[{"data":1,"prerenderedAt":904},["ShallowReactive",2],{"r9b2bZwFFy":3,"vnQZepDMow":59,"I0fx4WtnWI":127,"9trKdP2wkm":135,"JTbsVffizJ":748,"_apollo:default":903},{"post":4},{"id":5,"elementorData":6,"content":7,"layout":8,"title":9,"date":10,"featuredImage":11,"categories":16,"seo":23,"__typename":58},"cG9zdDozMjU2MA==","[{\"id\":\"bbddf13\",\"elType\":\"section\",\"settings\":{\"pp_animated_gradient_bg_color_list\":[{\"pp_animated_gradient_bg_color\":\"#F6AD1F\",\"_id\":\"ccbaf9e\"},{\"pp_animated_gradient_bg_color\":\"#F7496A\",\"_id\":\"f00c4c0\"},{\"pp_animated_gradient_bg_color\":\"#565AD8\",\"_id\":\"0f95668\"}],\"pp_display_conditions\":[{\"_id\":\"1d4c3ef\"}]},\"elements\":[{\"id\":\"53de0fc\",\"elType\":\"column\",\"settings\":{\"_column_size\":100,\"_inline_size\":null,\"pp_display_conditions\":[{\"_id\":\"32263a9\"}]},\"elements\":[{\"id\":\"0a8fe75\",\"elType\":\"widget\",\"settings\":{\"editor\":\"\u003Cp>\u003Cspan style=\\\"font-weight: 400;\\\">Complex investigations can take many forms: tracing a sophisticated \u003Ca href=\\\"https:\\/\\/linkurious.com\\/blog\\/insurance-fraud-investigation\\/\\\" target=\\\"_blank\\\" rel=\\\"noopener\\\">insurance fraud\u003C\\/a> ring, mapping an international money laundering network spanning \u003Ca href=\\\"https:\\/\\/linkurious.com\\/blog\\/real-estate-money-laundering\\/\\\" target=\\\"_blank\\\" rel=\\\"noopener\\\">real estate\u003C\\/a> and shell companies, or reconstructing \u003Ca href=\\\"https:\\/\\/linkurious.com\\/blog\\/digital-forensics-investigations\\/\\\" target=\\\"_blank\\\" rel=\\\"noopener\\\">digital evidence\u003C\\/a> from terabytes of data. But they often run into the same wall: harnessing vast amounts of \u003Ca href=\\\"https:\\/\\/linkurious.com\\/blog\\/unstructured-data-management-link-analysis\\/\\\" target=\\\"_blank\\\" rel=\\\"noopener\\\">unstructured data\u003C\\/a> and revealing critical connections hidden within the evidence.\\u00a0\u003C\\/span>\u003C\\/p>\u003Cp>\u003Cspan style=\\\"font-weight: 400;\\\">Traditional systems can readily process structured data. But valuable clues can also be buried in emails, case notes, witness statements, documents and other unstructured content. Extract and connect those details, and they can reveal relationships that would otherwise remain hidden.\u003C\\/span>\u003C\\/p>\u003Cp>\u003Cspan style=\\\"font-weight: 400;\\\">Unstructured data intelligence is what makes that extraction possible. Point it at a stack of documents, chat logs, or case notes, and it reads through all of it, pulling out the information buried inside. Extracting those details used to require someone reading every page by hand, but with unstructured data intelligence, free text turns into something an investigator can actually query.\u003C\\/span>\u003C\\/p>\u003Cp>\u003Cspan style=\\\"font-weight: 400;\\\">\u003Ca href=\\\"https:\\/\\/linkurious.com\\/decision-intelligence-platform-graph-analytics-ai-ml\\/\\\" target=\\\"_blank\\\" rel=\\\"noopener\\\">Graph technology\u003C\\/a> takes the next step: revealing how all that information connects. Every person, account, or other entity becomes a node. Every relationship between them becomes an edge linking two nodes together. Instead of examining each piece of evidence in isolation,\\u00a0 investigators visually explore the wider network at once, and a connection that would have taken hours to trace across a stack of spreadsheets shows up in a single glance.\u003C\\/span>\u003C\\/p>\u003Cp>\u003Cspan style=\\\"font-weight: 400;\\\">This article explores five areas where that combination can change how investigations are conducted: forensic data analytics, \u003Ca href=\\\"https:\\/\\/linkurious.com\\/fraud-investigation-solution\\/\\\" target=\\\"_blank\\\" rel=\\\"noopener\\\">fraud investigations\u003C\\/a>, \u003Ca href=\\\"https:\\/\\/linkurious.com\\/law-enforcement-and-intelligence-solution\\/\\\" target=\\\"_blank\\\" rel=\\\"noopener\\\">law enforcement\u003C\\/a>, \u003Ca href=\\\"https:\\/\\/linkurious.com\\/aml-investigation-solution\\/\\\" target=\\\"_blank\\\" rel=\\\"noopener\\\">anti-money laundering\u003C\\/a>, and investigative journalism. Across each, the principle is the same: unlock more of the information hidden in complex data, connect the dots, and give investigators a clearer picture of what they\\u2019re looking at.\u003C\\/span>\u003C\\/p>\",\"pp_display_conditions\":[{\"_id\":\"c1611b1\"}],\"pp_elements_tooltip_content\":\"Tooltip Content\"},\"elements\":[],\"widgetType\":\"text-editor\"}],\"isInner\":false}],\"isInner\":false},{\"id\":\"4c8ebd6\",\"elType\":\"section\",\"settings\":{\"pp_animated_gradient_bg_color_list\":[{\"pp_animated_gradient_bg_color\":\"#F6AD1F\",\"_id\":\"9335e34\"},{\"pp_animated_gradient_bg_color\":\"#F7496A\",\"_id\":\"b4d7b9b\"},{\"pp_animated_gradient_bg_color\":\"#565AD8\",\"_id\":\"45394ad\"}],\"pp_display_conditions\":[{\"_id\":\"c90057e\"}]},\"elements\":[{\"id\":\"1426fdd\",\"elType\":\"column\",\"settings\":{\"_column_size\":100,\"_inline_size\":null,\"pp_display_conditions\":[{\"_id\":\"7bf6693\"}]},\"elements\":[{\"id\":\"3d23fa0\",\"elType\":\"widget\",\"settings\":{\"title\":\"Forensic data analytics\",\"pp_display_conditions\":[{\"_id\":\"aad94a0\"}],\"pp_elements_tooltip_content\":\"Tooltip Content\"},\"elements\":[],\"widgetType\":\"heading\"},{\"id\":\"6e7901a\",\"elType\":\"widget\",\"settings\":{\"editor\":\"\u003Cp>\u003Cspan style=\\\"font-weight: 400;\\\">\u003Ca href=\\\"https:\\/\\/linkurious.com\\/blog\\/forensic-data-analytics\\/\\\" target=\\\"_blank\\\" rel=\\\"noopener\\\">Forensic data analytics\u003C\\/a>, the branch of digital forensics focused on finding patterns of criminal activity, only works once investigators have tracked down the sources that might hold relevant evidence, and that alone can be the hardest part of the process. It only gets harder as a case spans more borders, generates more data, and pulls in more formats that don't talk to each other. Typical sources include:\u003C\\/span>\u003C\\/p>\u003Cul>\u003Cli style=\\\"font-weight: 400;\\\" aria-level=\\\"1\\\">\u003Cspan style=\\\"font-weight: 400;\\\">Laptops and mobile phones\u003C\\/span>\u003C\\/li>\u003Cli style=\\\"font-weight: 400;\\\" aria-level=\\\"1\\\">\u003Cspan style=\\\"font-weight: 400;\\\">Servers and cloud accounts\u003C\\/span>\u003C\\/li>\u003Cli style=\\\"font-weight: 400;\\\" aria-level=\\\"1\\\">\u003Cspan style=\\\"font-weight: 400;\\\">Transaction logs\u003C\\/span>\u003C\\/li>\u003Cli style=\\\"font-weight: 400;\\\" aria-level=\\\"1\\\">\u003Cspan style=\\\"font-weight: 400;\\\">Chat platforms and communication records\u003C\\/span>\u003C\\/li>\u003C\\/ul>\u003Cp>\u003Cspan style=\\\"font-weight: 400;\\\">Even once every source is tracked down, investigators still need to make sense of what is often a fragmented mix of structured and unstructured data and the connections between them aren't spelled out anywhere.\\u00a0 A name mentioned in a chat, a date in a document, or an account number stored in a transaction record may all relate to the same person or event, with nothing to show they're the same thread.\u003C\\/span>\u003C\\/p>\",\"pp_display_conditions\":[{\"_id\":\"69d59f6\"}],\"pp_elements_tooltip_content\":\"Tooltip Content\"},\"elements\":[],\"widgetType\":\"text-editor\"},{\"id\":\"2e8976f\",\"elType\":\"widget\",\"settings\":{\"title\":\"What it looks like in practice\",\"header_size\":\"h3\",\"pp_display_conditions\":[{\"_id\":\"23060f8\"}],\"pp_elements_tooltip_content\":\"Tooltip Content\"},\"elements\":[],\"widgetType\":\"heading\"},{\"id\":\"a82d702\",\"elType\":\"widget\",\"settings\":{\"editor\":\"\u003Cp>\u003Cspan style=\\\"font-weight: 400;\\\">A suspect under investigation for embezzlement has a seized laptop with a deleted text file, a draft note referencing a vendor name. Their phone extraction has a chat message: 'the Acme Inc. invoice is handled, keep it between us.' A separate email thread includes an invoice from that same vendor, approved and forwarded by the suspect.\u003C\\/span>\u003C\\/p>\u003Cp>\u003Cspan style=\\\"font-weight: 400;\\\">None of these sit in a structured field an investigator would think to query together. They're scattered across a recovered file, a chat log, and an email attachment, all within the same case. Mapped as connected entities instead of three separate evidence folders, the vendor name becomes the thread tying the deleted file, the chat message, and the invoice into one pattern the investigator could review directly.\u003C\\/span>\u003C\\/p>\",\"pp_display_conditions\":[{\"_id\":\"d2c451f\"}],\"pp_elements_tooltip_content\":\"Tooltip Content\"},\"elements\":[],\"widgetType\":\"text-editor\"},{\"id\":\"e10eb59\",\"elType\":\"widget\",\"settings\":{\"title\":\"How unstructured data intelligence with graph visualization can help\",\"header_size\":\"h3\",\"pp_display_conditions\":[{\"_id\":\"fafa939\"}],\"pp_elements_tooltip_content\":\"Tooltip Content\"},\"elements\":[],\"widgetType\":\"heading\"},{\"id\":\"34ad695\",\"elType\":\"widget\",\"settings\":{\"editor\":\"\u003Cp>\u003Cspan style=\\\"font-weight: 400;\\\">Closing that gap is what an unstructured data intelligence platform with \u003Ca href=\\\"https:\\/\\/linkurious.com\\/graph-analytics\\/\\\" target=\\\"_blank\\\" rel=\\\"noopener\\\">graph analytics\u003C\\/a> and visualization is built to do. \u003Ca href=\\\"https:\\/\\/linkurious.com\\/decision-intelligence-platform-entity-resolution\\/\\\" target=\\\"_blank\\\" rel=\\\"noopener\\\">Entity matching\u003C\\/a> indicates that the vendor name in the deleted spreadsheet, the chat message, and the invoice all refer to the same entity, rather than three coincidental mentions.