share this checklist with your team for the next credit review meeting:
1. The "Ctrl+F" Test
Open the PDF. Can you highlight the text? If the bank statement is a flat image (a scan) but claims to be a digital download, ask why. If it is text, check if the fonts vary between the header and the body.
2. The Math Spot-Check
Do not just look at the ending balance. Calculator-check three random transactions. Do they sum correctly? If not, reject immediately.
3. The Metadata Peek
Right-click and select Properties. Does the creation date make sense? Is the "Producer" suspicious (e.g., Microsoft Word)?
4. The Duplicate Check
Does this address, phone number, or guarantor name exist anywhere else in your customer master? If yes, pull that file and compare the documents visually.
5. The Hard Conversation
If you suspect a forgery, do not email. Call. Fraudsters hate the phone. Ask them specific questions about a transaction on the statement: "I see a large debit on the 14th to X Supply Co, can you explain that relation?" If they stammer or cannot answer, you have your answer.
Fraud is evolving, but it isn't magic. It's often just a PDF editor and a gamble that you won't look closely. Make sure you do.
Ready to automate fraud detection? Bectran's AI-powered application processing and fraud suite goes beyond basic checks, automatically flagging suspicious metadata, duplicate submissions, and even AI-generated fraudulent images before they reach your desk. Get in touch with us today.
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