Abstract: Bitcoin is the most popular cryptocurrency because of the robust Blockchain technology that it is built on. While the technology prevents fraud on the network, there are no checks to track how the bitcoins are being used and for what purpose. It is concerning if it ends up being used for racketeering, trafficking, and money laundering. Bitcoin is no longer a niche concept and has been widely adopted, this is especially true after the recent Web 3.0 boom. Thus catching these criminals is of great importance. This project tries to investigate the block data stored on the blockchain to gain insight and build relationships between transactions that can shed light on the transactions. Using the HPCC Systems analytics platform for ingesting the data, the project will build rich relationships between the transactions that investigators can use to detect criminal activity. In this talk Rohan will take you through how clustering and transaction times series fingerprinting can give insights into the underlying transaction behaviour of users and help find the criminals utilizing the bitcoin network for illicit activities.
Bio: Rohan Maheshwari is a student at RV College of Engineering in Bengaluru, India with a keen interest in Deep Learning, Natural Language Processing, Graph modelling and Machine Learning as well as their applications in finance and sentiment analysis. He has worked under the Samsung PRISM program to create a code-mixed multi-intent classification system. He has also worked with SCII to create an invoice extraction system. He is actively working with the LexisNexis® Risk Solutions HPCC Systems® team and the RV College of Engineering Centre of Excellence on Cognitive Intelligent Systems for Sustainable Solutions to investigate block data stored on the blockchain to gain insight and build relationships between transactions that can shed light on potential criminal transactions . Rohan is pursuing a Bachelor of Computer Science and Engineering at RV College of Engineering