\\u00a0\u003C\\/span>\u003C\\/p>\u003Cp>\u003Cspan style=\\\"font-weight: 400;\\\">\u003Ca href=\\\"https:\\/\\/linkurious.com\\/decision-intelligence-platform-graph-visualization\\/\\\" target=\\\"_blank\\\" rel=\\\"noopener\\\">Graph analytics and visualization\u003C\\/a> then maps those matched entities as a connected structure, surfacing links that would otherwise take hours of manual cross-referencing to find.\u003C\\/span>\u003C\\/p>\u003Cp>\u003Cspan style=\\\"font-weight: 400;\\\">Together, these capabilities turn fragmented evidence into a connected view of the people, events, relationships, and information relevant to the investigation.\u003C\\/span>\u003C\\/p>\",\"pp_display_conditions\":[{\"_id\":\"8ce640c\"}],\"pp_elements_tooltip_content\":\"Tooltip Content\"},\"elements\":[],\"widgetType\":\"text-editor\"},{\"id\":\"0d5a01b\",\"elType\":\"widget\",\"settings\":{\"title\":\"Fraud investigations\",\"pp_display_conditions\":[{\"_id\":\"7bac2d3\"}],\"pp_elements_tooltip_content\":\"Tooltip Content\"},\"elements\":[],\"widgetType\":\"heading\"},{\"id\":\"44d77aa\",\"elType\":\"widget\",\"settings\":{\"editor\":\"\u003Cp>\u003Ca href=\\\"https:\\/\\/linkurious.com\\/fraud-schemes\\/\\\" target=\\\"_blank\\\" rel=\\\"noopener\\\">Organized fraud rings\u003C\\/a> have a playbook, and step one is looking unremarkable. Each claim clears review on its own, with nothing to connect it to any other. The connections between those claims may be hiding in places traditional fraud controls struggle to exploit: claim narratives, email threads and notes. Combined with the structured information already available, those clues can reveal a very different picture.\u003C\\/p>\",\"pp_display_conditions\":[{\"_id\":\"eb8255e\"}],\"pp_elements_tooltip_content\":\"Tooltip Content\"},\"elements\":[],\"widgetType\":\"text-editor\"},{\"id\":\"a2a067f\",\"elType\":\"widget\",\"settings\":{\"title\":\"What it looks like in practice\",\"header_size\":\"h3\",\"pp_display_conditions\":[{\"_id\":\"808196b\"}],\"pp_elements_tooltip_content\":\"Tooltip Content\"},\"elements\":[],\"widgetType\":\"heading\"},{\"id\":\"f172369\",\"elType\":\"widget\",\"settings\":{\"editor\":\"\u003Cp>\u003Cspan style=\\\"font-weight: 400;\\\">At an \u003Ca href=\\\"https:\\/\\/linkurious.com\\/insurance-solution\\/\\\" target=\\\"_blank\\\" rel=\\\"noopener\\\">insurance company\u003C\\/a>, an adjuster reviewing a single claim has no way to know it's one of three. They see an amount, a policy number, a date of loss, all within normal range. What they can't see is that a repair shop is quietly inflating damage estimates across multiple claims, working with claimants recruited specifically to file them.\\u00a0\u003C\\/span>\u003C\\/p>\u003Cp>\u003Cspan style=\\\"font-weight: 400;\\\">That shop's name shows up in another adjuster's free-text notes on a different claim, and again in a voicemail transcript attached to a third. None of the three claimants know each other, and none of the three adjusters have any reason to compare notes. Each detail sits in a different claim, a different format, reviewed by a different person, with no shared field connecting any of it.\u003C\\/span>\u003C\\/p>\",\"pp_display_conditions\":[{\"_id\":\"337df90\"}],\"pp_elements_tooltip_content\":\"Tooltip Content\"},\"elements\":[],\"widgetType\":\"text-editor\"},{\"id\":\"b5228ba\",\"elType\":\"widget\",\"settings\":{\"title\":\"How unstructured data intelligence with graph visualization can help\",\"header_size\":\"h3\",\"pp_display_conditions\":[{\"_id\":\"b8671e5\"}],\"pp_elements_tooltip_content\":\"Tooltip Content\"},\"elements\":[],\"widgetType\":\"heading\"},{\"id\":\"bec8fb9\",\"elType\":\"widget\",\"settings\":{\"editor\":\"\u003Cp>\u003Cspan style=\\\"font-weight: 400;\\\">Unstructured data intelligence processes the notes, images, and attachments across all three claims and pulls those details out as identifiable entities: a shop name, a phone number, a location, each one now something a system can actually match against the others.\u003C\\/span>\u003C\\/p>\u003Cp>\u003Cspan style=\\\"font-weight: 400;\\\">Graph analytics and visualization is what makes the result visible and explorable: it connects people, businesses, and claims instead of examining them one at a time. An analyst pulling up the repair shop's node sees every claim linked to it in one view, filed by different adjusters, different dates, different claimants, instead of having to already suspect a connection before going looking for one. What used to depend on memory now shows up automatically, the moment three claims share a single detail buried in unstructured text.\u003C\\/span>\u003C\\/p>\",\"pp_display_conditions\":[{\"_id\":\"c2dd7fb\"}],\"pp_elements_tooltip_content\":\"Tooltip Content\"},\"elements\":[],\"widgetType\":\"text-editor\"},{\"id\":\"3478fe0\",\"elType\":\"widget\",\"settings\":{\"title\":\"Anti-money laundering investigations\",\"pp_display_conditions\":[{\"_id\":\"674e98a\"}],\"pp_elements_tooltip_content\":\"Tooltip Content\"},\"elements\":[],\"widgetType\":\"heading\"},{\"id\":\"fb927e3\",\"elType\":\"widget\",\"settings\":{\"editor\":\"\u003Cp>\u003Cspan style=\\\"font-weight: 400;\\\">\u003Ca href=\\\"https:\\/\\/linkurious.com\\/anti-money-laundering\\/\\\" target=\\\"_blank\\\" rel=\\\"noopener\\\">AML\u003C\\/a> risk doesn't always show up where automated screening looks for it, and two separate problems make it hard to catch. First, the evidence needed to uncover it is often scattered across different sources, corporate filings, ownership disclosures, adverse media, internal records, much of it buried in unstructured text rather than sitting in a structured field.\\u00a0\u003C\\/span>\u003C\\/p>\u003Cp>\u003Cspan style=\\\"font-weight: 400;\\\">Second, the relationship that matters is often several steps removed, layered through intermediaries and ownership structures rather than sitting in a single and direct link. Look at each source and each entity in isolation, and the risk stays invisible; follow the money and the connections, and a different picture can emerge.\u003C\\/span>\u003C\\/p>\",\"pp_display_conditions\":[{\"_id\":\"724b5d4\"}],\"pp_elements_tooltip_content\":\"Tooltip Content\"},\"elements\":[],\"widgetType\":\"text-editor\"},{\"id\":\"9d60fec\",\"elType\":\"widget\",\"settings\":{\"title\":\"What it looks like in practice\",\"header_size\":\"h3\",\"pp_display_conditions\":[{\"_id\":\"a5b946d\"}],\"pp_elements_tooltip_content\":\"Tooltip Content\"},\"elements\":[],\"widgetType\":\"heading\"},{\"id\":\"1dfbb13\",\"elType\":\"widget\",\"settings\":{\"editor\":\"\u003Cp>\u003Cspan style=\\\"font-weight: 400;\\\">A \u003Ca href=\\\"https:\\/\\/linkurious.com\\/blog\\/transaction-monitoring\\/\\\" target=\\\"_blank\\\" rel=\\\"noopener\\\">transaction monitoring\u003C\\/a> alert flags a wire transfer as unusual. The analyst investigating it requests supporting documentation, and the client provides an invoice justifying the payment: a supplier billing for their services. Nothing in the documentation or the wider account activity immediately contradicts the client\\u2019s explanation, so the alert is closed.\u003C\\/span>\u003C\\/p>\u003Cp>\u003Cspan style=\\\"font-weight: 400;\\\">Weeks later, a different alert fires on a completely unrelated client's account. Same process: the analyst requests documentation, the client provides an invoice from a different, unrelated supplier, the transaction appears consistent with the explanation provided, so the alert is closed as well.\u003C\\/span>\u003C\\/p>\u003Cp>\u003Cspan style=\\\"font-weight: 400;\\\">Neither analyst has any reason to compare the two invoices, but the phone number and address on both, supposedly two separate suppliers, are identical. A phone number and address showing up as the contact detail behind two supposedly separate businesses is exactly the kind of overlap that can point to connection between the businesses, potentially indicating a shell-company structure or a wider network being used to support suspicious transactions.\u003C\\/span>\u003C\\/p>\",\"pp_display_conditions\":[{\"_id\":\"51ca77a\"}],\"pp_elements_tooltip_content\":\"Tooltip Content\"},\"elements\":[],\"widgetType\":\"text-editor\"},{\"id\":\"62a666e\",\"elType\":\"widget\",\"settings\":{\"title\":\"How unstructured data intelligence with graph visualization can help\\n\",\"header_size\":\"h3\",\"pp_display_conditions\":[{\"_id\":\"cdd325b\"}],\"pp_elements_tooltip_content\":\"Tooltip Content\"},\"elements\":[],\"widgetType\":\"heading\"},{\"id\":\"eaede89\",\"elType\":\"widget\",\"settings\":{\"editor\":\"\u003Cp>\u003Cspan style=\\\"font-weight: 400;\\\">In AML investigations, unstructured data intelligence can extract names, organizations, addresses, account numbers, phone numbers, and other relevant details from supporting documents, case notes, and other free-text sources. This makes information that would otherwise remain buried available for analysis alongside structured transaction and customer data.