The anti-fraud data challenge
Fraud is an expensive and complicated problem. Making sense of it requires a detailed understanding of connections – between people, accounts, transactions and dates – and the ability to quickly separate legitimate activity from fraud.
This is further complicated by the fast-evolving tactics used by fraudsters. As the crimes become more sophisticated, so too must the data analysis techniques used by fraud analysts. Increasingly, this means relying on powerful network visualization tools.
The link analysis solution
When trying to detect fraud, analysts are looking for two different kinds: known and unknown fraud.
As the names suggest, known fraud is fraudulent activity that has previously been encountered. The behavior patterns can be defined and detected using rule-scoring and pattern-matching algorithms. In this case, network visualization is an effective investigation tool, providing the fast and accurate situational analysis required to make a fast go / no-go decision.
Unknown fraud is fraud that has not been previously detected. As automated processes will not help here, network visualization acts as a detection tool.
Investigations of known fraud tend to take a case-centric approach – starting from a specific transaction, account or person, and working outwards. We can see that approach in action in this blog post about insurance fraud.
Investigations of unknown fraud use a global approach – viewing large volumes of transactions in one chart to uncover outliers or unusual patterns. This healthcare fraud example shows how this works.
White paper: fraud detection & investigation
Discover five of the ways our customers use network visualization to detect and investigate fraud.
Why choose us?
Fraud data is large, complex, noisy and often incomplete. Network visualization helps analysts investigate fraud in an interactive and intuitive way.
Our technologies can help analysts:
- Join the dots in fraud data to discover patterns and anomalies
- Pull transaction data from multiple sources
- Share interactive charts for reporting and investigation
- Make informed decisions more quickly
Our toolkits also include advanced functionality to help analysts unlock fraud data and see context on demand, including filtering, layouts, social network analysis, time analysis and geospatial visualization.
Find out more
We work with organizations, including Visa, Aviva, Cifas and JPMC, to help them make sense of complex connected data for fraud management. If you’d like to learn more, request a trial of our technology or get in touch.
Other fraud posts from our blog
In this post we demonstrate why network visualization should be part of your fraud detection workflow. We show how integrating KeyLines can help you make sense of large and complex fraud datasets.
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 n
We’ve written about fraud as a connected data problem before. We’ve shown how the key to fraud insight lies in relationships between people, accounts, transactions and events. Fraud analysts prese