Financial services organizations are having to deal with highly complex fraud rings today. Fraudsters are spreading their activities across a large number of transactions and geographical regions in a more coordinated fashion, making traditional anomaly identification methods nearly obsolete.
Detecting fraudulent activity in a timely manner requires rapidly sifting through terabytes of data and accurately detecting signal from noise. Fraudsters rapidly change tactics and sneakily hide behind seemingly “normal” activities. This makes it incredibly difficult to anticipate how a new “signal” in the data will manifest or the best way to traverse the data in order to reveal it. This level of uncertainty can translate into a huge time sink even for highly skilled staff, which leaves managers to painfully prioritize only a small number of anomalies to be investigated.
Unlike relational databases, supporting uncertainty is where graph technology really shines! Graph data dimensionality is different from relational data. Relational data has a fairly fixed set of dimensions, but graph data is dynamic. And while you can certainly use graph to power similar use cases as other NoSQL systems, for instance, your next recommendation engine, graph is capable of so much more. Pair the always-on, real-time processing ability provided by DataStax Enterprise (DSE) Graph with thoughtful data visualizations and you’ve got a serious weapon for identifying points of compromise for fraudulent activity.
When it comes to viewing and sifting through massive amounts of bank transaction data, the old adage of not being able to see the forest for the trees is incredibly apt in this case. In a recent joint webinar presented by Expero and DataStax, we discussed how to apply intelligent algorithms such as Data Clustering, Similarity, Matching, Flow and Centrality as actionable means for rapidly visualizing and experiencing any data modeled as a graph (FIG 1). By applying these intelligent algorithms, visualization patterns and DataStax Enterprise Graph, companies can quickly create tools to identify anomalies in real-time and take immediate actions to mitigate risk and prevent losses.
View the on-demand webinar to learn how to combine DSE Graph with an iterative, user-driven approach and the latest UI tools to enable your team to start creating effective fraud identification and prevention methods with graph data.
What you’ll learn:
- Why a real-time graph data capability is particularly well-suited to address the uncertainty associated with fraudulent activity and how to realize the value of early and accurate anomaly detection to save time and prevent losses.
- New methods for rapid anomaly identification and risk scoring
- How to create rapid prototypes with DataStax to explore and understand the value a real-time graph capability delivers for fighting bank fraud.
- How to structure a typical graph technology stack with Datastax and Expero’s software.
- Common methods for visualizing Risks, Anomalies and Hot Spots that enable mere mortals and key decision-makers to interact with real-time data and diagnostics.
- Examples of:
- Entity Link Analysis
- Graph Traversals
- Geospatial Viewing
- Timeline Analysis
About the Author: Scott Heath is a leader in Expero’s Graph Practice and a software geek at heart. Expero is a DataStax Premier Partner with expertise in solutions using DataStax Enterprise Graph. A key part of Expero’s value proposition is our longtime focus on “Innovation through Technology” and enabling our customers to solve their most complex business problems like identifying and eliminating fraud.
SHARE THIS PAGE