\u003C\\/span>\u003C\\/p>\u003Cp>\u003Cspan style=\\\"font-weight: 400;\\\">Graph analytics and visualization is what makes that overlap visible and explorable. In this example, an analyst could see that both closed alerts, filed weeks apart on unrelated accounts, are backed by invoices sharing the same phone number and address. That shared contact detail becomes a lead worth reopening and exploring further.\u003C\\/span>\u003C\\/p>\u003Cp>\u003Cspan style=\\\"font-weight: 400;\\\">Instead of investigating each alert or customer in isolation, analysts can follow connections across cases and data sources to uncover wider networks of potentially suspicious activity that may not be visible from any single transaction alone.\u003C\\/span>\u003C\\/p>\",\"pp_display_conditions\":[{\"_id\":\"2fb963f\"}],\"pp_elements_tooltip_content\":\"Tooltip Content\"},\"elements\":[],\"widgetType\":\"text-editor\"},{\"id\":\"d261bf2\",\"elType\":\"widget\",\"settings\":{\"title\":\"Law enforcement investigations\",\"pp_display_conditions\":[{\"_id\":\"457c5a0\"}],\"pp_elements_tooltip_content\":\"Tooltip Content\"},\"elements\":[],\"widgetType\":\"heading\"},{\"id\":\"b7ba620\",\"elType\":\"widget\",\"settings\":{\"editor\":\"\u003Cp>\u003Cspan style=\\\"font-weight: 400;\\\">Criminal investigations generate evidence across different teams and formats: reports, interview notes, surveillance data, \u003Ca href=\\\"https:\\/\\/linkurious.com\\/blog\\/osint-tools-enhance-investigations\\/\\\" target=\\\"_blank\\\" rel=\\\"noopener\\\">open source intelligence\u003C\\/a>. As investigations grow, the challenge isn\\u2019t just finding relevant information. It\\u2019s understanding how people, places, events, and pieces of evidence relate across an increasingly complex body of data. Without a shared, connected view, investigators can spend valuable time piecing together context that already exists somewhere across their evidence.\u003C\\/span>\u003C\\/p>\",\"pp_display_conditions\":[{\"_id\":\"e81561c\"}],\"pp_elements_tooltip_content\":\"Tooltip Content\"},\"elements\":[],\"widgetType\":\"text-editor\"},{\"id\":\"e6aae0c\",\"elType\":\"widget\",\"settings\":{\"title\":\"What it looks like in practice\",\"header_size\":\"h3\",\"pp_display_conditions\":[{\"_id\":\"d8c853b\"}],\"pp_elements_tooltip_content\":\"Tooltip Content\"},\"elements\":[],\"widgetType\":\"heading\"},{\"id\":\"4faa4e1\",\"elType\":\"widget\",\"settings\":{\"editor\":\"\u003Cp>\u003Cspan style=\\\"font-weight: 400;\\\">A detective working a fraud case seizes a suspect's phone. Buried in a chat conversation, the suspect mentions sending payment to a wallet address, just typed into the message.\u003C\\/span>\u003C\\/p>\u003Cp>\u003Cspan style=\\\"font-weight: 400;\\\">A separate, unrelated case, a different detective, a different suspect, includes a document where that same wallet address appears again, this time in a screenshot of a transaction the second suspect forwarded to someone else.\u003C\\/span>\u003C\\/p>\u003Cp>\u003Cspan style=\\\"font-weight: 400;\\\">Neither case treats the wallet address as significant on its own, it's a string of characters buried in a chat message in one file and inside an image in another. Nothing about how these two cases were opened or investigated would necessarily reveal that they share the same wallet address.\u003C\\/span>\u003C\\/p>\",\"pp_display_conditions\":[{\"_id\":\"dd57909\"}],\"pp_elements_tooltip_content\":\"Tooltip Content\"},\"elements\":[],\"widgetType\":\"text-editor\"},{\"id\":\"cbcbd6e\",\"elType\":\"widget\",\"settings\":{\"title\":\"How unstructured data intelligence with graph visualization can help\",\"header_size\":\"h3\",\"pp_display_conditions\":[{\"_id\":\"1f4d2be\"}],\"pp_elements_tooltip_content\":\"Tooltip Content\"},\"elements\":[],\"widgetType\":\"heading\"},{\"id\":\"14186f2\",\"elType\":\"widget\",\"settings\":{\"editor\":\"\u003Cp>\u003Cspan style=\\\"font-weight: 400;\\\">Unstructured data intelligence technology processes the evidence and extracts relevant details from sources such as emails, reports, messages, transcripts and attachments, while also making available metadata and other structured or semi-structured information such as dates, timestamps, and geolocation information.\u003C\\/span>\u003C\\/p>\u003Cp>\u003Cspan style=\\\"font-weight: 400;\\\">Graph analytics and visualization bring those details together in context. An analyst working either case can connect the wallet address mentioned in one case's chat log with the wallet address in another case's forwarded transaction, revealing that two otherwise separate suspects were dealing with the same wallet.\u003C\\/span>\u003C\\/p>\u003Cp>\u003Cspan style=\\\"font-weight: 400;\\\">That shared wallet becomes a new lead to explore. Investigators can then expand the network around the people, locations, events, and evidence involved to see what else the two cases may have in common.\u003C\\/span>\u003C\\/p>\",\"pp_display_conditions\":[{\"_id\":\"b38cc66\"}],\"pp_elements_tooltip_content\":\"Tooltip Content\"},\"elements\":[],\"widgetType\":\"text-editor\"},{\"id\":\"e783a3f\",\"elType\":\"widget\",\"settings\":{\"title\":\"Journalism and NGO Investigations\",\"pp_display_conditions\":[{\"_id\":\"631a0f1\"}],\"pp_elements_tooltip_content\":\"Tooltip Content\"},\"elements\":[],\"widgetType\":\"heading\"},{\"id\":\"53e8f59\",\"elType\":\"widget\",\"settings\":{\"editor\":\"\u003Cp>\u003Cspan style=\\\"font-weight: 400;\\\">A leaked document dump can contain millions of files and reference a vast number of entities\\u00a0 across scanned contracts, incorporation records, registration forms, and correspondence spread across multiple languages and formats. No single document tells the full story on its own. The real ownership or control behind an entity is rarely stated outright, it's built up in pieces, a name on one filing, an address on another, a director listed in a different jurisdiction years later.\\u00a0\u003C\\/span>\u003C\\/p>\u003Cp>\u003Cspan style=\\\"font-weight: 400;\\\">The connection only emerges when those fragments can be analyzed together across documents and at a scale that is extremely difficult for investigative journalists to handle manually. Finding that connection means tracing a chain across documents that were never meant to be read together, filed in different jurisdictions, years apart, by people who had every reason to keep them looking unrelated.\u003C\\/span>\u003C\\/p>\",\"pp_display_conditions\":[{\"_id\":\"cfd7d69\"}],\"pp_elements_tooltip_content\":\"Tooltip Content\"},\"elements\":[],\"widgetType\":\"text-editor\"},{\"id\":\"9b20dec\",\"elType\":\"widget\",\"settings\":{\"title\":\"What it looks like in practice\",\"header_size\":\"h3\",\"pp_display_conditions\":[{\"_id\":\"619ac2c\"}],\"pp_elements_tooltip_content\":\"Tooltip Content\"},\"elements\":[],\"widgetType\":\"heading\"},{\"id\":\"4bfd9c1\",\"elType\":\"widget\",\"settings\":{\"editor\":\"\u003Cp>\u003Cspan style=\\\"font-weight: 400;\\\">Two hundred thousand documents in a leak doesn't mean two hundred thousand stories. It may mean one story broken into pieces and scattered, with no way to know which pieces belong together.\u003C\\/span>\u003C\\/p>\u003Cp>\u003Cspan style=\\\"font-weight: 400;\\\">Investigators faced exactly this problem across the Panama Papers. Mossack Fonseca built more than 214,000 shell companies. Across the firm's business, using a nominee director, a placeholder name standing in for the real owner, was standard practice.\u003C\\/span>\u003C\\/p>\u003Cp>\u003Cspan style=\\\"font-weight: 400;\\\">At that volume, across 11.5 million files, 2.6 terabytes of data and 30 years worth of crime, no team of journalists could check for that kind of overlap by reading documents one at a time. The connections were there, but nobody could see it.\u003C\\/span>\u003C\\/p>\",\"pp_display_conditions\":[{\"_id\":\"b950584\"}],\"pp_elements_tooltip_content\":\"Tooltip Content\"},\"elements\":[],\"widgetType\":\"text-editor\"},{\"id\":\"f2cf266\",\"elType\":\"widget\",\"settings\":{\"title\":\"How unstructured data intelligence with graph visualization can help\",\"header_size\":\"h3\",\"pp_display_conditions\":[{\"_id\":\"46d3135\"}],\"pp_elements_tooltip_content\":\"Tooltip Content\"},\"elements\":[],\"widgetType\":\"heading\"},{\"id\":\"efe2c5e\",\"elType\":\"widget\",\"settings\":{\"editor\":\"\u003Cp>\u003Cspan style=\\\"font-weight: 400;\\\">Unstructured data intelligence helps turn large and heterogeneous document collections into something investigative journalists can actually work with. It can process scanned filings, agreements, correspondence, and other records at scale, extracting names, organizations, addresses, dates, signatories, and other relevant details that would otherwise remain buried across thousands or millions of files.