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Home > Technology > How Siloed Data Leaves Financial Institutions Open to Fraud

How Siloed Data Leaves Financial Institutions Open to Fraud

Published by gbaf mag

Posted on October 19, 2020

5 min read

Last updated: January 21, 2026

Illustration of financial data management highlighting fraud prevention strategies - Global Banking & Finance Review — Visual representation of effective data management strategies in finance, emphasizing the importance of accurate data in fraud prevention. This image relates to the article's focus on reducing fraud risk in financial institutions through better internal data practices.

By Stephany Lapierre, CEO Tealbook

Reducing the risk of fraud is a top priority for all financial institutions since fraud is responsible for massive profit loss, as well as the degradation of an institution’s integrity and brand.

In trying to prevent fraud, most executives look to protect themselves from the outside in, implementing layers of security and launching reactive measures. However, in order to truly protect your organization from fraud, it’s imperative to begin by looking at your existing internal structures. The most critical and often overlooked area to assess is how your organization obtains, enriches, and distributes data.

Streamlining and scrubbing your data can increase profitability without adding to resource spend. Having good data allows you to complete your due diligence on vendors and external entities your organization regularly deals with. It favorably adjusts your efficiency ratio and reduces risk by eliminating redundancies, conflicting information, and information gaps. In addition, it allows smaller teams to operate with increased scale and effectiveness. In turn, this leads to a more effective vendor vetting process and less room for error in payment information verification.

Conversely, poorly managed data is confusing and deceiving and can play an unfortunate role in giving fraudulent access to outside parties through internal miscommunications. For example, updates could be made in one system and not another, and suddenly different departments are working with different data sets like payment information or legal formation documents that regulators look for in audits, and no one knows what is true or accurate. This effect snowballs over time, creating massive holes in the integrity of the data, creating unnecessary risk exposure and audit failures.

All of these vulnerabilities can serve as the foundation for developing a risk management protocol that may be rendered useless if it is based on poor data. It is impossible to properly vet vendors and suppliers or verify payment information if the data is unreliable.

By investing in a solid Data Foundation, you’ll see an increase in the success of your risk management and fraud prevention measures. In many instances, you won’t need to add more steps or resources, just power your existing systems with clean, agile, and accurate data to see improved efficiency.

Here’s a closer look at the most common vulnerabilities within a typical financial institution’s data ecosystem:

Fragmented Organization Structure

As organizations grow and scale, it’s inevitable that different subsections will become isolated from one another and begin different processes for data management. Poorly managed systems can exacerbate this lack of communication and threaten data integrity.

It may not seem like cause for concern if a few different arms of an organization aren’t completely in sync. However, in the financial space, this issue rarely applies to just one or two organizational divides. For example, a prominent US-based financial institution boasts over 90 business units, all of which need to be synergized in order to prevent inaccurate data, redundancies, and problems with regulatory information gathering. This siloed information is, unfortunately, a common practice that needs to be addressed.

Unmanaged Proprietary Systems

In an attempt to serve data in a highly specialized way, many institutions have explored developing proprietary data systems for internal use. However, because of factors like employee turnover or an inability to keep up with data integrity best practices, these legacy systems quickly become obsolete and unmanaged. Their custom nature also renders them inflexible and unable to integrate with other solutions.

When trying to work around an unmanaged system, different branches of an institution may turn to different solutions. When work is being done across different platforms, this reduces visibility and increases risk for inaccuracies, which leads to poor decisions, costly rework, and potentially fraud.

If your organization is reliant on a proprietary system, consider if that system is functional and scalable. If it’s not, you may want to look into a flexible data management system that can work with other technologies.

Disparate Information Across Systems

Mergers, acquisitions, and growth also lead to using and implementing many different ERP solutions and antiquated legacy software that are forced to communicate with each other using painful manual efforts. A major problem arises from the fact that these systems operate across numerous lines of businesses, all with different siloed data. By having so many siloed systems that could be compromised with harmful data, these disparate data sources leave banks and other financial institutions exposed to unnecessary risk.

Different departments have different needs, so it makes sense that they would use different solutions, but it’s important that those solutions pull from a single source of truth in order to prevent the types of data inaccuracies that lead to vulnerabilities.

Final Thoughts

Closing the holes in your data integrity is the most proactive way a financial institution can defend against fraud. As hackers get increasingly creative and aggressive, it becomes even more critical that organizations have a trusted Data Foundation to base their decisions on. This can be achieved by ensuring that siloed systems are powered by consistent and accurate data from a single reliable source.