Difference between revisions of "DIGITAL"

From EU COST Fin-AI
Jump to navigation Jump to search
Line 9: Line 9:
 
= [https://wiki.fin-ai.eu/index.php/SNF_P2P SNF: Network-based P2P Scoring Models ] =
 
= [https://wiki.fin-ai.eu/index.php/SNF_P2P SNF: Network-based P2P Scoring Models ] =
  
−
= SNF: Anomaly and fraud detection in blockchain networks =
+
= [https://wiki.fin-ai.eu/index.php/SNF_Blockchain SNF: Anomaly and fraud detection in blockchain networks] =
−
[https://data.snf.ch/grants/grant/211195 Grant link]
 
−
 
 
−
Blockchain networks are increasingly being implemented into healthcare, supply chain, and retail systems, through smart contracts, smart devices, smart identity management. Although the use of this technology brings with it benefits, it can also still cause problems. A particular problem is derived from the immutability property, which means that fraudulent transactions or transfers of information cannot be reversed. Rationale: Blockchains can be attacked via a deluge of requests or transactions within a short time span, resulting in the loss of connectivity to the blockchain for users and businesses, or even financial institutions. Therefore, the rapid detection of anomalies from such activities is critical in order to prevent damage from occurring, or correct any damage as soon as possible to reduce the severity of its impact.Overall objectives: This project will study the problem of anomaly and fraud detection from the perspective of blockchain-based networks. Anomaly and fraud detection in blockchain-based networks is more complex due to their unique properties such as decentralisation, global reach, anonymity, etc., which make them different from traditional networks.Specific aims: To further the understanding of the sources and behaviours of anomalies and fraud in blockchain-based networks, and develop new improved methods for both static and dynamic anomaly detection that can be used alongside blockchain-based systems for real-time fraud detection.Methods: Developing and implementing static anomaly detection methods via a hybrid approach and developing dynamic anomaly detection methods using extreme value theory.Expected results: This research work will be able to contribute to improving the security relating to blockchain-based networks by providing more accurate and efficient methods for detecting anomalies and fraud and reducing the impact of losses resulting from these anomalies.Impact for the field: The project will be particularly beneficial alongside real world blockchain-based networks to allow for the fast detection of anomalous or fraudulent data, preventing damage or allowing for damage to be corrected as soon as possible. For cryptocurrency networks, this will reduce the impact of market manipulation, fraud, and more widely on global financial markets, currencies, and trade. In addition, the project will be of interest to a broad range of cryptocurrency and blockchain stakeholders including (but not limited to) academics, financial institutions, policymakers, regulators, and cybercrime agencies.
 
  
 
= SNSF - NSF: XAI for Finance =
 
= SNSF - NSF: XAI for Finance =

Revision as of 12:03, 17 October 2023

MSCA: Digital Finance

SERI: Digital Finance

COST: application 2023

SNF: Narrative Digital Finance

SNF: Network-based P2P Scoring Models

SNF: Anomaly and fraud detection in blockchain networks

SNSF - NSF: XAI for Finance

ING Cooperation

Retrieved from "https://wiki.fin-ai.eu/index.php?title=DIGITAL&oldid=79633"