\u003C\\/span>\u003C\\/p>\u003Cp>\u003Cspan style=\\\"font-weight: 400;\\\">Graph visualization and analytics add the relational layer. Investigators can visually explore how companies, directors, addresses, intermediaries, funding sources, and public figures connect, while graph analysis helps identify patterns within those networks, such as recurring intermediaries, clusters of related entities, indirect connections, or chains linking one organization to another.\u003C\\/span>\u003C\\/p>\u003Cp>\u003Cspan style=\\\"font-weight: 400;\\\">That changes both the scale and depth of the investigation. Investigators can follow ownership and control structures, test hypotheses, identify promising leads, and move through complex networks without losing sight of the underlying evidence. Patterns that would be extremely difficult to reconstruct manually become easier to identify, explore, and verify.\u003C\\/span>\u003C\\/p>\u003Cp>\u003Cspan style=\\\"font-weight: 400;\\\">This is exactly what played out with the \u003Ca href=\\\"https:\\/\\/resources.linkurious.com\\/financial-crime\\/investigation-detection-graph-analytics-solution\\\" target=\\\"_blank\\\" rel=\\\"noopener\\\">Panama Papers\u003C\\/a>. \u003Ca href=\\\"https:\\/\\/www.nuix.com\\/case-studies\\/panama-papers-analyzing-largest-data-leak-ever\\\" target=\\\"_blank\\\" rel=\\\"noopener\\\">Nuix's tools\u003C\\/a> made millions of spreadsheets, documents, emails, and other unstructured data available for analysis across multiple languages and formats, while removing duplicates and irrelevant data. ICIJ's data team transformed the leaked files into a connected graph structure and used Linkurious to visualize it. That enabled more than 370 journalists in over 100 newsrooms to analyze the entire network at once, surfacing context and connections that reading files one at a time might never have caught.\u003C\\/span>\u003C\\/p>\",\"pp_display_conditions\":[{\"_id\":\"88985f9\"}],\"pp_elements_tooltip_content\":\"Tooltip Content\"},\"elements\":[],\"widgetType\":\"text-editor\"},{\"id\":\"6ab3f92\",\"elType\":\"widget\",\"settings\":{\"image\":{\"url\":\"\\/images\\/uploads\\/2026\\/09\\/Copy-of-Wael-testimonial-new-branding-640-x-270-px-7.png\",\"id\":32564,\"size\":\"\",\"alt\":\"\",\"source\":\"library\"},\"pp_display_conditions\":[{\"_id\":\"b556b94\"}],\"pp_elements_tooltip_content\":\"Tooltip Content\"},\"elements\":[],\"widgetType\":\"image\"},{\"id\":\"6473122\",\"elType\":\"widget\",\"settings\":{\"title\":\"Turning buried data into a connected graph\",\"pp_display_conditions\":[{\"_id\":\"2e83061\"}],\"pp_elements_tooltip_content\":\"Tooltip Content\"},\"elements\":[],\"widgetType\":\"heading\"},{\"id\":\"29ee0d2\",\"elType\":\"widget\",\"settings\":{\"editor\":\"\u003Cp>\u003Cspan style=\\\"font-weight: 400;\\\">Across all five use cases, the challenge is fundamentally the same: valuable information is often buried in unstructured data, while the relationships that give that information meaning are scattered across a much wider body of evidence. Investigators need to do both: bring more of that information into the investigation and understand how it connects.\u003C\\/span>\u003C\\/p>\u003Cp>\u003Cspan style=\\\"font-weight: 400;\\\">\u003Ca href=\\\"https:\\/\\/www.nuix.com\\/\\\" target=\\\"_blank\\\" rel=\\\"noopener\\\">Nuix\u003C\\/a> with \u003Ca href=\\\"https:\\/\\/linkurious.com\\/\\\" target=\\\"_blank\\\" rel=\\\"noopener\\\">Linkurious\u003C\\/a> closes that gap, addressing those two sides of the problem within Nuix Neo. Nuix Neo processes over 1,000 file types at terabyte scale, extracting relevant information from emails, documents, chats, scanned records, and other sources and making it available for analysis alongside existing structured data. Then, it can automatically turn those entities and relationships into an interactive network that investigators can explore directly.\u003C\\/span>\u003C\\/p>\u003Cp>\u003Cspan style=\\\"font-weight: 400;\\\">Instead of reviewing individual records or documents in isolation, they can follow connections, identify patterns, expand around new leads, and understand the wider context surrounding an investigation.\u003C\\/span>\u003C\\/p>\u003Cp>\u003Cspan style=\\\"font-weight: 400;\\\">With unstructured data estimated to account for up to 90% of enterprise information, a significant share of potentially relevant context can otherwise remain difficult to access and analyze. The opportunity is not simply to process more data, but to bring more of the available evidence into complex investigations and turn it into context investigators can actually use.\u003C\\/span>\u003C\\/p>\",\"pp_display_conditions\":[{\"_id\":\"44bf1f6\"}],\"pp_elements_tooltip_content\":\"Tooltip Content\"},\"elements\":[],\"widgetType\":\"text-editor\"},{\"id\":\"14105be\",\"elType\":\"widget\",\"settings\":{\"title\":\"Get in touch with us\",\"header_size\":\"h3\",\"pp_display_conditions\":[{\"_id\":\"9981247\"}],\"pp_elements_tooltip_content\":\"Tooltip Content\"},\"elements\":[],\"widgetType\":\"heading\"},{\"id\":\"774fe44\",\"elType\":\"widget\",\"settings\":{\"editor\":\"\u003Cp>\u003Cspan style=\\\"font-weight: 400;\\\">Every use case above starts with the same question: what's hiding in the unstructured data you already have? \u003Ca href=\\\"https:\\/\\/linkurious.com\\/contact-us\\/\\\" target=\\\"_blank\\\" rel=\\\"noopener\\\">Get in contact with our experts\u003C\\/a> to talk through what that could look like for your team.\u003C\\/span>\u003C\\/p>\",\"pp_display_conditions\":[{\"_id\":\"5c77f90\"}],\"pp_elements_tooltip_content\":\"Tooltip Content\"},\"elements\":[],\"widgetType\":\"text-editor\"},{\"id\":\"4f83b95\",\"elType\":\"widget\",\"settings\":{\"title\":\"FAQ\",\"cards\":[{\"list_title\":\"What's the difference between structured and unstructured data?\",\"list_subtitle\":\"\u003Cp>Structured data fits into predefined fields, rows, and columns, think spreadsheets and SQL databases, and is easy to search and analyze with simple queries. Unstructured data has no fixed format, text documents, images, audio, video, emails, and requires more advanced techniques, like natural language processing, to extract anything usable from it. Structured data is easier to store and process; unstructured data holds far more volume and, often, far more of the actual signal.\u003C\\/p>\",\"_id\":\"2c2fea6\"},{\"list_title\":\"How does graph analytics work with unstructured data?\",\"list_subtitle\":\"\u003Cp>Graph analytics on its own needs data already modeled into nodes and relationships, so it can't read raw text directly. Unstructured data intelligence closes that gap first: natural language processing pulls entities, names, addresses, account numbers, out of free text, then graph analytics maps how those extracted entities connect. The result is a visual, explorable network built from sources, like case notes and email threads, that a spreadsheet or keyword search could never surface on its own.\u003C\\/p>\",\"_id\":\"a7ad8e3\"}],\"pp_display_conditions\":[{\"_id\":\"09366c6\"}],\"pp_elements_tooltip_content\":\"Tooltip Content\"},\"elements\":[],\"widgetType\":\"partner-program-how-to-partner\"}],\"isInner\":false}],\"isInner\":false}]","\t\t\u003Cdiv data-elementor-type=\"wp-post\" data-elementor-id=\"32560\" class=\"elementor elementor-32560\">\n\t\t\t\t\t\t\u003Csection class=\"elementor-section elementor-top-section elementor-element elementor-element-bbddf13 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"bbddf13\" data-element_type=\"section\">\n\t\t\t\t\t\t\u003Cdiv class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t\u003Cdiv class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-53de0fc\" data-id=\"53de0fc\" data-element_type=\"column\">\n\t\t\t\u003Cdiv class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\n\t\t\u003Cdiv class=\"elementor-element elementor-element-0a8fe75 elementor-widget elementor-widget-text-editor\" data-id=\"0a8fe75\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\u003Cdiv class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\u003Cp>\u003Cspan style=\"font-weight: 400;\">Complex investigations can take many forms: tracing a sophisticated \u003Ca href=\"https://linkurious.com/blog/insurance-fraud-investigation/\" target=\"_blank\" rel=\"noopener\">insurance fraud\u003C/a> ring, mapping an international money laundering network spanning \u003Ca href=\"https://linkurious.com/blog/real-estate-money-laundering/\" target=\"_blank\" rel=\"noopener\">real estate\u003C/a> and shell companies, or reconstructing \u003Ca href=\"https://linkurious.com/blog/digital-forensics-investigations/\" target=\"_blank\" rel=\"noopener\">digital evidence\u003C/a> from terabytes of data. But they often run into the same wall: harnessing vast amounts of \u003Ca href=\"https://linkurious.com/blog/unstructured-data-management-link-analysis/\" target=\"_blank\" rel=\"noopener\">unstructured data\u003C/a> and revealing critical connections hidden within the evidence. \u003C/span>\u003C/p>\u003Cp>\u003Cspan style=\"font-weight: 400;\">Traditional systems can readily process structured data. But valuable clues can also be buried in emails, case notes, witness statements, documents and other unstructured content. Extract and connect those details, and they can reveal relationships that would otherwise remain hidden.