What is Network Intelligence and How Can It Prevent Fraud and Financial Crime?

Fraudsters share their latest scams just as easily as social media influencers share makeup tutorials or cooking tips. These criminal networks are fast, adaptive, and costly – hittingUS victims for more than $16.6 billion in 2024 alone.¹ To fight back, banks and financial institutions need a smarter, more connected approach to fraud defense. That’s the promise of network intelligence. It’s all about quickly analyzing data to understand fraud threats across the whole system.

This comprehensive guide explores how network intelligence, powered by data orchestration, provides the visibility and analytical capabilities to combat complex fraud networks and protect financial services across multiple institutions.

Key Takeaways

• Network intelligence uses smart tech like AI to catch fraud patterns that often get missed.
• Sharing data to fight fraud is often difficult because the data is spread out and poorly organized; it also raises critical privacy and security concerns.
• Data-related issues are a key challenge for many banks using AI, according to Feedzai research—with 87% citing data management as a key issue.
• Network intelligence, especially with federated learning, helps improve how we train AI and makes it better at finding fraud.
• If banks use network intelligence effectively, they can lose less money to fraud, find new scams proactively, make fairer AI, and work better with others.

How Does Network Intelligence Work?

Network intelligence (NI) uses advanced analytics like AI and machine learning to analyze complex patterns across vast global datasets. This analysis empowers the network to “see” or understand subtle anomalies and sophisticated fraud schemes that traditional methods often miss, and make sense of all the data flowing through it.

Individual financial institutions have a limited risk view based on their specific exposure. However, network-level visibility offers a broader perspective that exceeds the insights limited to individual FIs.

 

Think of it as giving your network a brain: it doesn’t just move information from point A to point B. Instead, it collects, analyzes, and interprets what’s happening inside that traffic. If NI is a brain for your network, think of data as its brain food. In today’s world, there’s a massive amount of data—AI thrives on data and is always hungry for more. 

In this sea of data, the question is not how to cast a wide net. It’s how to catch the right fish? That’s where NI comes in. NI equips a network with the capacity to perceive, comprehend, and derive meaning from its entire data stream. 

How Does Network Intelligence Improve Current Data Sharing Models?

Many financial institutions engage in data sharing or consortium data models. However, standard approaches are often siloed and fragmented. Everyone has their own pile of information, and it’s hard to put it all together. Examples include:

• Bank-to-law enforcement: Banks collaborate with law enforcement to investigate suspicious activity linked to crimes. This partnership is crucial for building cases, arresting criminals, and dismantling criminal networks.
• Bank-to-bank: Financial institutions can directly share information on suspicious activity with other banks on a case-by-case basis, following established legal and security protocols. 
• Bank-to-vendor consortia (e.g., shared databases): Vendor consortia pool anonymized bank data, offering a broad view of threats. Member banks receive valuable insights, improving their ability to detect and prevent advanced attacks.
• Industry consortiums (e.g., FS-ISAC): Financial service consortiums like FS-ISAC share threat intelligence, best practices, and security alerts and coordinate with government and incident responses.

These mechanisms facilitate the exchange of information between various entities, enhancing their ability to detect, prevent, and investigate illicit activities. 

What Are the Top Data Challenges in Using Network Intelligence?

Data is the fuel that network intelligence runs on. But here’s the problem: many banks and businesses struggle to access and use the correct data effectively. In addition, there are barriers to sharing data due to the heterogeneous formats, quality, integration challenges, and privacy concerns. Feedzai’s 2025 report found that only 34% of financial pros use consortium data, which is really important for making AI better at finding fraud. 

Our research shows that data issues are the most significant industry problem when using AI to prevent fraud.

• 87% of respondents say their biggest AI challenge is managing data accurately as digital transactions increase.
• 61% cite data privacy as their top data concern.
• 60% wish regulators understood that data quality is key to effective AI.

Network Intelligence is a key asset in the fight against fraud and financial crime. It allows banks to embrace intelligence sharing without worrying about common data concerns.

Why is Network Intelligence Important?

Network intelligence helps banks and businesses fight back against fraud by letting them share information safely and spot patterns they couldn’t see before. 

Here’s how it changes the game for banks.

• Access to Federated Learning: Federated learning significantly enables collaborative model training without compromising data privacy or centralizing sensitive information. Banks can leverage network intelligence into their AI models without sharing sensitive customer data, collectively improving model accuracy and reliability by expanding across more diverse datasets. 
• Actionable Insights: Use actionable insights that point to the likelihood of fraud and financial crime. NI can identify suspicious patterns and anomalies that traditional rule-based systems might miss by connecting diverse data points within vast datasets. This proactive approach enables earlier detection and intervention, minimizing potential losses and improving security.
• Confirmed Outcomes: Access to confirmed fraud data, combined with anonymized network insights, significantly enhances financial institutions’ fraud defenses by enabling them to proactively identify emerging patterns and evolve attack techniques.
• Look Beyond Your Own Data: It’s essential to look beyond the data connections at your organization. Access to an extensive, global data network spanning diverse use cases enables more comprehensive data processing. This, in turn, allows for more accurate insights that can significantly enhance risk strategies across the industry.

