Digital forensics investigations: Analyzing the data behind complex criminal schemes

A deleted file isn't gone, it's just harder to find. Every crime with a digital footprint leaves a trace: a login timestamp, a wire transfer, a fragment that survives the delete key. A digital forensics investigation is how investigators turn those traces into a case that holds up in court.

A lone bad actor may be easy to catch: follow the trail, find the person. Organized networks are the real problem, as they spread that same trail across dozens of devices, platforms, and countries, buried under encryption and fake identities built to survive exactly investigators’ scrutiny. And there's more of it every year.

The FBI's Internet Crime Complaint Center reported more than $20.8 billion in losses from cybercrime in 2025 alone, a 26% jump from the year before. That number covers a wide range of activity, compromised accounts, stolen data, fraudulent wire transfers, and behind each entry is a specific trail an investigator has to reconstruct. The main challenge isn't always a shortage of evidence. More often, it's too much of it, scattered in pieces that don't obviously connect.

Data analytics is changing that. Teams that once spent weeks cross-referencing spreadsheets and emails by hand can now surface the same connections in hours. This piece covers what a digital forensics investigation actually involves, why it can be challenging even for experienced teams, and how modern analytics tools are helping investigators keep pace with schemes that keep getting more elaborate.

Most investigations move through five stages, in roughly this order:

• Identification: Investigators track down every potentially relevant data source: laptops, phones, servers, cloud accounts, transaction logs, chat platforms, and more.
• Data preservation: Before anyone touches the data, they capture it in its original state, often creating forensically sound images or backups of the original data sources to allow analysis without risking any modification of the original evidence.
• Analysis: Investigators dig through the preserved data looking for suspicious activity, hidden relationships, and patterns that explain how the scheme worked.
• Documentation: Every tool used, finding, and new piece of evidence gets documented, along with a clear record of who handled it and when
• Presentation of findings: Compiling all findings, documentation, and relevant evidence into a  report that can be used for legal proceedings, disciplinary actions, organizational improvements, or any other required purposes. 

The analytics phase of a digital forensics investigation often presents the highest degree of complexity. This is the stage when investigators put all the pieces together and uncover the true mechanics of how a scheme unfolded, work that gets harder as cases span more borders, generate more data, and pull in more formats that don't talk to each other.

A criminal case rarely arrives as a clean data set, it shows up as a phone extraction, a spreadsheet of wire transfers, a folder of scanned bank statements. Investigators have to make all of it agree with itself before they can prove anything, and the following challenges make that harder than it sounds:

Criminal networks are constantly building multi-layered schemes to cover their tracks. Sophisticated criminal cases may involve a web of accounts and transactions across multiple countries, the use of shell companies, synthetic identities, and more.

Untangling that web of activity is like following a trail of breadcrumbs, as investigators have to make sense of a staggering amount of data to reconstruct what actually happened.

Case volume is up, but the bigger shift is where that evidence lives. Each case now pulls from more places at once, transaction logs, chat exports, cloud accounts, device extractions, where a decade ago it might have been one or two. More sources per case means more places for a connection to hide.

The scale of the problem shows up clearly in Internet Crime Complaint Center’s numbers: complaint volume has grown from under 50,000 in 2001 to over one million in 2025, a more than 20 times increase over roughly two decades. Many of those complaints can generate its own trail of accounts, devices, transactions, and communications for an investigator to work through.

A single case might combine structured transactional data, such as bank transfers or ledger entries, with unstructured data that doesn't fit neatly into rows and columns:

• Emails, chat logs, and messaging app exports
• Call transcripts and voice recordings
• Photos, video, and social media activity
• Scanned documents and free-text notes

Most legacy forensic tools were built for the structured side of that list. Unstructured data takes more work to parse, tag, and connect back to the rest of a case, which is exactly where investigators lose time. For complex schemes, investigators have to piece together these different formats without missing details buried in one silo or another.

The same suspect can look like several different people across a case's data: one name spelling on a device, a different one in a financial record, a transliterated version in an international database. Matching those records to a single real identity is its own discipline, known as entity resolution or identity resolution. The problem compounds in cross-border cases, where names transliterated from scripts like Cyrillic or Arabic can appear multiple different ways across systems that were never built to reconcile them.

Without resolving those matches, two records describing the same person can sit in a case file without anyone realizing it, and a connection that should have been obvious gets missed simply because nothing ever linked the records together.

