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Brian McClair
I
November 25, 2025
6 minutes to read
Your quarterly exposure report shows XYZ Industries at 65% credit utilization. The CFO's ERP view shows them at 110% with a shipment block. Which number is real?
Both are real. And that's the problem.
Your system has two entries: "XYZ Industries, Inc." and "XYZ Ind." One account holds the active credit limit. The other holds the aging balance from a system migration three years ago. Your report pulled one. The CFO pulled the other. The combined reality is that the customer is over-limit, and your report just lost all credibility.
This isn't a rare edge case. It's the daily operational reality for credit teams managing millions in receivables across fragmented systems. When you can't trust the rows and columns in front of you, you're not managing credit risk. You're managing data chaos.
Data integrity issues rarely manifest as dramatic explosions. They're a slow erosion of trust and efficiency that shows up in three distinct ways: duplicate entities, sporadic reporting failures, and the downstream nightmare of duplicate payments.
When customer master data isn't clean, simple tasks become forensic investigations. You look up a customer to approve an order, but you find multiple records. Which one is the truth? Which one has the correct address? Which one has the tax exemption certificate attached?
Credit teams encounter this constantly. Some customers show up twice in the system with slightly different codes. Others have one record with proper identification and another marked "N/A." Every account lookup requires cross-referencing codes, checking dates, and manually verifying which record is active. A 30-second approval turns into a 10-minute investigation.
This "double vision" doesn't just slow down workflows. It creates exposure gaps. If a customer has two accounts each using $60k against a $100k credit limit, you're actually $20k over your risk threshold without knowing it.
Even when the data is correct, the pipelines delivering that data often struggle under the weight of legacy infrastructure. Credit teams rely on automated reports to prioritize their day, calling the riskiest accounts first. But when reports show up sporadically (working some days, failing others), you might be missing critical risk signals without realizing it.
Sporadic data is worse than no data. If a report fails completely, you know something is broken. If it works sometimes, you're exposed on the days it silently fails, and you have no warning.
Bad data doesn't stay on a spreadsheet. It impacts cash flow. When your system has duplicate customer records or fails to reconcile invoices correctly, customers get confused. They receive duplicate invoices or statements for accounts they thought were closed.
The result? They pay twice. While receiving extra money sounds like a good problem, duplicate payments are an operational headache. They require manual investigation, refund processing, and uncomfortable conversations with customers who wonder why your accounting department can't keep things straight.
Duplicate payments inflate your cash position artificially and create a backlog of unapplied cash that skews your DSO and aging metrics.
If every Credit Manager knows this is a problem, why hasn't it been solved? Why are we still dealing with "XYZ Industries" vs. "XYZ Ind." in 2025? The root causes are usually structural, not personal. It's not that the data entry team is lazy. It's that the environment they work in is hostile to data hygiene.
Many large B2B organizations grow through acquisition. Company A buys Company B. Company A uses SAP. Company B uses Oracle NetSuite. Instead of a full migration (which is expensive and risky), IT builds a "bridge." That bridge often dumps raw customer data from the acquired company into the parent company's ledger without strictly deduplicating it. Suddenly, you have thousands of duplicate accounts with different internal IDs, so the system treats them as strangers.
Most ERPs are designed to be transaction-heavy, not relationship-heavy. They're great at logging an invoice but terrible at understanding that the "Walmart" in Arkansas is the parent company of the "Walmart" shipping to Ohio. Without a Master Data Management (MDM) layer or an intelligent AR platform sitting on top of the ERP, there is no "Golden Record." There's no single source of truth that links all child accounts, addresses, and variations to one ultimate parent entity.
In many workflows, sales reps or customer service agents create new accounts to rush an order through. If the system allows free-text entry for company names without forcing a validation check against existing records (or external databases like Dun & Bradstreet), duplicates are inevitable. A rep types "The Home Depot" instead of "Home Depot, Inc." just to get the order released. The system accepts it. A new account is born. The data is now fractured.
Solving this requires moving away from manual cleanup (which is impossible at scale) and toward intelligent, automated data governance. You need a framework that treats data hygiene as an active, continuous process.
The best way to clean data is to never let it get dirty in the first place. Modern AR workflows must act as a gatekeeper.
Mechanism: When a credit application is submitted or a new account is requested, the system should automatically check against existing master data and third-party bureaus.
The AI Role: AI agents can fuzzy-match text. They understand that "Intl." and "International" are the same. They can flag a potential duplicate before the account is created, prompting the user: "It looks like this customer already exists under ID #5543. Do you want to link this order to them instead?"
For the mess that already exists in your ERP, you need Entity Resolution. This is the process of identifying records that refer to the same real-world entity across different data sources.
Mechanism: Scanning the entire customer master file to link parent-child relationships and merge duplicates.
The AI Role: Instead of a human staring at Excel rows, AI models analyze addresses, tax IDs, phone numbers, and email domains to propose merges. For example, it sees that two accounts share the same tax exemption certificate and bank account number, flagging them as the same entity despite different names.
Data decays. Customers move, change names, or get acquired. A clean database today will be dirty next quarter without monitoring.
Mechanism: Automated periodic reviews of customer master data.
The AI Role: Watching for anomalies in reporting pipelines. If a report fails to pull, the system should self-diagnose: Is the query timing out? Did a field name change? Is the integration broken? Automated alerts should trigger before the user notices the data is missing.
Cleaning up duplicate data isn't just an IT housekeeping task. It is a revenue strategy.
1. Accurate Risk Exposure
If you have a credit limit of $100k for a customer, but they have two accounts each using $60k, your total exposure is $120k. You are $20k over limit and don't know it. Consolidating this data reveals your true risk position immediately.
2. Reduced DDO (Days Deductions Outstanding)
Duplicate payments and billing errors are a leading cause of deductions and disputes. By ensuring the invoice goes to the right account with the right code every time, you eliminate the "administrative" disputes that clog up your collectors' queues.
3. Credibility with the C-Suite
When your reports are accurate, you control the narrative. You stop answering questions about why the numbers don't match and start answering questions about how to drive growth. Reliability builds political capital.
The feeling that "my reports are lying to me" is a symptom of a disconnect between modern business velocity and legacy data structures. As transaction volumes grow, the manual analysts fixing spreadsheets can no longer hold the process together. To regain trust in your AR insights, you must automate the hygiene.
Your 3-Step Data Audit for This Week:
Data is the soil from which all your credit decisions grow. If the soil is toxic, the fruit will be rotten.
Looking for a system that catches duplicates before they're created? Bectran's AI-powered AR platform flags potential duplicates at intake and continuously monitors data integrity. Get in touch with us to learn more.
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