January 17, 2025
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Bad debt poses a significant risk to your company’s profitability and strategic goals, yet data fragmentation often goes unnoticed. When key information, like financial records, trade references, and payment histories, is scattered across disparate systems, businesses lack a comprehensive view of credit risk.
This disjointed data leaves critical gaps in assessments. For CFOs and leaders, it limits the ability to mitigate risk and protect cash flow within your strategy. Let’s explore how this challenge silently undermines credit risk management and what you can do about it.
In credit risk management, every piece of information—from a customer’s payment history to news of industry shifts—adds value to a credit assessment. The more scattered it becomes, the harder it is to track and interpret, ultimately leading to significant financial and operational consequences.
41% of employees report that their company struggles with data fragmentation, which severely hampers the ability to make accurate, timely decisions (McKinsey & Company). This not only creates inefficiencies, but it has numerous hidden risks:
Data gaps can lead to flawed credit decisions. J.P. Morgan’s Guide to Credit Risk warns that missed signals, like payment dips or industry changes, are critical red flags. And a survey found 49% of CFOs lack confidence in their data systems, mainly due to inconsistent or incomplete information. Failing to detect financial deterioration early can result in preventable account write-offs.
Lack of cohesive data delays reactions to overdue payments or changes in a customer’s financial position. When critical data is spread across multiple systems, credit managers scramble to gather it, often reacting too late.
Companies with high data fragmentation experience response delays up to 25% longer than competitors, increasing exposure to high-risk customers and bad debt. Early intervention, made possible by accurate, accessible data, helps prevent late payments from becoming defaults.
Without integrating vital datasets from reliable sources, credit professionals risk missing signs of fraudulent activity or financial distress that would normally be flagged in a unified system.
S&P Global reported that companies with poor data governance are 2.5x more likely to experience significant fraud. Fraud is especially difficult to catch without complete customer data, as isolated records often fail to provide the full picture, allowing bad actors to slip through the cracks.
Bad debt is a pain—but it also impacts the company’s cash flow, profitability, and stability. The global average bad debt ratio is 5-7%, with some companies reaching 10% due to poor risk management.
Companies with fragmented data are 35% more likely to report higher bad debt than those with integrated systems (Deloitte). Slow or inaccurate assessments increase unpaid accounts, raising financial and reputational risks.
This first step is recognizing the issue. Integrating data from all available sources into a single platform with a real-time view of customer creditworthiness is essential. Here are several steps you can take to reduce the risk of bad debt:
Data fragmentation may not be on your radar, but it quietly fuels bad debt and makes managing customer credit a challenge.
The good news? With the right tools and tech, consolidating your data into one actionable view doesn’t have to be a headache. Take control, reduce bad debt, boost cash flow, and reduce risk by making your data work for you—every step of the way.
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