Modern Fraud Prevention Requires Scalable, Low-Latency Connectivity

Financial crime, such as payment fraud, account takeover and money laundering, is more than just a drain on the global economy. For banks and other financial enterprises, it can lead to lower customer satisfaction, damage to reputation, and heavy regulatory fines. In fact, global penalties for failing to comply with anti-money laundering (AML), know your customer (KYC), sanctions and customer due diligence (CDD) regulations totaled $3.8 billion in 2025.[1]

Banks and their partners have been using machine learning and predictive analytics as part of their fraud detection efforts for many years now. However, financial fraud is a constantly evolving threat, and  fraudsters are getting more sophisticated all the time. According to one report, more than 50% of financial fraud involves the use of AI.[2] In particular, criminals are applying many different generative AI tools in their fraud efforts, including deepfakes, synthetic identities and AI phishing scams.

It’s only logical for banks, financial enterprises and fintechs to fight fire with fire by adding advanced AI capabilities of their own. But for many companies, understanding the value of AI and actually using it at scale are two different things altogether. There are many challenges that financial firms will face as they attempt to move AI from testing into production.

What are the infrastructure requirements for future-proof fraud detection with AI?

Financial data is increasing in volume, and it’s often difficult for large banks and financial enterprises to manage all that data in a unified, consistent manner. This is especially true now that data is typically fragmented across hybrid multicloud environments, leading to data sprawl. And this sprawl doesn’t just occur within the bank’s own infrastructure; enabling AI also requires accessing data from a variety of ecosystem partners.

Data is the fuel that makes AI run, and a fraud detection model can only be as good as the data you feed into it. To gain an advantage over the fraudsters, banks must be able to quickly move large volumes of data across many different endpoints. In short, fraud detection has become a matter of network modernization.

To handle the massive volume of distributed data that today’s fraud detection models need to process, large banks and financial enterprises can’t rely on legacy data infrastructure that’s too rigid and slow to keep up. They need a consistent, holistic approach to data management, backed up by scalable, low-latency connectivity across all their data sources.

An authoritative core provides better control over data

To start, it’s essential that they maintain control over their data. If they move data into the cloud, they must do so without putting themselves at risk of vendor lock-in. They can achieve this by deploying an authoritative data core in a cloud-adjacent neutral environment.

This means they can move copies of their data into the cloud to access services such as analytics tools, while still maintaining authoritative data copies on infrastructure that they control. This can help ensure the free flow of data throughout their hybrid multicloud architecture.

Interconnection provides private, high-performance connectivity

Banks also need to ensure scalable, high-performance connectivity between their different environments and endpoints. They won’t be able to do this if they rely on traditional physical network infrastructure. A virtual interconnection solution can give banks the flexibility they need to scale up bandwidth on demand or add new connections quickly.

Interconnection also provides reliability and privacy that the public internet can’t match. An interconnection is a direct, point-to-point link between two parties, so there’s no risk of data being exposed in transit. In contrast, the internet is an inherently public transmission medium, and it’s impossible to control the exact route that data traffic will follow. Therefore, any company that moves sensitive financial data over the internet puts themselves at risk of non-compliance with data sovereignty and privacy regulations.

Edge infrastructure helps keep latency low

Large banks and financial enterprises also need to process fraud detection workloads in near-real time, and that means they need to optimize for low latency throughout their network. Solving for latency essentially boils down to proximity. Physical distance is the underlying cause of all latency, so they must ensure they’re deploying fraud detection workloads in close proximity to data sources and their AI ecosystem partners at the edge, while still ensuring physical security.

Case study: Block pursues AI with help from Equinix and NVIDIA

In 2025, Block announced that it would deploy the NVIDIA DGX SuperPOD with DGX GB200 systems inside an Equinix data center, becoming the first company in North America to do so.

The company plans to use this technology to support the research and training of open-source generative AI models with novel capabilities in underexplored areas. Block has previously conducted AI research on anti-fraud measures such as deepfake detection.

By deploying inside an Equinix data center, Block can pair the power of NVIDIA hardware with low-latency edge connectivity to thousands of partners and service providers in the Equinix ecosystem.[3]

Why Equinix for fraud detection?

Banks and anti-fraud specialists need a digital infrastructure partner that can help them address all the data challenges outlined above. Equinix is uniquely positioned to be that partner, for the following reasons:

• A neutral, cloud-adjacent infrastructure environment: When customers deploy their authoritative data core in an Equinix colocation data center, they’ll have access to our industry-leading portfolio of native cloud on-ramps. Thus, they’ll be able to quickly move their data into multiple clouds as needed, while still maintaining control over their data at Equinix. Cloud on-ramps are accessible via Equinix Fabric®, our virtual interconnection solution. This means that customers don’t even have to be physically colocated with their chosen cloud providers to connect with them.
• Access to financial ecosystem partners: The Equinix ecosystem is home to thousands of enterprises and service providers, including many specialists in the financial services industry. Equinix customers can easily connect to their partners and service providers from wherever they’re located, using their choice of physical cross-connects or Equinix Fabric virtual connections.
• Global reach: Equinix has 280 data centers spread across 77 strategic markets on six continents. This makes it easy for our customers to deploy infrastructure in proximity to all the different places they’ll need to collect and process fraud prevention data.
• Physical security: All Equinix data centers feature a multi-layered security perimeter and continuous monitoring to prevent unauthorized access to customer equipment.

Case study: Outseer optimizes fraud detection by reducing network complexity

While some big banks attempt to meet their own fraud detection requirements via data-intensive AI applications, service providers that specialize in fraud detection typically take a different approach. This involves taking in data from their customers and running that data through fraud detection applications hosted in strategic locations. They need a global infrastructure foundation to help make this happen.

Outseer, a global leader in digital fraud prevention, is one example of this. The company utilized Equinix’s global reach to establish a presence in several markets. As shown below, Outseer now has data and applications hosted in Equinix colocation data centers in multiple locations in both the U.S. and Europe. As a result, the company was able to achieve proximity to its customers and partners to ensure low-latency processing.

The company connects these different locations via Equinix Fabric virtual connections, rather than continuing to rely on outdated MPLS networks. It also achieved significant cost savings with a simplified network architecture that reduced the required number of VPNs. Finally, the transformation set the stage for Outseer’s future adoption of cloud-based solutions and hybrid architectures.

Outseer interconnects its global fraud detection infrastructure with Equinix Fabric

To learn more, read the full case study: Equinix enables Outseer to optimize fraud detection, connectivity and costs.

[1] Global financial regulatory penalties fall by 18% in 2025 as enforcement shifts from US to EMEA and APAC, Fenergo, January 13, 2026.

[2] More than 50% of fraud involves the use of artificial intelligence, Feedzai, May 30, 2025.

[3] Block Becomes First Company in North America to Deploy the Latest NVIDIA GB200 Systems for Frontier Models, Block, March 12, 2025.