\u003C/span>\u003C/p>\u003Cp>\u003Cspan style=\"font-weight: 400;\">Unstructured data intelligence is what makes that extraction possible. Point it at a stack of documents, chat logs, or case notes, and it reads through all of it, pulling out the information buried inside. Extracting those details used to require someone reading every page by hand, but with unstructured data intelligence, free text turns into something an investigator can actually query.\u003C/span>\u003C/p>\u003Cp>\u003Cspan style=\"font-weight: 400;\">\u003Ca href=\"https://linkurious.com/decision-intelligence-platform-graph-analytics-ai-ml/\" target=\"_blank\" rel=\"noopener\">Graph technology\u003C/a> takes the next step: revealing how all that information connects. Every person, account, or other entity becomes a node. Every relationship between them becomes an edge linking two nodes together. Instead of examining each piece of evidence in isolation,  investigators visually explore the wider network at once, and a connection that would have taken hours to trace across a stack of spreadsheets shows up in a single glance.\u003C/span>\u003C/p>\u003Cp>\u003Cspan style=\"font-weight: 400;\">This article explores five areas where that combination can change how investigations are conducted: forensic data analytics, \u003Ca href=\"https://linkurious.com/fraud-investigation-solution/\" target=\"_blank\" rel=\"noopener\">fraud investigations\u003C/a>, \u003Ca href=\"https://linkurious.com/law-enforcement-and-intelligence-solution/\" target=\"_blank\" rel=\"noopener\">law enforcement\u003C/a>, \u003Ca href=\"https://linkurious.com/aml-investigation-solution/\" target=\"_blank\" rel=\"noopener\">anti-money laundering\u003C/a>, and investigative journalism. Across each, the principle is the same: unlock more of the information hidden in complex data, connect the dots, and give investigators a clearer picture of what they’re looking at.\u003C/span>\u003C/p>\t\t\t\t\t\t\t\t\u003C/div>\n\t\t\t\t\u003C/div>\n\t\t\t\t\t\u003C/div>\n\t\t\u003C/div>\n\t\t\t\t\t\u003C/div>\n\t\t\u003C/section>\n\t\t\t\t\u003Csection class=\"elementor-section elementor-top-section elementor-element elementor-element-4c8ebd6 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"4c8ebd6\" data-element_type=\"section\">\n\t\t\t\t\t\t\u003Cdiv class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t\u003Cdiv class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-1426fdd\" data-id=\"1426fdd\" data-element_type=\"column\">\n\t\t\t\u003Cdiv class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\u003Cdiv class=\"elementor-element elementor-element-3d23fa0 elementor-widget elementor-widget-heading\" data-id=\"3d23fa0\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\u003Cdiv class=\"elementor-widget-container\">\n\t\t\t\t\t\u003Ch2 class=\"elementor-heading-title elementor-size-default\">Forensic data analytics\u003C/h2>\t\t\t\t\u003C/div>\n\t\t\t\t\u003C/div>\n\t\t\t\t\u003Cdiv class=\"elementor-element elementor-element-6e7901a elementor-widget elementor-widget-text-editor\" data-id=\"6e7901a\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\u003Cdiv class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\u003Cp>\u003Cspan style=\"font-weight: 400;\">\u003Ca href=\"https://linkurious.com/blog/forensic-data-analytics/\" target=\"_blank\" rel=\"noopener\">Forensic data analytics\u003C/a>, the branch of digital forensics focused on finding patterns of criminal activity, only works once investigators have tracked down the sources that might hold relevant evidence, and that alone can be the hardest part of the process. It only gets harder as a case spans more borders, generates more data, and pulls in more formats that don’t talk to each other. Typical sources include:\u003C/span>\u003C/p>\u003Cul>\u003Cli style=\"font-weight: 400;\" aria-level=\"1\">\u003Cspan style=\"font-weight: 400;\">Laptops and mobile phones\u003C/span>\u003C/li>\u003Cli style=\"font-weight: 400;\" aria-level=\"1\">\u003Cspan style=\"font-weight: 400;\">Servers and cloud accounts\u003C/span>\u003C/li>\u003Cli style=\"font-weight: 400;\" aria-level=\"1\">\u003Cspan style=\"font-weight: 400;\">Transaction logs\u003C/span>\u003C/li>\u003Cli style=\"font-weight: 400;\" aria-level=\"1\">\u003Cspan style=\"font-weight: 400;\">Chat platforms and communication records\u003C/span>\u003C/li>\u003C/ul>\u003Cp>\u003Cspan style=\"font-weight: 400;\">Even once every source is tracked down, investigators still need to make sense of what is often a fragmented mix of structured and unstructured data and the connections between them aren’t spelled out anywhere.  A name mentioned in a chat, a date in a document, or an account number stored in a transaction record may all relate to the same person or event, with nothing to show they’re the same thread.\u003C/span>\u003C/p>\t\t\t\t\t\t\t\t\u003C/div>\n\t\t\t\t\u003C/div>\n\t\t\t\t\u003Cdiv class=\"elementor-element elementor-element-2e8976f elementor-widget elementor-widget-heading\" data-id=\"2e8976f\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\u003Cdiv class=\"elementor-widget-container\">\n\t\t\t\t\t\u003Ch3 class=\"elementor-heading-title elementor-size-default\">What it looks like in practice\u003C/h3>\t\t\t\t\u003C/div>\n\t\t\t\t\u003C/div>\n\t\t\t\t\u003Cdiv class=\"elementor-element elementor-element-a82d702 elementor-widget elementor-widget-text-editor\" data-id=\"a82d702\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\u003Cdiv class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\u003Cp>\u003Cspan style=\"font-weight: 400;\">A suspect under investigation for embezzlement has a seized laptop with a deleted text file, a draft note referencing a vendor name. Their phone extraction has a chat message: ‘the Acme Inc. invoice is handled, keep it between us.’ A separate email thread includes an invoice from that same vendor, approved and forwarded by the suspect.\u003C/span>\u003C/p>\u003Cp>\u003Cspan style=\"font-weight: 400;\">None of these sit in a structured field an investigator would think to query together. They’re scattered across a recovered file, a chat log, and an email attachment, all within the same case. Mapped as connected entities instead of three separate evidence folders, the vendor name becomes the thread tying the deleted file, the chat message, and the invoice into one pattern the investigator could review directly.\u003C/span>\u003C/p>\t\t\t\t\t\t\t\t\u003C/div>\n\t\t\t\t\u003C/div>\n\t\t\t\t\u003Cdiv class=\"elementor-element elementor-element-e10eb59 elementor-widget elementor-widget-heading\" data-id=\"e10eb59\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\u003Cdiv class=\"elementor-widget-container\">\n\t\t\t\t\t\u003Ch3 class=\"elementor-heading-title elementor-size-default\">How unstructured data intelligence with graph visualization can help\u003C/h3>\t\t\t\t\u003C/div>\n\t\t\t\t\u003C/div>\n\t\t\t\t\u003Cdiv class=\"elementor-element elementor-element-34ad695 elementor-widget elementor-widget-text-editor\" data-id=\"34ad695\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\u003Cdiv class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\u003Cp>\u003Cspan style=\"font-weight: 400;\">Closing that gap is what an unstructured data intelligence platform with \u003Ca href=\"https://linkurious.com/graph-analytics/\" target=\"_blank\" rel=\"noopener\">graph analytics\u003C/a> and visualization is built to do. \u003Ca href=\"https://linkurious.com/decision-intelligence-platform-entity-resolution/\" target=\"_blank\" rel=\"noopener\">Entity matching\u003C/a> indicates that the vendor name in the deleted spreadsheet, the chat message, and the invoice all refer to the same entity, rather than three coincidental mentions. \u003C/span>\u003C/p>\u003Cp>\u003Cspan style=\"font-weight: 400;\">\u003Ca href=\"https://linkurious.com/decision-intelligence-platform-graph-visualization/\" target=\"_blank\" rel=\"noopener\">Graph analytics and visualization\u003C/a> then maps those matched entities as a connected structure, surfacing links that would otherwise take hours of manual cross-referencing to find.\u003C/span>\u003C/p>\u003Cp>\u003Cspan style=\"font-weight: 400;\">Together, these capabilities turn fragmented evidence into a connected view of the people, events, relationships, and information relevant to the investigation.\u003C/span>\u003C/p>\t\t\t\t\t\t\t\t\u003C/div>\n\t\t\t\t\u003C/div>\n\t\t\t\t\u003Cdiv class=\"elementor-element elementor-element-0d5a01b elementor-widget elementor-widget-heading\" data-id=\"0d5a01b\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\u003Cdiv class=\"elementor-widget-container\">\n\t\t\t\t\t\u003Ch2 class=\"elementor-heading-title elementor-size-default\">Fraud investigations\u003C/h2>\t\t\t\t\u003C/div>\n\t\t\t\t\u003C/div>\n\t\t\t\t\u003Cdiv class=\"elementor-element elementor-element-44d77aa elementor-widget elementor-widget-text-editor\" data-id=\"44d77aa\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\u003Cdiv class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\u003Cp>\u003Ca href=\"https://linkurious.com/fraud-schemes/\" target=\"_blank\" rel=\"noopener\">Organized fraud rings\u003C/a> have a playbook, and step one is looking unremarkable. Each claim clears review on its own, with nothing to connect it to any other. The connections between those claims may be hiding in places traditional fraud controls struggle to exploit: claim narratives, email threads and notes. Combined with the structured information already available, those clues can reveal a very different picture.