Why banks need network-level fraud intelligence
“A major part of today’s fraud problem is that individual banks only see their own fraud patterns.”
Financial criminals operate across borders and institutions, often reusing the same mule accounts, scam tactics and laundering patterns against multiple banks. One of the biggest challenges in fraud prevention is that most banks only see threats inside their own systems. When banks share fraud intelligence, a broader network-level view of criminal activity becomes visible.
Fraud intelligence sharing helps banks detect threats earlier and respond faster. When one institution identifies a new scam pattern – such as token fraud or coordinated mule account activity – anonymised insights can update fraud rules and AI models across the wider platform. This reduces the time criminals have to repeat the same attack elsewhere.
Shared fraud intelligence improves detection accuracy and lowers costs
Shared fraud signals improve detection accuracy and reduce false positives. Data pooled across multiple financial institutions strengthens AI risk scoring by helping banks separate genuine threats from normal customer behaviour. Shared signals can also help banks identify mule herders, suspicious inbound flows and sanctioned evasion routes more effectively.
Banks can also reduce manual investigations and lower operational costs linked to fraud analysis and monitoring. Pilot programmes show that this consortium-based approach can accelerate fraud analysis and help prevent losses worth billions.
These results are driving wider adoption of consortium fraud prevention models across the UK, the Netherlands and Estonia. Regulatory support is also increasing. Article 75 of the EU’s AML Regulation allows banks to share selected risk signals to strengthen financial crime prevention. Early results are promising, including identifying more connected subjects during investigations and preventing fraud that might otherwise go undetected.
Shared banking platforms strengthen fraud prevention
Tieto Banktech operates a shared multi-tenant fraud prevention platform used by more than 130 banks and fintechs across Europe. Because institutions operate on the same underlying platform – particularly for card transaction monitoring and fraud prevention – anonymised fraud intelligence can strengthen detection across the wider network. Tieto Banktech also participates in major financial crime prevention organisations and industry forums to stay aligned with evolving threats, best practices and regulatory expectations around intelligence sharing.
“Thanks to our networked intelligence, we offer banks an industry-leading fraud detection rate of up to 95% of all fraud committed.”
When Tieto Banktech’s AI identifies new fraud patterns or emerging threats within one institution, our fraud specialists analyse the activity and update detection rules, risk profiles, AI models and fraud scenario templates across the platform. No raw customer data is shared. Intelligence sharing happens through anonymised patterns, rules and risk signals in compliance with GDPR, AMLD and PSD2 requirements.
Network-level fraud intelligence strengthens bank defences
Updated fraud rules and intelligence automatically strengthen protection across the platform, turning isolated fraud events into system-wide defence. This helps banks respond faster to emerging threats while improving detection rates for known fraud patterns. Thanks to networked intelligence across the platform, Tieto Banktech helps banks detect up to 95% of fraud.
Other benefits of networked intelligence include fewer false positives and stronger card transaction monitoring as banks improve fraud detection across both domestic and international payments. Shared intelligence can also reduce operational costs by lowering the need for large in-house threat intelligence teams.
When banks outsource fraud operations to Tieto Banktech, the benefits of shared intelligence become even stronger. Our specialists monitor multiple institutions around the clock to identify network-level fraud patterns – such as coordinated mule account activity – that would be difficult for a single bank to detect alone. To continuously strengthen fraud prevention, Tieto Banktech is developing advanced human-supervised AI models, including the forthcoming Atlas Large Financial Model. Atlas is being developed using aggregated and anonymised banking data to help predict fraudulent behaviour and improve fraud detection using broader cross-bank and industry-wide intelligence patterns.
