Enriching dark data to uncover hidden patterns
In our first article on Forensic Data Analytics (FDA), we explored how advanced analytics, AI, and machine learning are transforming forensic investigations – helping organisations detect fraud, identify risks, and uncover patterns hidden within vast datasets.
But what happens when critical information exists outside the reach of traditional analytics?
A significant portion of enterprise data remains “dark” – unindexed, unstructured, and largely inaccessible to forensic investigators. Today, we’ll explore how data enrichment techniques can transform this dark data into a valuable resource to allow investigators to investigate fraud with forensic data analytics.
What is dark data?
Dark data usually refers to the unprocessed, unstructured, or inaccessible information that organisations collect but rarely analyse. In the context of eDiscovery and forensic investigations, however, the term dark data is used specifically to refer to content that cannot be discovered – usually because it lacks an indexable text layer.
Examples of dark data include:
- Audio and video files (e.g. recorded calls, surveillance footage)
- Images and scanned documents without recognised text
- Handwritten notes and signatures
- Unstructured logs from IT systems, applications, or networks
Despite being largely untapped, dark data often holds key insights into fraud, compliance risks, or regulatory breaches – provided it can be enriched and analysed effectively.
How is dark data enriched?
To unlock the potential of dark data, it must first be enriched – transforming raw, unstructured information into a format that can be processed, indexed, and searched.
Dark data can be enriched using:
- Speech-to-text transcription: Converts audio and video into searchable text, allowing investigators to quickly identify relevant keywords in digital recordings.
- AI-powered image recognition: Uses AI to label and categorise objects (and people) in images, enabling searches like “Find all images containing a knife” or “Identify all scanned identity documents”.
- Optical character recognition (OCR): Cutting edge OCR can now transform handwritten text into searchable data in addition to conventional printed text, making contracts, notes, and records easier to analyse.
How is enriched data brought into FDA workflows?
Once dark data is enriched, forensic investigators can use forensic data analytics to investigate fraud, including uncovering suspicious activity, analysing trends, and generating insights.
Here’s how we approach this at Salient.
Computer-assisted audit techniques (CAAT)
Our FDA specialists use Computer-Assisted Audit Techniques (CAAT) to analyse large datasets, identifying anomalies that may indicate fraud or non-compliance. CAAT allows forensic teams to:
- Process and analyse vast amounts of data quickly and accurately.
- Detect patterns, relationships and irregularities that might indicate fraudulent behaviour.
- Automate complex and/or repetitive investigative processes, enabling organisations to proactively monitor data and manage risk.
Before running CAAT tests, investigators must normalise data from multiple sources to ensure consistency and accuracy. With data often coming in a variety of formats from different systems, this process can be complex. Partnering with experts like Salient ensures that information is correctly structured, enabling precise and reliable FDA insights.
Did you know? FDA techniques, including CAAT, can be applied not only to support one-time investigations but also to establish ongoing monitoring systems. Once a specific issue – such as fraud or compliance violations – has been investigated, the same methods can be repeated proactively to detect and flag similar risks in the future, helping organisations stay ahead of potential threats.
Data visualisation and pattern recognition
Our forensic specialists are able to develop purpose-built, interactive visualisations and data models in tools like Power BI and Qlik. These help to:
- Expose hidden patterns and connections between datasets.
- Unravel complex ownership structures and monetary flows.
- Clarify complex data relationships, enabling investigators to make faster, data-driven decisions.
Turning hidden data into actionable intelligence
In today’s data-driven investigations, overlooked information can mean missed opportunities – or hidden vulnerabilities. Enriching dark data is the key to unlocking critical insights that can expose fraud, strengthen compliance, and support litigation. Without it, vital connections may remain unseen.
At Salient, we specialise in transforming unstructured data into clear, actionable intelligence. Whether you need to uncover hidden patterns, normalise complex datasets, or proactively monitor for risks, our FDA expertise and AI-driven enrichment techniques give you the edge.
Ready to shine a light on your dark data? Contact us today to see how we can help you turn untapped information into powerful forensic insights – and protect your organisation from unseen risks.
Unveiling the power of analytics in forensic investigations
What is forensic data analytics, how do you get the most from it and when should it be used? Read our full series of articles about forensic data analytics.
Introduction to forensic data analytics techniques
Forensic Data Analytics is a powerful tool that leverages advanced analytics and machine learning to analyse large datasets, helping to uncover hidden patterns and detect fraudulent activities. Here we discuss the applications and challenges of forensic data analytics in modern investigations.
Enriching dark data to uncover hidden patterns with forensic data analytics
Forensic Data Analytics is revolutionising how organisations investigate fraud and compliance risks, however a significant challenge is the existence of “dark data” (unstructured and unindexed data). We share how to enrich dark data and use it to reveal critical insights.
Cracking the code of financial crime with forensic data analytics
Forensic data analytics is revolutionising financial crime investigations by utilising advanced analytics, AI, and machine learning to trace illicit funds, identify suspicious patterns, and monitor transactions in real-time. Find out about about forensic data analytics in action.