\u003C/p>\t\t\t\t\t\t\t\t\u003C/div>\n\t\t\t\t\u003C/div>\n\t\t\t\t\u003Cdiv class=\"elementor-element elementor-element-a2a067f elementor-widget elementor-widget-heading\" data-id=\"a2a067f\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\u003Cdiv class=\"elementor-widget-container\">\n\t\t\t\t\t\u003Ch3 class=\"elementor-heading-title elementor-size-default\">What it looks like in practice\u003C/h3>\t\t\t\t\u003C/div>\n\t\t\t\t\u003C/div>\n\t\t\t\t\u003Cdiv class=\"elementor-element elementor-element-f172369 elementor-widget elementor-widget-text-editor\" data-id=\"f172369\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\u003Cdiv class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\u003Cp>\u003Cspan style=\"font-weight: 400;\">At an \u003Ca href=\"https://linkurious.com/insurance-solution/\" target=\"_blank\" rel=\"noopener\">insurance company\u003C/a>, an adjuster reviewing a single claim has no way to know it’s one of three. They see an amount, a policy number, a date of loss, all within normal range. What they can’t see is that a repair shop is quietly inflating damage estimates across multiple claims, working with claimants recruited specifically to file them. \u003C/span>\u003C/p>\u003Cp>\u003Cspan style=\"font-weight: 400;\">That shop’s name shows up in another adjuster’s free-text notes on a different claim, and again in a voicemail transcript attached to a third. None of the three claimants know each other, and none of the three adjusters have any reason to compare notes. Each detail sits in a different claim, a different format, reviewed by a different person, with no shared field connecting any of it.\u003C/span>\u003C/p>\t\t\t\t\t\t\t\t\u003C/div>\n\t\t\t\t\u003C/div>\n\t\t\t\t\u003Cdiv class=\"elementor-element elementor-element-b5228ba elementor-widget elementor-widget-heading\" data-id=\"b5228ba\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\u003Cdiv class=\"elementor-widget-container\">\n\t\t\t\t\t\u003Ch3 class=\"elementor-heading-title elementor-size-default\">How unstructured data intelligence with graph visualization can help\u003C/h3>\t\t\t\t\u003C/div>\n\t\t\t\t\u003C/div>\n\t\t\t\t\u003Cdiv class=\"elementor-element elementor-element-bec8fb9 elementor-widget elementor-widget-text-editor\" data-id=\"bec8fb9\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\u003Cdiv class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\u003Cp>\u003Cspan style=\"font-weight: 400;\">Unstructured data intelligence processes the notes, images, and attachments across all three claims and pulls those details out as identifiable entities: a shop name, a phone number, a location, each one now something a system can actually match against the others.\u003C/span>\u003C/p>\u003Cp>\u003Cspan style=\"font-weight: 400;\">Graph analytics and visualization is what makes the result visible and explorable: it connects people, businesses, and claims instead of examining them one at a time. An analyst pulling up the repair shop’s node sees every claim linked to it in one view, filed by different adjusters, different dates, different claimants, instead of having to already suspect a connection before going looking for one. What used to depend on memory now shows up automatically, the moment three claims share a single detail buried in unstructured text.\u003C/span>\u003C/p>\t\t\t\t\t\t\t\t\u003C/div>\n\t\t\t\t\u003C/div>\n\t\t\t\t\u003Cdiv class=\"elementor-element elementor-element-3478fe0 elementor-widget elementor-widget-heading\" data-id=\"3478fe0\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\u003Cdiv class=\"elementor-widget-container\">\n\t\t\t\t\t\u003Ch2 class=\"elementor-heading-title elementor-size-default\">Anti-money laundering investigations\u003C/h2>\t\t\t\t\u003C/div>\n\t\t\t\t\u003C/div>\n\t\t\t\t\u003Cdiv class=\"elementor-element elementor-element-fb927e3 elementor-widget elementor-widget-text-editor\" data-id=\"fb927e3\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\u003Cdiv class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\u003Cp>\u003Cspan style=\"font-weight: 400;\">\u003Ca href=\"https://linkurious.com/anti-money-laundering/\" target=\"_blank\" rel=\"noopener\">AML\u003C/a> risk doesn’t always show up where automated screening looks for it, and two separate problems make it hard to catch. First, the evidence needed to uncover it is often scattered across different sources, corporate filings, ownership disclosures, adverse media, internal records, much of it buried in unstructured text rather than sitting in a structured field. \u003C/span>\u003C/p>\u003Cp>\u003Cspan style=\"font-weight: 400;\">Second, the relationship that matters is often several steps removed, layered through intermediaries and ownership structures rather than sitting in a single and direct link. Look at each source and each entity in isolation, and the risk stays invisible; follow the money and the connections, and a different picture can emerge.\u003C/span>\u003C/p>\t\t\t\t\t\t\t\t\u003C/div>\n\t\t\t\t\u003C/div>\n\t\t\t\t\u003Cdiv class=\"elementor-element elementor-element-9d60fec elementor-widget elementor-widget-heading\" data-id=\"9d60fec\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\u003Cdiv class=\"elementor-widget-container\">\n\t\t\t\t\t\u003Ch3 class=\"elementor-heading-title elementor-size-default\">What it looks like in practice\u003C/h3>\t\t\t\t\u003C/div>\n\t\t\t\t\u003C/div>\n\t\t\t\t\u003Cdiv class=\"elementor-element elementor-element-1dfbb13 elementor-widget elementor-widget-text-editor\" data-id=\"1dfbb13\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\u003Cdiv class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\u003Cp>\u003Cspan style=\"font-weight: 400;\">A \u003Ca href=\"https://linkurious.com/blog/transaction-monitoring/\" target=\"_blank\" rel=\"noopener\">transaction monitoring\u003C/a> alert flags a wire transfer as unusual. The analyst investigating it requests supporting documentation, and the client provides an invoice justifying the payment: a supplier billing for their services. Nothing in the documentation or the wider account activity immediately contradicts the client’s explanation, so the alert is closed.\u003C/span>\u003C/p>\u003Cp>\u003Cspan style=\"font-weight: 400;\">Weeks later, a different alert fires on a completely unrelated client’s account. Same process: the analyst requests documentation, the client provides an invoice from a different, unrelated supplier, the transaction appears consistent with the explanation provided, so the alert is closed as well.\u003C/span>\u003C/p>\u003Cp>\u003Cspan style=\"font-weight: 400;\">Neither analyst has any reason to compare the two invoices, but the phone number and address on both, supposedly two separate suppliers, are identical. A phone number and address showing up as the contact detail behind two supposedly separate businesses is exactly the kind of overlap that can point to connection between the businesses, potentially indicating a shell-company structure or a wider network being used to support suspicious transactions.\u003C/span>\u003C/p>\t\t\t\t\t\t\t\t\u003C/div>\n\t\t\t\t\u003C/div>\n\t\t\t\t\u003Cdiv class=\"elementor-element elementor-element-62a666e elementor-widget elementor-widget-heading\" data-id=\"62a666e\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\u003Cdiv class=\"elementor-widget-container\">\n\t\t\t\t\t\u003Ch3 class=\"elementor-heading-title elementor-size-default\">How unstructured data intelligence with graph visualization can help\n\u003C/h3>\t\t\t\t\u003C/div>\n\t\t\t\t\u003C/div>\n\t\t\t\t\u003Cdiv class=\"elementor-element elementor-element-eaede89 elementor-widget elementor-widget-text-editor\" data-id=\"eaede89\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\u003Cdiv class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\u003Cp>\u003Cspan style=\"font-weight: 400;\">In AML investigations, unstructured data intelligence can extract names, organizations, addresses, account numbers, phone numbers, and other relevant details from supporting documents, case notes, and other free-text sources. This makes information that would otherwise remain buried available for analysis alongside structured transaction and customer data.\u003C/span>\u003C/p>\u003Cp>\u003Cspan style=\"font-weight: 400;\">Graph analytics and visualization is what makes that overlap visible and explorable. In this example, an analyst could see that both closed alerts, filed weeks apart on unrelated accounts, are backed by invoices sharing the same phone number and address. That shared contact detail becomes a lead worth reopening and exploring further.\u003C/span>\u003C/p>\u003Cp>\u003Cspan style=\"font-weight: 400;\">Instead of investigating each alert or customer in isolation, analysts can follow connections across cases and data sources to uncover wider networks of potentially suspicious activity that may not be visible from any single transaction alone.