What are the Key Benefits of Network Intelligence?

Using network intelligence, enhanced by AI and federated learning, AI algorithms can analyze vast datasets in real time, identifying subtle patterns and anomalies that would otherwise go unnoticed. This understanding enables your organization to reduce fraud and uncover complicated fraud and scams that are otherwise hard to spot. 

For banks and financial institutions, key benefits of network intelligence include

Less Money Lost to Fraud

By identifying fraudulent activities and actors more accurately and in real time through analyzing interconnected data points, banks can proactively prevent successful fraud attempts, thereby minimizing financial losses.

Share Information Safely

Employ privacy-preserving techniques like anonymization, aggregation, and federated learning to share valuable insights about emerging fraud trends and threats without exposing sensitive customer data.

Uncover Complex Fraud Schemes 

Network intelligence excels at uncovering sophisticated fraud rings and multi-layered schemes by revealing hidden relationships and patterns across seemingly disparate entities that traditional, siloed analysis would miss.

Enhanced Customer Experience 

By strategically integrating and orchestrating data from disparate silos, network intelligence leverages data orchestration to create a comprehensive, unified view of customer interactions and transactions, enabling the detection of subtle connections indicative of fraud that would be missed in fragmented data.

Greater Operational Efficiency

Automating the detection and prioritization of high-risk activities through network intelligence frees fraud analysts to focus on complex investigations rather than sifting through numerous false positives, improving overall team productivity.

Better Compliance with Regulatory Requirements

Network intelligence features assist in adhering to strict KYC and AML requirements. They achieve this by offering a holistic understanding of customer connections and transaction movements, thereby enabling the detection of anomalous behavior.

Informed Decision-Making

The insights gained can guide everything from customer experience improvements to compliance and risk management.

Strengthened Privacy

Decentralizing sensitive data is unnecessary with federated learning. Instead, models undergo local training, and only model updates are shared, substantially lowering privacy vulnerabilities.

Reduced Bias

Incorporating data from diverse sources and demographics through federated learning can reduce bias in fraud detection models, resulting in fairer and more equitable results. This ultimately leads to more trustworthy and ethical AI-powered fraud prevention solutions.

Improved Industry Collaboration

Financial entities can pool collective intelligence without directly sharing sensitive customer data by enabling collaborative model training across decentralized datasets. The continuous learning nature of federated systems allows institutions to adapt more rapidly to evolving fraud trends, ensuring their defenses remain cutting-edge.

Shifts in the Regulations Require Enhanced Network Intelligence

Recent regulatory changes across the globe are placing greater emphasis on secure data handling and collaboration among financial institutions. These shifts necessitate enhanced network intelligence capabilities to ensure compliance while combating fraud and financial crime effectively.

• GDPR (General Data Protection Regulation): The EU-wide GDPR initiative includes strict rules for data processing and sharing within the EU, requiring financial institutions to implement robust security measures and obtain explicit consent for data sharing. This requires secure intelligence-sharing frameworks that preserve data privacy while enabling the exchange of crucial information for fraud prevention.   
• CCPA (California Consumer Privacy Act): Similar to GDPR, the CCPA grants California citizens greater control over their data, limiting how financial institutions can collect and share information.  To comply, institutions must adopt secure methods for sharing intelligence, such as anonymization and aggregation, to detect fraud patterns without violating individual privacy rights.   
• PSD3 (Payment Services Directive 3): PSD3 aims to modernize payment services and enhance consumer protection in the EU, emphasizing the need for secure data exchange to combat payment fraud. It encourages collaboration and intelligence sharing among payment service providers to identify and mitigate emerging threats while upholding strong security standards.   
• Brazil’s Resolution Number 6: Brazil’s central bank is focused on cybersecurity and data protection. They require banks to have systems for sharing information about fraud and security incidents so everyone can work together.
• Australia’s Scam Safe Accord: In Australia, banks, phone companies, and the government are teaming up to fight scams. They know that sharing information about how scams work and who’s being targeted is key to stopping them.

The Power of the Network

Just as fraudsters form networks to share tactics like influencers sharing trends, financial institutions must leverage the power of their networks to combat fraud. Network intelligence is the gateway to leveraging this crucial advantage, enabling institutions to collaborate, analyze data collectively, and stay ahead of fraud trends. 

Related Resources:

• Blog: How AI is Helping Fight Back Against Financial Crime
• eBook: The TRUST Framework: Responsible AI for Innovation
• Solution Sheet: Feedzai as a Service
• Solution: AI that Goes Beyond the Ordinary