When it comes to fraud prevention, sharing is caring for financial institutions.
News broke this month that a cybercriminal gang using Airbnb rentals as mini call centers for a years-long run of bank fraud and phishing campaigns across Europe was apprehended. The incident shows that companies within security-critical sectors like banking and payments are wising up to the benefits of data sharing across their ecosystems to combat fraud.
As 2025 nears, embracing a consortium approach to data sharing is emerging as a way to beat back a rising tide of cybercrime and bank fraud.
By combining anonymized data from multiple sources, consortiums can spot suspicious patterns that would otherwise fly under the radar. It’s like putting together a jigsaw puzzle of fraud attempts that allows financial institutions to predict and block fraudulent activity faster than would be possible alone.
There’s a catch, however. Data sharing comes with its own set of headaches, from privacy concerns to regulatory hurdles. But financial institutions are working overtime to ensure that the data is clean, secure and compliant with the alphabet soup of regulations (such as CFPB, CCPA, GDPR, PSD2, DORA, etc).
Ultimately, against an increasingly dynamic and sophisticated backdrop of fraud and cybercrime, by combining their insights, financial institutions are not only protecting themselves but also strengthening the broader financial ecosystem against systemic vulnerabilities.
Read also: BEC and Phishing Attacks Surge, Exploiting New Domain Names
Fraud is no longer a one-bank problem. Criminals operate across institutions, exploiting the lack of cross-bank visibility. A siloed approach doesn’t work anymore, and data-sharing consortiums aim to bridge this visibility gap by pooling anonymized data on fraud attempts and successful breaches from member institutions, which can include banks, FinTechs and sometimes government agencies.
According to the PYMNTS Intelligence report “The State of Fraud and Financial Crime in the U.S.: What FIs Need to Know,” scam-related fraud jumped 56% in 2024. As a result, 71% of financial institutions have adopted fraud score solutions, elevating their ability to identify potentially fraudulent transactions before they are completed.
When multiple organizations share data, patterns that would otherwise remain invisible emerge. For instance, a fraud ring using the same IP address to target different banks would likely go undetected in isolation. But a consortium can flag that IP as suspicious in real time.
“You could have a database of information that an issuer could ‘ping’ against to say, ‘Haven’t we seen this bad actor before?’” Thredd CEO Jim McCarthy told PYMNTS in February. “…There’s an opportunity to do similar data sharing on the merchant side.”
In April, Intellicheck CEO Bryan Lewis advocated for the development of consortiums and data sharing to bolster identity verification. By pooling resources and sharing verified data, consortiums can establish a robust framework for identity validation, bolstering trust and confidence in financial transactions.
“If I am dealing with someone in a consortium, I know that they’ve received a stamp of approval,” Lewis said, stressing that security of information and personal details should be a paramount consideration.
See also: Attack Vectors 2024: Identity Theft and Digital Banking
The success of data-sharing consortiums depends on widespread adoption and the willingness of institutions to collaborate.
While the benefits are becoming clearer, data-sharing consortiums still face several hurdles. Banks are traditionally competitive entities, and convincing them to share potentially sensitive data requires building a high degree of trust.
“Companies and enterprises are increasingly facing a dilemma between how much they want to leverage their data versus how much they want to keep it secure and protected,” Pyte CEO and founder Sadegh Riazi told PYMNTS in June.
Financial institutions can adopt privacy-enhancing technologies (PETs), such as homomorphic encryption, differential privacy and federated learning, which enable data analysis without exposing sensitive information. Federated learning allows institutions to train machine learning models collaboratively while keeping raw data local.
A consortium is only as strong as its data pool. If large banks dominate or smaller institutions opt out, the insights generated may be incomplete. Designing incentive mechanisms, such as reduced consortium fees or shared fraud prevention rewards, can also help to encourage participation and build trust.
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