Digital evidence rarely means anything on its own. A login timestamp doesn't mean much on its own, it needs the IP address that generated it and the file that got altered right after. Match a file hash against the same hash on a different device, and suddenly two unrelated devices are connected. A phone number in a chat export stays anonymous until it's linked to the account that owns it and the person that account belongs to. Evidence only becomes useful once investigators establish how these pieces relate to each other.

None of those relationships live in any single record. Pulling everything into one case file puts the evidence in the same room, but it doesn't draw the lines between it. That’s a visualization problem, and it only gets harder as a scheme grows: the more accounts, devices, and jurisdictions a case touches, the more of those connections stay invisible in a table or a list, even when every piece is technically already there in the data.

None of these five problems show up in isolation. A real case is usually complex, high-volume, and unstructured, with duplicate identities and invisible connections, all at the same time, which is exactly why fixing them one at a time rarely works. The common thread underneath all five: the evidence almost always exists somewhere in the case file. The real question is whether anyone can see how it connects before the case goes cold.

Most traditional tools and methods weren't built for cases that touch the increasing volume of devices, formats, and jurisdictions at once. What closes that gap isn't just faster versions of the same manual steps, it's technology that maps to the actual shape of an investigation: pulling evidence out of unstructured formats, unifying data scattered across separate systems, resolving identity automatically, and surfacing connections no single record shows on its own.

A meaningful share of digital evidence never sits in a clean, structured record. It's in scanned documents, chat exports, emails, and reports, formats most forensic tools were never built to read directly. 

AI-powered processing tools, such as Nuix Neo, close that gap by extracting names, addresses, account numbers, and other identifying details straight out of documents, turning files that only a human could open one at a time into data that can actually be searched and connected.

Making each source searchable on its own only solves half the problem. The other half is that more sources per case means more places to search separately, unless something pulls them together. 

Modern investigative platforms close that gap by pulling structured and unstructured data from systems that are already in place, such as device extractions, transaction logs and chat exports, into a single connected view, instead of leaving investigators to open each system separately and stitch the results together by hand. 

Nuix Neo does this at the ingestion layer, rapidly pulling in data from disparate sources across more than 1,000 file types while maintaining the chain of custody and auditable workflows that keep the resulting evidence usable in court.

Before evidence can be connected, it has to be recognized as belonging to the same person or entity in the first place. 

Platforms that offer entity resolution handle that matching. Once two records are matched to the same real identity, every account, device, and transaction tied to either version becomes visible under one entity, instead of sitting scattered across records that were never linked.

A graph data structure holds not just individual data points but the relationships between them, structured as nodes, the data points, connected by edges, the relationships linking them. With that, investigators can visually explore and analyze which accounts, devices, or people connect, and how, instead of scanning tables for a match.

In an investigative context, tools like Linkurious Enterprise are purpose-built to explore the networks around fraudsters, cybercriminals, and other wrongdoers. In a financial crime case, for example, that means querying the data directly to see which accounts money is flowing to and from, in what quantities, and within what timeframe. 

Indirect relationships surface too, expanding across several levels of connections instead of stopping at the first hop, which makes it possible to trace a full network rather than just its individual members. Those same connections probably wouldn’t show up when each piece of evidence is reviewed in isolation.

What used to mean days of tab-through-tab tracing across separate systems can surface in a single query once the data is structured this way. Having graph visualization and analytics as part of the investigation makes those connections something an analyst can understand quickly and intuitively, rather than infer from a spreadsheet.

Every investigation is bounded by what its tools can actually surface. When a connection goes unnoticed, the cost isn't measured in extra hours, it's a case that never gets proven, or a network that walks away intact because nothing tied its pieces together in time.

That cost is what Nuix Neo and Linkurious' joint solution is built to prevent. By combining Nuix Neo's data ingestion and chain of custody management with Linkurious' graph analysis, the integration pulls meaning out of evidence that used to require hours of manual work, creates a single source of truth, and shines light on connections that would otherwise stay buried in evidence nobody thought to compare.

Global organizations are already using graph visualization and analytics to stay ahead of criminal networks. Deloitte Switzerland applies graph technology from Linkurious to forensic data analytics for faster, more efficient investigations, working through the same kind of layered, cross-border complexity described above. Read the case study to see how graph technology from Linkurious helps investigators move from fragmented data to a complete picture, faster.

Want to see how Nuix and Linkurious can help your team investigate faster? Get in touch with us.