Detecting fraud schemes used to require investigations using large amounts and varying types of data that come from many different anti-fraud systems. Investigators then need to combine the different types of data and use statistical methods to uncover suspicious claims, which is time consuming and inefficient in most cases.
We are always looking for ways to improve fraud investigation methods and stay one step ahead of our ever-growing fraudsters. In the introductory blog and graph concepts articles of this series, we’ve covered experimenting with a set of Graph Network technologies, including Graph Visualisation, and the basics of graph concepts.
In this post, we will introduce our Graph Visualisation Platform and briefly illustrate how it makes fraud investigations easier and more effective.
Why visualise a graph?
If you’re a fan of crime shows, you would have come across scenes like a detective putting together evidence, such as pictures, notes and articles, on a board and connecting them with thumb tacks and yarn. When you look at the board, it’s easy to see the relationships between the different pieces of evidence. That’s what graphs do, especially in fraud detection.
In the same way, while graph data is the raw material of an investigation, some of the most interesting relationships are often inferred rather than modelled directly in the data. Visualising these relationships can give a unique “big picture” of the data that is difficult or impossible to obtain with traditional relational tables and business intelligence tools.
On the other hand, graph visualisation enhances the quick identification of relationships and significant structures because it is an intuitive way to help detect patterns. Plus, the human brain processes visual information much faster; that’s where our Graph Visualisation platform comes in.
What is the Graph Visualisation platform?
Graph Visualisation platform is a full-featured investigation platform that can reveal hidden connections and context in data by transforming raw records into highly visual and interactive maps. From there, investigators can grab any data point and quickly see relationships, patterns, and anomalies, and if necessary, drill down to investigate further.
This is all done without writing a manual query, switching between anti-fraud systems, or having to think about data science! These are some of the interactions on the platform that easily make anomalies or relevant patterns stand out.
Expanding the data
To date, we have over three billion nodes and edges in our storage system. It is not possible (nor necessary) to show all of the data at once. The platform allows the user to grab any data point and easily expand to view the relationships.
Timeline tracking and history replay
The Graph Visualisation platform’s interactive time filter lets you see temporal relationships within your data and clearly reveals the chronological progression of events. You can start with a specific time of interest, track everything that happens after, then quickly focus on the time and relationships that matter most.
Here are a few examples of how the Graph Visualisation platform facilitates fraud investigations.
The following image shows the difference between a true fraudster and a falsely identified one. On the left, we have a Grab rental corporate account that was falsely detected by a fraud rule. Upon review, we discovered that there is no suspicious connection to this account, thus the account got unblocked.
On the right, we have a passenger that was blocked by the system and they appealed. Investigations showed that the passenger is, in fact, part of an extremely dense device-sharing network, so we maintained our decision to block.
Modus operandi discovery
Passenger sharing device
Fraudsters tend to share physical resources to maximise their revenue. With our Graph Visualisation platform, you can see exactly how this pattern looks like. The image below shows a device that is shared by a lot of fraudsters.
Anti-money laundering (AML)
On the left, we see a pattern of healthy spending on Grab. However, on the right, we can see that passengers are highly connected, and it has frequent large amount transfers to other payment providers.
Graph Visualisation is an intuitive way to investigate suspicious connections and potential patterns of crime. Investigators can directly interact with any data point to get the details they need and literally view the relationships in the data to make fast, accurate, and defensible decisions.
While fraud detection is a good use case for Graph Visualisation, it’s not the only possibility. Graph Visualisation can help make anything more efficient and intelligent, especially if you have highly connected data.
Speak to us
GrabDefence is a proprietary fraud prevention platform built by Grab, Southeast Asia’s leading superapp. Since 2019, the GrabDefence team has shared its fraud management capabilities and platform with enterprises and startups to leverage Grab’s advanced AI/ML models, hyper local insights and patented device intelligence technologies.
To learn more about GrabDefence or to speak to our fraud management experts, contact us at email@example.com.
Grab is the leading superapp platform in Southeast Asia, providing everyday services that matter to consumers. More than just a ride-hailing and food delivery app, Grab offers a wide range of on-demand services in the region, including mobility, food, package and grocery delivery services, mobile payments, and financial services across 428 cities in eight countries.
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