There is a strong likelihood that when one bank is targeted in a particular fraud scheme, other institutions will targeted as well. This is largely attributable to the rise of organized criminal enterprises, which take a calculated, big-picture approach to fraud.
In response, calls for industry-wide consortia and shared intelligence platforms have grown. The premise is straightforward: if the first financial institution to fall victim to a scam alerts its peers, the industry as a whole can better identify and counter the activities of fraud rings and cybercriminal syndicates.
There is merit to this approach. However, as Lucas Olson, Fraud Management Analyst at Javelin Strategy & Research, examined in the Sharing is Caring: The Fraud ‘Consortium’ Effect report, there are still many lingering obstacles to effective, industry-wide fraud data sharing.
Beyond the logistical challenges of building and maintaining an accurate, up-to-date intelligence platform that serves tens of thousands of financial institutions, many banks remain reluctant to share customer data due to privacy, regulatory, and competitive concerns.
Despite these challenges, the benefits of data sharing—and the growing threat of fraud—make it imperative for financial institutions to pursue greater industry cooperation.
Finding a Regulatory Shield
Two of the most frequently cited barriers to data sharing are compliance risks and customer data protection.
For example, some banks have worried that flagging an individual as a fraud risk could violate consumer protections under the Fair Credit Reporting Act, which imposes requirements on the reporting and sharing of certain consumer information.
To address some of these concerns, FinCEN’s Section 314(b) framework provides a mechanism for eligible financial institutions to share information related to suspected money laundering, with certain liability protections. Its potential role in broader fraud intelligence sharing will depend on how institutions interpret and apply the framework.
Those protections aren’t automatic. Institutions must meet the program’s eligibility and procedural requirements. As the industry continues to navigate the framework, questions remain about its practical implications for data sharing.
That uncertainty is only amplified by the rapid evolution of fraud schemes.
“A regulatory issue that still comes up is that when you’re sharing across banks, you have different taxonomies that are in place,” Olson said. “How you define a particular type of first-party fraud here is different than over here. You’re trying to integrate these systems and they don’t work because there’s no standardized definition for some of these things across the industry. There’s a lot of regulatory issues that make this difficult.”
Framing Data Sharing for Customers
Consumer privacy concerns also make financial institutions cautious about data. These concerns have become more pronounced amid continued reports of data breaches and leaks.
As a result, many customers are uncomfortable with their information being shared across institutions, with third-party vendors, or with systems that use artificial intelligence.
Emerging technologies, however, may help strengthen privacy protections. Hashing and tokenization, for instance, can allow banks to match or exchange certain fraud-related information without directly exposing underlying personal identifiers.
Even with these safeguards, banks must be transparent about what information they share, why they share it, and how they protect it.
“It’s the framing of it,” Olson said. “Customers oftentimes are nervous about sharing, but it’s an opportunity for financial institutions to build trust if they’re communicating in customer-centric language. Yes, we’re sharing your data but here’s why you should be okay with it because it’s helping to protect you and your friends, neighbors, and family. It’s actually an opportunity to build trust.”
Finding the Right Data-Sharing Model
Regulatory and privacy concerns are not the only barriers. Some banks are also wary that competitors could use shared information to gain an advantage.
Cost is another consideration. Many data sharing networks charge fees, prompting institutions to weigh the expense against the potential benefits. These concerns are legitimate—not all shared intelligence creates the same value.
“More data is not better, like if you’re getting information about the banking industry in Europe but you don’t have any operations there,” Olson said. “You have to find the right representative data for the specific use case that you have in mind, then you have to staff for integrating a new process.”
Even banks willing to participate can face additional hurdles. Data-sharing models vary widely and continue to evolve. In the U.S., the fraud intelligence ecosystem is highly fragmented, making it challenging for financial institutions to identify the right solution for a particular use case.
This is evidenced by the many ownership structures for consortiums. For example, Visa and Mastercard offer tools and services that support the exchange of fraud-related intelligence across their networks.
Other models include industry groups like Early Warning Services, and government-led models such as the one created through FinCEN 314(b) which operate consortiums.
Fraud prevention and cybersecurity fintechs have also entered the market with network-based intelligence solutions. While these offerings were once more distinct from traditional consortium models, the lines between shared intelligence networks, commercial platforms, and other fraud prevention tools have increasingly blurred.
“There’s no consistency in how these terms are used,” Olson said. “Over time, the focus of these different sharing networks has become increasingly specific. You’ll have a fraud vendor that’s says we’re going to be the expert on check fraud, and we have our consortium that’s built specifically for check fraud.”
“Increasingly, vendors are coming out with the so-called definitive consortium network intelligence for first-party fraud, scams, or money laundering,” he said. “The list is getting longer and longer; everyone has their specialty that they’re focusing on and it’s highly fragmented.”
Staying True to the Mission
The escalating fraud threat makes greater industry collaboration increasingly essential. The sophistication and scale of modern fraud will only continue to drive demand for more coordinated approaches.
Ultimately, participating in a shared intelligence network can support banks’ core responsibility to safeguard their customers’ funds and information. Rather than allowing concerns about risk to stall progress, financial institutions should work toward stronger industry collaboration, with appropriate safeguards and clear expectations for participation.
That effort must remain adaptable, however. Fraud tactics, technologies, and regulatory expectations will continue to evolve, requiring data-sharing models to evolve with them.
“These outdated stereotypes of what this used to look like are just not applicable anymore,” Olson said. “Especially in the last couple of years, these lines between network intelligence and data consortiums have collapsed. If a bank thinks they understand how this works, they probably don’t. Things have changed and will continue to change. This is a rapidly moving area.”