Data accuracy should start with proactive solutions

by Kerry Rivera 2 min read June 23, 2016

blog-post-image-data-arc-930x420

What you give, you get. At least that is what popular philosophers claim.

And if you think about it, this statement is also applicable to the world of data accuracy.

As organizations of all sizes increasingly rely on data to interact with customers and create insights to drive strategy, it’s no secret bad data can quickly lead a company or financial institution down the wrong path, even landing them into regulatory troubles.

A recent Experian Data Quality study found:

  • Seventy-five percent of organizations believe inaccurate data is undermining their ability to provide an excellent customer experience.
  • Sixty-five percent of organizations wait until there are specific issues with their data before they address and fix them.
  • Fifty-six percent of organizations believe mistakes can be attributed to human error.

For years, organizations have wanted good data simply for operational efficiencies and cost savings, but now a shift has taken place where businesses are using data for nearly every aspect of their organization. The majority of sales decisions are expected to be driven by customer data by 2020, with companies determined to turn data into actionable insight to find new customers, increase customer retention, better understand their needs, and increase the value of each customer.

Additionally, the Fair Credit Reporting Act (FCRA) requires those contributing data to provide accurate and complete information to the credit reporting agencies. If they fail to meet accuracy obligations when reporting negative account histories to credit reporting companies, the result could be bureau action and fines.

Organizations still deal with a high degree of inaccurate data because there are a number of challenges to maintaining it. Some of them are external forces, but many are internal challenges – most notably a reliance on reactive data management strategies.

The biggest problem organizations face around data management today actually comes from within. Businesses get in their own way by refusing to create a culture around data and not prioritizing the proper funding and staffing for data management. Many businesses know they need to improve their data quality, but often have a hard time defining why an investment is needed in the current structure.

Solutions exist to get in front of data accuracy challenges. DataArc 360 Powered by Experian Pandora, for example, is designed to check the consumer credit information provided by data furnishers prior to submission to credit bureaus. This allows data suppliers to take more of a proactive approach to ensuring the accuracy of information, that may result in fewer credit disputes and a more positive interaction between consumers and their credit.

Creating a clear governance plan, and centralizing data management policies and policies can also clean up internal challenges and improve accuracy standards.

The importance of data cannot be neglected, but again, the data has to be clean for it to matter. What you give is what you’ll get.

Related Posts

Building for Bots in the AI Age: Separating Legitimate Agents From Fraudsters

See how NeuroID helps businesses distinguish legitimate AI agents from fraud bots, while Agent Trust verifies agent-initiated transactions.

October 29, 2026 by Laura Burrows
2026 Retail Banking Identity and Fraud Report 

Learn how banks can convert rising fraud investment into protection customers recognize, value and feel in our recent report. Read more!

October 28, 2026 by Laura Burrows
One Platform, Fewer Handoffs, Faster Decisions

The average credit union manages between 100-300 vendor relationships, and nearly a third have no dedicated staff managing them. What was supposed to drive efficiency is stunting growth and compromising the member experience. Application volume is continuing to rise, and consumers expect a loan decision at digital speed. But for credit unions, meeting that expectation isn't the only goal. They must also continue to make the right lending decisions while also prioritizing risk management. The institutions pulling ahead aren't the ones with the most tools. They're the ones that have turned credit, fraud and decisioning into a single, coordinated system. A single loan decision touches: Credit data Identity verification Fraud signals Decisioning logic Reporting Rarely does all of that live in one place. More often, it’s stitched together across separate platforms, each with its own contract and its own support line. When a credit view and a fraud view are generated separately, they must be reconciled instead of read together in the same instant. Every handoff is a place where a decision slows down and a signal gets missed. It's why a fraud pattern that would be obvious next to the credit file often surfaces only after the funds are already gone. Fragmentation also makes it harder to optimize lending strategies over time. When data, analytics and decisioning are disconnected, credit unions have less visibility into what's working, what's changing and where strategies need to be adjusted. An integrated decisioning ecosystem closes those seams by design, with credit, fraud and lending strategy running on one platform and accessed through a single Application Programming Interface (API). Identity and compliance capabilities can be an integrated decisioning flow. The data doesn't need to be reconciled after the fact because it was never separated in the first place. Stop overengineering, start removing friction When decisioning underperforms, the instinct is to build something more custom: a proprietary model, tighter segmentation, more configuration.  Custom modeling has its place for institutions with the scale and data to justify it. But often, for small or mid-sized credit unions, the bigger opportunity is a flexible decisioning strategy that uses a strong, continuously refreshed model. This shouldn't be either/or. The right approach gives credit unions a strong decisioning strategy today, with the ability to introduce more customized analytics and modeling as their needs evolve.  The same principle applies to strategy management: credit unions can gain greater control through self-service capabilities as they build a more sophisticated data science team.   That kind of flexibility is important as credit unions modernize their decisioning infrastructure. Research surveying more than 2,000 senior credit risk and fraud decision-makers across 11 countries found that organizations investing in unified platforms and modern infrastructure are moving faster and realizing the greatest Return on Investment (ROI).   As their needs evolve, credit unions can adjust decisioning strategies without introducing another layer of technology or relying on third parties to make every change. Until self-service is the right fit, choose a partner that understands what makes your credit union’s membership and risk appetite different from others.  Same member, same afternoon, faster answer Consider a credit union growing its auto and personal loan originations.  A member applies for an auto loan on Saturday afternoon. Credit data, identity and fraud signals are read together in a single real-time pass instead of separate reviews:  Most applications resolve immediately, approved or declined based on a more comprehensive view of the applicant. A smaller number are flagged for quick step-up verification and cleared within minutes. Only genuinely ambiguous cases reach an underwriter, who is working from one complete picture instead of reconciling multiple signals. The member has an answer before the weekend is over. The credit union books the loan before competitors even open on Monday. Built to scale This kind of process holds up today. However, the better test is whether it holds up eighteen months from now when application volume has grown, new products are live and the data flowing through the system looks nothing like it does right now. Future-proofing isn't only about handling more volume. It's about having room to be nimbler and make adjustments as quickly as the market changes. A credit union that relies on standard models within a decisioning strategy may want more customized analytics and greater control over its strategies tomorrow. Connect with us With more than three decades of credit expertise and one of the industry's largest data footprints, Experian helps credit unions do more than solve this year's friction. We're helping to build a decisioning foundation that scales, so growth becomes something the system absorbs, rather than something it must be rebuilt around. Explore More

October 21, 2026 by Zohreen Ismail

Subscribe to our Newsletter

Enter your name and email for the latest updates.

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Subscribe to our Newsletter

Don't miss out on the latest industry trends and insights!
Subscribe