Apply a graph-centric perspective on public ledgers (e.g., Bitcoin blockchain).
Find and flag patterns in transactions that do not conform to expected behaviour.
Analyse in real-time using scalable data processing infrastructures.
GraphSense provides algorithmic solutions for real-time analytics of digital currency transactions for gaining insight into cash flows and functionality of digital currency systems. A special focus lies on anomaly detection, which should identify transactions and transaction patterns that deviate from typical structures. This could, for instance, help in identifying and tracing fraudulent activities. GraphSense will be applicable for fiat as well as emerging virtual currencies such as Bitcoin.
Distributed Storage and Processing
Real-time streaming adapters to various backends
Applicable for fiat and virtual currency transactions
Improve anomaly detection by learning from expert feedback
We are happy to announce the first public prototype release (v0.2) of our GraphSense tool, which allows exploration of virtual currency ecosystems such as Bitcoin. It is now available at: http://bitcoin-demo.graphsense.info/ We have learned a lot from initial user feedback and have been working hard on improving things and implemented some key features: A better user Read more about First public beta (r.0.2) is out[…]
Emerging cryptocurrencies such as Bitcoin provide a unique opportunity to gain insight into uses and evolution of a currency over a period of time, which is now almost 6 years. This is important for understanding digital currencies systems and could also inform development of novel empirical economic models. However, the blockchain has passed the 400K boundary and each Read more about Processing the blockchain with Apache Spark[…]
The overall goal of GraphSense is to provide a real-time analytics solution for detecting anomalies (e.g., fraud) in financial transaction data. In contrast to other solutions, it should bridge the gap between fiat (e.g., EUR) and virtual (e.g., Bitcoin) digital currency transactions and apply a network-centric perspective on the anomaly detection problem. Development has started and a first Read more about What to expect from GraphSense[…]