Fraud analysts and investigators face the trend of increasing amount of data for processing, as the fraud is evolving. Growing amount of data has to be searched throughout the traditional databases in not user-friendly environments for non-tech users. Additionally, a lot of manual effort and time needs to be spent to analyze and detect fraud rings properly. Moreover, fraud analysts need to deliver precise and not faulty results in shorter and shorter periods of time. In real time ideally.
Fraud pattern is a well-known term to every single insurance company, bank or e-commerce company. Modern fraudsters build sophisticated fraud rings and schemes that are hard to unveil with traditional tools. And, can cause serious losses if not detected properly and on time. On the other hand, traditional red flag systems with too strict criteria must be adjusted to eliminate false positive indicators. As it would lead to overwhelming fraud indications. Which secondarily would head to paralyzing investigator’s daily work.
Disruptively changing markets and strong competition pushing every company to meet clients’ needs open new opportunities for fraudsters:
Demanding clients with their needs and high expectations on one side.
Professionally organized groups, virtual circles, stolen and synthetic identities, hijacked devices, offshore accounts... on the other side.
Rapidly changing business environment, increasing amount of data and evolving fraud methods then request increasing resources for every bank, insurance or e-commerce company.
An ideal solution to this situation is a graph visualization powered by Graphlytic as a powerful support to traditional antifraud tools.
Part of a fraud ring
Lietuvos draudimas, October 2018, Vilnius. Created by Viktorija Katilienė and Saulius Švirmickas.
The basic idea behind is based on the fact that the human eye can simply distinguish and find any pattern in a graphical form much easier than in any table or data set. It means that antifraud analyst can capture fraud schemes within graph visualization much easier, faster and smarter than in solely traditional tools.
Easier, as antifraud analysts after short training are able to work with an intuitive and easy to use environment. Easier, as analysts’ eyes can easily identify what is hard to identify in tables.
Faster, as the manual analysts review time per case will be decreased significantly! Antifraud department can then review multiples of transactions monthly compared to the situation before a graph visualization solution was implemented. Speaking in financial terms, faster means saving of significant resources!
Smarter, as by visualizing the relationships between the subjects in antifraud analysis you can operate more extensive searches and depict previously unnoticed fraud connections. Again, smarter in this way means saving another pile of resources, that otherwise would end in hands of fraudsters.
Pain points / Challenges
- Organized fraud groups, fraud rings, hidden patterns
- Increasing amount of data for processing
- Analysts’ work in not user-friendly environment
- A lot of manual effort
- Pressure to act faster
- Serious losses if not detected properly and on time
- Graphlytic on-premises implementation (due to sensitive data)
- Using graph visualization as a strong support tool to traditional antifraud tools
- Leveraging human eye’s strength in capturing patterns within graphs
- Shift from individual claim toward overall interconnected overview
- Intuitive and easy to use environment
- Significantly faster results. Significant savings!
- More extensive searches than ever before
- Higher protection against fraudsters by unveiling previously unseen fraud connections
- GDPR compliant