\u003C/span>\u003C/p>\t\t\t\t\t\t\t\t\u003C/div>\n\t\t\t\t\u003C/div>\n\t\t\t\t\u003Cdiv class=\"elementor-element elementor-element-d261bf2 elementor-widget elementor-widget-heading\" data-id=\"d261bf2\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\u003Cdiv class=\"elementor-widget-container\">\n\t\t\t\t\t\u003Ch2 class=\"elementor-heading-title elementor-size-default\">Law enforcement investigations\u003C/h2>\t\t\t\t\u003C/div>\n\t\t\t\t\u003C/div>\n\t\t\t\t\u003Cdiv class=\"elementor-element elementor-element-b7ba620 elementor-widget elementor-widget-text-editor\" data-id=\"b7ba620\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\u003Cdiv class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\u003Cp>\u003Cspan style=\"font-weight: 400;\">Criminal investigations generate evidence across different teams and formats: reports, interview notes, surveillance data, \u003Ca href=\"https://linkurious.com/blog/osint-tools-enhance-investigations/\" target=\"_blank\" rel=\"noopener\">open source intelligence\u003C/a>. As investigations grow, the challenge isn’t just finding relevant information. It’s understanding how people, places, events, and pieces of evidence relate across an increasingly complex body of data. Without a shared, connected view, investigators can spend valuable time piecing together context that already exists somewhere across their evidence.\u003C/span>\u003C/p>\t\t\t\t\t\t\t\t\u003C/div>\n\t\t\t\t\u003C/div>\n\t\t\t\t\u003Cdiv class=\"elementor-element elementor-element-e6aae0c elementor-widget elementor-widget-heading\" data-id=\"e6aae0c\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\u003Cdiv class=\"elementor-widget-container\">\n\t\t\t\t\t\u003Ch3 class=\"elementor-heading-title elementor-size-default\">What it looks like in practice\u003C/h3>\t\t\t\t\u003C/div>\n\t\t\t\t\u003C/div>\n\t\t\t\t\u003Cdiv class=\"elementor-element elementor-element-4faa4e1 elementor-widget elementor-widget-text-editor\" data-id=\"4faa4e1\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\u003Cdiv class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\u003Cp>\u003Cspan style=\"font-weight: 400;\">A detective working a fraud case seizes a suspect’s phone. Buried in a chat conversation, the suspect mentions sending payment to a wallet address, just typed into the message.\u003C/span>\u003C/p>\u003Cp>\u003Cspan style=\"font-weight: 400;\">A separate, unrelated case, a different detective, a different suspect, includes a document where that same wallet address appears again, this time in a screenshot of a transaction the second suspect forwarded to someone else.\u003C/span>\u003C/p>\u003Cp>\u003Cspan style=\"font-weight: 400;\">Neither case treats the wallet address as significant on its own, it’s a string of characters buried in a chat message in one file and inside an image in another. Nothing about how these two cases were opened or investigated would necessarily reveal that they share the same wallet address.\u003C/span>\u003C/p>\t\t\t\t\t\t\t\t\u003C/div>\n\t\t\t\t\u003C/div>\n\t\t\t\t\u003Cdiv class=\"elementor-element elementor-element-cbcbd6e elementor-widget elementor-widget-heading\" data-id=\"cbcbd6e\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\u003Cdiv class=\"elementor-widget-container\">\n\t\t\t\t\t\u003Ch3 class=\"elementor-heading-title elementor-size-default\">How unstructured data intelligence with graph visualization can help\u003C/h3>\t\t\t\t\u003C/div>\n\t\t\t\t\u003C/div>\n\t\t\t\t\u003Cdiv class=\"elementor-element elementor-element-14186f2 elementor-widget elementor-widget-text-editor\" data-id=\"14186f2\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\u003Cdiv class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\u003Cp>\u003Cspan style=\"font-weight: 400;\">Unstructured data intelligence technology processes the evidence and extracts relevant details from sources such as emails, reports, messages, transcripts and attachments, while also making available metadata and other structured or semi-structured information such as dates, timestamps, and geolocation information.\u003C/span>\u003C/p>\u003Cp>\u003Cspan style=\"font-weight: 400;\">Graph analytics and visualization bring those details together in context. An analyst working either case can connect the wallet address mentioned in one case’s chat log with the wallet address in another case’s forwarded transaction, revealing that two otherwise separate suspects were dealing with the same wallet.\u003C/span>\u003C/p>\u003Cp>\u003Cspan style=\"font-weight: 400;\">That shared wallet becomes a new lead to explore. Investigators can then expand the network around the people, locations, events, and evidence involved to see what else the two cases may have in common.\u003C/span>\u003C/p>\t\t\t\t\t\t\t\t\u003C/div>\n\t\t\t\t\u003C/div>\n\t\t\t\t\u003Cdiv class=\"elementor-element elementor-element-e783a3f elementor-widget elementor-widget-heading\" data-id=\"e783a3f\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\u003Cdiv class=\"elementor-widget-container\">\n\t\t\t\t\t\u003Ch2 class=\"elementor-heading-title elementor-size-default\">Journalism and NGO Investigations\u003C/h2>\t\t\t\t\u003C/div>\n\t\t\t\t\u003C/div>\n\t\t\t\t\u003Cdiv class=\"elementor-element elementor-element-53e8f59 elementor-widget elementor-widget-text-editor\" data-id=\"53e8f59\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\u003Cdiv class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\u003Cp>\u003Cspan style=\"font-weight: 400;\">A leaked document dump can contain millions of files and reference a vast number of entities  across scanned contracts, incorporation records, registration forms, and correspondence spread across multiple languages and formats. No single document tells the full story on its own. The real ownership or control behind an entity is rarely stated outright, it’s built up in pieces, a name on one filing, an address on another, a director listed in a different jurisdiction years later. \u003C/span>\u003C/p>\u003Cp>\u003Cspan style=\"font-weight: 400;\">The connection only emerges when those fragments can be analyzed together across documents and at a scale that is extremely difficult for investigative journalists to handle manually. Finding that connection means tracing a chain across documents that were never meant to be read together, filed in different jurisdictions, years apart, by people who had every reason to keep them looking unrelated.\u003C/span>\u003C/p>\t\t\t\t\t\t\t\t\u003C/div>\n\t\t\t\t\u003C/div>\n\t\t\t\t\u003Cdiv class=\"elementor-element elementor-element-9b20dec elementor-widget elementor-widget-heading\" data-id=\"9b20dec\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\u003Cdiv class=\"elementor-widget-container\">\n\t\t\t\t\t\u003Ch3 class=\"elementor-heading-title elementor-size-default\">What it looks like in practice\u003C/h3>\t\t\t\t\u003C/div>\n\t\t\t\t\u003C/div>\n\t\t\t\t\u003Cdiv class=\"elementor-element elementor-element-4bfd9c1 elementor-widget elementor-widget-text-editor\" data-id=\"4bfd9c1\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\u003Cdiv class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\u003Cp>\u003Cspan style=\"font-weight: 400;\">Two hundred thousand documents in a leak doesn’t mean two hundred thousand stories. It may mean one story broken into pieces and scattered, with no way to know which pieces belong together.\u003C/span>\u003C/p>\u003Cp>\u003Cspan style=\"font-weight: 400;\">Investigators faced exactly this problem across the Panama Papers. Mossack Fonseca built more than 214,000 shell companies. Across the firm’s business, using a nominee director, a placeholder name standing in for the real owner, was standard practice.\u003C/span>\u003C/p>\u003Cp>\u003Cspan style=\"font-weight: 400;\">At that volume, across 11.5 million files, 2.6 terabytes of data and 30 years worth of crime, no team of journalists could check for that kind of overlap by reading documents one at a time. The connections were there, but nobody could see it.\u003C/span>\u003C/p>\t\t\t\t\t\t\t\t\u003C/div>\n\t\t\t\t\u003C/div>\n\t\t\t\t\u003Cdiv class=\"elementor-element elementor-element-f2cf266 elementor-widget elementor-widget-heading\" data-id=\"f2cf266\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\u003Cdiv class=\"elementor-widget-container\">\n\t\t\t\t\t\u003Ch3 class=\"elementor-heading-title elementor-size-default\">How unstructured data intelligence with graph visualization can help\u003C/h3>\t\t\t\t\u003C/div>\n\t\t\t\t\u003C/div>\n\t\t\t\t\u003Cdiv class=\"elementor-element elementor-element-efe2c5e elementor-widget elementor-widget-text-editor\" data-id=\"efe2c5e\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\u003Cdiv class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\u003Cp>\u003Cspan style=\"font-weight: 400;\">Unstructured data intelligence helps turn large and heterogeneous document collections into something investigative journalists can actually work with. It can process scanned filings, agreements, correspondence, and other records at scale, extracting names, organizations, addresses, dates, signatories, and other relevant details that would otherwise remain buried across thousands or millions of files.