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portfolio
Graph Convolutional Network (GCN) model for ex-ante detection of cybercrimes on the Ethereum blockchain
Graph Convolutional Network (GCN) model for ex-ante detection of cybercrimes on the Ethereum blockchain The code implements a Graph Convolutional Network (GCN) model to classify cybercriminal and non-cybercriminal addresses on the Ethereum blockchain, using ATGraph technology. Our approach uses several on-chain input features for classification. It builds on Hornuf, Momtaz, Nam, and Ye (2024). and is designed to perform well in samples of limited size.
publications
Cybercrime on the ethereum blockchain
Published in Journal of Banking and Finance, June 01, 2025
We examine how cybercrime impacts victims’ risk-taking and returns. The results from our difference-in-differences analysis of a sample of victim and matched non-victim investors on the Ethereum blockchain are in line with prospect theory and suggest that victims increase their long-term total risk-taking after losing part of their wealth, leading to lower risk-adjusted returns in the post-cybercrime period. Victims’ long-term total risk-taking increases because they increase diversifiable risk due to victims’ post-cybercrime withdrawal from altcoins. At the same time, the reduction in risk-adjusted returns correlates with increased trading activity and churn, due plausibly to managing cybercrime exposure. In the cross-section of Ethereum addresses, we show that the most affluent victims take a systematic approach to restore their pre-cybercrime wealth level, while the least affluent victims turn into gamblers. Finally, a parsimonious forensic model explains a good part of the addresses’ probability of being involved in cybercrime, on both the victim and the cybercriminal side.
Recommended citation: Hornuf, L., Momtaz, P. P., Nam, R. J., & Yuan, Y. (2023). Cybercrime on the ethereum blockchain. https://doi.org/ 10.2139/ssrn.4527415
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Artificial Intelligence and Crowdfunding
December 30, 2025
Information asymmetries shape the (in)efficiency of entrepreneurial finance markets. We introduce a distinction between information-endowment asymmetry (unequal access to signals) and information-utilization asymmetry (unequal processing of signals). Our study explores whether the onset of generative artificial intelligence (AI) improves signal conversion efficiency in entrepreneurial finance; i.e., the effective utilization of the information endowment. Empirically, we engineer an AI agent that utilizes the information endowment in blockchainbased crowdfunding campaigns to generate investment recommendations, and compare those to recommendations by human experts and a human-built recommendation algorithm. Among all major signals of capital formation in blockchain-based crowdfunding, our AI agent’s advice outperforms statistically and economically: Including our AI agent’s advice as a signal for capital formation raises R-squared by up to 35%, and a one-standard-deviation increase in AI recommendation strength is associated with 48% higher funding amounts. Explainable AI in the form of Shapley values corroborates AI’s superior predictive power. Consistent with our signal conversion efficiency conjecture, we also find that AI-human advice congruence is positively associated with capital formation, while AI-human advice divergence predicts lower funding amounts if investment recommendations by humans are more optimistic than those by AI, highlighting the practical potential of AI as a corrective device for human biases in investment decisions.
Recommended citation: Momtaz, Paul P. and Wiklund, Johan and Yuan, Ye, Artificial Intelligence and Crowdfunding (December 30, 2025). Available at SSRN: https://ssrn.com/abstract=5987114 or http://dx.doi.org/10.2139/ssrn.5987114
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talks
PWC Next Digital Leader Summit 2022
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Entrepreneurial Finance Associations Annual Meeting 2024
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Entrepreneurial Finance Associations Annual Meeting 2024 Presented the paper “Cybercrime on the Ethereum blockchain” in the phd session
Colorcloud 2025
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Colorcloud 2025 Attended the Colorcloud 2025 in Hamburg, Germany, focusing on current trend of Microsoft Business Applications.
AI in entrepreneurship
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AI in entrepreneurship: ETP-Supported Research Workshop Contributed as coauthor to the paper “Artificial intelligence and crowdfunding,” presented at the ETP-Supported Research Workshop on AI in Entrepreneurship (Universiteit van Amsterdam); engaged with researchers and practitioners on AI’s role in entrepreneurship.
teaching
Blockchain course
Lecture, Technical University of Munich, April 15, 2024
Teaching bachelor and master students blockchain and smart contracts.
Seminar Fintech
Seminar, Technical University of Munich, October 15, 2024
Hold the seminar Fintech, teaching students blockchain and smart contract
EMBA TUM
MBA course, Technical University of Munich, December 01, 2024
Teaching MBA students blockchain and smart contract
Advanced Seminar in Fintech: AI agents and blockchain
Master course, Technical University of Munich, October 31, 2025
Ai agents, blockchain