In the first of three blog posts, we explore the real-world applications for graph visualization, starting with law enforcement and fraud management.
Use cases for graph visualization
“Data is the new oil”– may be a tired cliche, but in some ways it’s true. Like oil, data in its raw, unrefined form is pretty worthless. To unlock its value, data needs to be refined, analyzed and understood.
You can say the same about graphs. More and more organizations are seeing the potential buried in their data connections. The question is, how do you analyze those data connections at scale to unlock their potentially valuable insights?
Often the answer is interactive graph visualization.
Why visualize graphs?
There are four reasons why graph visualization is such a powerful tool:
- It’s intuitive – presenting a graph as a node-link structure instantly makes sense, even to people who’ve never worked with graphs before.
- It’s fast – our brains are great at spotting patterns, but only when data is presented in a tangible format. Armed with visualization, we can spot trends and outliers very effectively.
- It’s flexible – the world is densely connected, so as long as there’s an interesting relationship in your data somewhere, you’ll find value in a graph visualization.
- It’s insightful – exploring graph data interactively allows users to gain deeper knowledge, understand context and ask more questions, compared to static visualization or just looking at raw data.
In a growing number of domains, graph visualization has become a must-have data analysis tool. Let’s take a quick look at the ways graph visualization is used in real life.
Law enforcement and security
Graph visualization isn’t new. The police have been using it – or link analysis as it’s commonly known – for decades to ‘join the dots’ in investigations. What has changed is the use of technology to make joining the dots a more automated and scalable process.
A failure to analyze the bigger, joined-up picture was cited as a shortcoming of the intelligence services after the 9/11 terror attacks. In the years that followed, there was a huge interest in link analysis software for law enforcement and security agencies. New approaches and technologies were created specifically for large-scale data analysis of communications records, open-source intelligence (OSINT), and police databases.
Lawful interception, the legally mandated interception of personal communications data, provided huge volumes of data on criminal and terrorist activity. Paired with social network analysis, graph visualization techniques allowed non-specialized staff to explore the data and uncover important insight.
Over time, new use cases for graph visualization have emerged. We’ve worked closely with Microsoft Services to build a call-handling interface that helps UK policing teams respond to incidents more effectively.
This webinar with Neo4j shows how visualizing crime records data helps users to understand patterns and allocate resources better. Our blog post about applying graph visualization to law enforcement data explains two possible uses for visualization in an event and investigation-led approach.
We’ve also seen how visualizing OSINT (in this case, advertising data) could help uncover human trafficking activity.
This kind of analysis is complex and highly specialized, so many law enforcement agencies choose to build their own visualization solutions instead of relying on off-the-shelf products. The scale of the data being analyzed has also transformed the nature of the visualization: it’s now essential to have effective layouts, filtering and grouping, as well as powerful graphics rendering.
Fraud detection and management
The financial services industry was another early adopter of graph visualization techniques.
Fraud detection is about finding unusual connections – between accounts, transactions, insurance policies, devices, etc. There’s great value in visualizing that data as a graph.
As we discussed in a previous blog post Enterprise fraud management investigation vs detection, the nature of the visualization will depend on whether you’re looking for known or unknown fraud.
Known fraud detection is largely automated with rule scoring and pattern matching. Analysts use visual tools to review edge-cases and outliers more quickly. They often only have seconds to approve or deny an application or transaction, so speed is essential.
In those cases, visualizations are small and simple, with limited interaction. To get a clear overview fast, analysts need visualization functionality like effective layouts, combine and expand, and filtering.
Unknown fraud requires a different process. Here we take a ‘global’ approach – loading a large volume of data and seeking out anomalies. This requires more specialized domain expertise, and a wider range of visual analysis functionality – like grouping, social network analysis and temporal analysis.
These two techniques are widely used in the banking and insurance industries to uncover incidences of fraud. Over time, we’ve seen these approaches used to combat other kinds of fraud, including healthcare fraud, gambling fraud, review fraud and even fake news.
What do these use cases have in common?
Three things are consistent across both graph visualization use cases:
- They involve highly connected data (obviously)
- That highly connected data conceals risk insight
- That insight is needed to power quick and confident decision making
When connected data insight is critical, only interactive and robust data visualization tools are up to the task.
Next time, we’ll look at a use case that’s emerged more recently but is increasingly vital: cyber security. In the meantime, don’t forget to start a trial, or get in touch if you’d like to know more about KeyLines, ReGraph or KronoGraph – our data visualization toolkits.