\u003C/span>\u003C/p>\u003Cp>\u003Cspan style=\"font-weight: 400;\">Graph visualization and analytics add the relational layer. Investigators can visually explore how companies, directors, addresses, intermediaries, funding sources, and public figures connect, while graph analysis helps identify patterns within those networks, such as recurring intermediaries, clusters of related entities, indirect connections, or chains linking one organization to another.\u003C/span>\u003C/p>\u003Cp>\u003Cspan style=\"font-weight: 400;\">That changes both the scale and depth of the investigation. Investigators can follow ownership and control structures, test hypotheses, identify promising leads, and move through complex networks without losing sight of the underlying evidence. Patterns that would be extremely difficult to reconstruct manually become easier to identify, explore, and verify.\u003C/span>\u003C/p>\u003Cp>\u003Cspan style=\"font-weight: 400;\">This is exactly what played out with the \u003Ca href=\"https://resources.linkurious.com/financial-crime/investigation-detection-graph-analytics-solution\" target=\"_blank\" rel=\"noopener\">Panama Papers\u003C/a>. \u003Ca href=\"https://www.nuix.com/case-studies/panama-papers-analyzing-largest-data-leak-ever\" target=\"_blank\" rel=\"noopener\">Nuix’s tools\u003C/a> made millions of spreadsheets, documents, emails, and other unstructured data available for analysis across multiple languages and formats, while removing duplicates and irrelevant data. ICIJ’s data team transformed the leaked files into a connected graph structure and used Linkurious to visualize it. That enabled more than 370 journalists in over 100 newsrooms to analyze the entire network at once, surfacing context and connections that reading files one at a time might never have caught.\u003C/span>\u003C/p>\t\t\t\t\t\t\t\t\u003C/div>\n\t\t\t\t\u003C/div>\n\t\t\t\t\u003Cdiv class=\"elementor-element elementor-element-6ab3f92 elementor-widget elementor-widget-image\" data-id=\"6ab3f92\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t\u003Cdiv class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\u003Cimg decoding=\"async\" width=\"100\" height=\"42\" src=\"/images/uploads/2026/09/Copy-of-Wael-testimonial-new-branding-640-x-270-px-7-100x42.png\" class=\"attachment-large size-large wp-image-32564\" alt=\"\" srcset=\"/images/uploads/2026/09/Copy-of-Wael-testimonial-new-branding-640-x-270-px-7-100x42.png 100w, /images/uploads/2026/09/Copy-of-Wael-testimonial-new-branding-640-x-270-px-7-578x244.png 578w, /images/uploads/2026/09/Copy-of-Wael-testimonial-new-branding-640-x-270-px-7-150x63.png 150w, /images/uploads/2026/09/Copy-of-Wael-testimonial-new-branding-640-x-270-px-7-382x161.png 382w, /images/uploads/2026/09/Copy-of-Wael-testimonial-new-branding-640-x-270-px-7-40x17.png 40w, /images/uploads/2026/09/Copy-of-Wael-testimonial-new-branding-640-x-270-px-7.png 640w\" sizes=\"(max-width: 100px) 100vw, 100px\" style=\"width:100%;height:42.19%;max-width:640px\" />\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\u003C/div>\n\t\t\t\t\u003C/div>\n\t\t\t\t\u003Cdiv class=\"elementor-element elementor-element-6473122 elementor-widget elementor-widget-heading\" data-id=\"6473122\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\u003Cdiv class=\"elementor-widget-container\">\n\t\t\t\t\t\u003Ch2 class=\"elementor-heading-title elementor-size-default\">Turning buried data into a connected graph\u003C/h2>\t\t\t\t\u003C/div>\n\t\t\t\t\u003C/div>\n\t\t\t\t\u003Cdiv class=\"elementor-element elementor-element-29ee0d2 elementor-widget elementor-widget-text-editor\" data-id=\"29ee0d2\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\u003Cdiv class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\u003Cp>\u003Cspan style=\"font-weight: 400;\">Across all five use cases, the challenge is fundamentally the same: valuable information is often buried in unstructured data, while the relationships that give that information meaning are scattered across a much wider body of evidence. Investigators need to do both: bring more of that information into the investigation and understand how it connects.\u003C/span>\u003C/p>\u003Cp>\u003Cspan style=\"font-weight: 400;\">\u003Ca href=\"https://www.nuix.com/\" target=\"_blank\" rel=\"noopener\">Nuix\u003C/a> with \u003Ca href=\"https://linkurious.com/\" target=\"_blank\" rel=\"noopener\">Linkurious\u003C/a> closes that gap, addressing those two sides of the problem within Nuix Neo. Nuix Neo processes over 1,000 file types at terabyte scale, extracting relevant information from emails, documents, chats, scanned records, and other sources and making it available for analysis alongside existing structured data. Then, it can automatically turn those entities and relationships into an interactive network that investigators can explore directly.\u003C/span>\u003C/p>\u003Cp>\u003Cspan style=\"font-weight: 400;\">Instead of reviewing individual records or documents in isolation, they can follow connections, identify patterns, expand around new leads, and understand the wider context surrounding an investigation.\u003C/span>\u003C/p>\u003Cp>\u003Cspan style=\"font-weight: 400;\">With unstructured data estimated to account for up to 90% of enterprise information, a significant share of potentially relevant context can otherwise remain difficult to access and analyze. The opportunity is not simply to process more data, but to bring more of the available evidence into complex investigations and turn it into context investigators can actually use.\u003C/span>\u003C/p>\t\t\t\t\t\t\t\t\u003C/div>\n\t\t\t\t\u003C/div>\n\t\t\t\t\u003Cdiv class=\"elementor-element elementor-element-14105be elementor-widget elementor-widget-heading\" data-id=\"14105be\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\u003Cdiv class=\"elementor-widget-container\">\n\t\t\t\t\t\u003Ch3 class=\"elementor-heading-title elementor-size-default\">Get in touch with us\u003C/h3>\t\t\t\t\u003C/div>\n\t\t\t\t\u003C/div>\n\t\t\t\t\u003Cdiv class=\"elementor-element elementor-element-774fe44 elementor-widget elementor-widget-text-editor\" data-id=\"774fe44\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\u003Cdiv class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\u003Cp>\u003Cspan style=\"font-weight: 400;\">Every use case above starts with the same question: what’s hiding in the unstructured data you already have? \u003Ca href=\"https://linkurious.com/contact-us/\" target=\"_blank\" rel=\"noopener\">Get in contact with our experts\u003C/a> to talk through what that could look like for your team.\u003C/span>\u003C/p>\t\t\t\t\t\t\t\t\u003C/div>\n\t\t\t\t\u003C/div>\n\t\t\t\t\u003Cdiv class=\"elementor-element elementor-element-4f83b95 elementor-widget elementor-widget-partner-program-how-to-partner\" data-id=\"4f83b95\" data-element_type=\"widget\" data-widget_type=\"partner-program-how-to-partner.default\">\n\t\t\t\t\u003Cdiv class=\"elementor-widget-container\">\n\t\t\t\t\t\u003Cdiv>\u003Cimg decoding=\"async\" src=\"https://wordpress-website.wordpress.k8s.preprod.linkurious.net/wp-content/plugins/linkurious-elementor-widgets/widgets/partner-program-how-to-partner/preview.png\" />\u003C/div>\t\t\t\t\u003C/div>\n\t\t\t\t\u003C/div>\n\t\t\t\t\t\u003C/div>\n\t\t\u003C/div>\n\t\t\t\t\t\u003C/div>\n\t\t\u003C/section>\n\t\t\t\t\u003C/div>\n\t\t","","Top 5 use cases for unstructured data intelligence with graph analytics","2026-09-16T14:53:16",{"node":12,"__typename":15},{"sourceUrl":13,"__typename":14},"/images/uploads/2026/09/Social-Banners-for-Blogs-700-x-400-px-8.png","MediaItem","NodeWithFeaturedImageToMediaItemConnectionEdge",{"nodes":17,"__typename":22},[18],{"name":19,"slug":20,"__typename":21},"Other use cases","other-use-cases","Category","PostToCategoryConnection",{"title":24,"description":25,"canonicalUrl":26,"focusKeywords":27,"fullHead":28,"robots":29,"jsonLd":35,"openGraph":38,"__typename":57},"5 Use Cases for Unstructured Data Intelligence & Graph Analytics","Unstructured data hides the connections investigators need. Discover 5 use cases where unstructured data intelligence and graph analytics reveal them.","https://wordpress-website.wordpress.k8s.preprod.linkurious.net/2026/09/16/unstructured-data-intelligence/",null,"\u003Ctitle>5 Use Cases for Unstructured Data Intelligence & Graph Analytics\u003C/title>\n\u003Cmeta name=\"description\" content=\"Unstructured data hides the connections investigators need. Discover 5 use cases where unstructured data intelligence and graph analytics reveal them.\"/>\n\u003Cmeta name=\"robots\" content=\"index, follow, max-snippet:-1, max-video-preview:-1, max-image-preview:large\"/>\n\u003Clink rel=\"canonical\" href=\"https://wordpress-website.wordpress.k8s.preprod.linkurious.net/2026/09/16/unstructured-data-intelligence/\" />\n\u003Cmeta property=\"og:locale\" content=\"en_US\" />\n\u003Cmeta property=\"og:type\" content=\"article\" />\n\u003Cmeta property=\"og:title\" content=\"5 Use Cases for Unstructured Data Intelligence & Graph Analytics\" />\n\u003Cmeta property=\"og:description\" content=\"Unstructured data hides the connections investigators need. 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