Andrea Mazzali

PhD in Artificial Intelligence

Intelligent systems,for Finance.

Exploring the intersection of artificial intelligence, statistics and financial systems.

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01 · About

Research at the intersection of AI, statistics and finance.

I explore how artificial intelligence and statistical methods can be used to understand complex financial systems and develop more intelligent, reliable and data-driven decision tools.

My research focuses on machine learning, synthetic data generation and quantitative modelling, with particular attention to banking and financial applications. I am interested in building systems that combine methodological rigour with practical relevance.

Portrait of Andrea Mazzali
Andrea MazzaliPhD Student in Artificial Intelligence
Currently
PhD Student in Artificial Intelligence
Institution
University of Bergamo
Research focus
Artificial Intelligence · Statistics · Finance
Academic background
Master's Degree in FinanceUniversity of Pavia
Bachelor's Degree in EconomicsUniversity of Pavia
Artificial intelligenceStatistical modellingFinancial systems

02 · Research

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Machine learning enables Artificial intelligenceDeep learning extends Artificial intelligenceStatistics supports Artificial intelligenceArtificial intelligence enables Synthetic dataArtificial intelligence applies to Exit strategiesMachine learning applies to Financial systemsMachine learning applies to Private equityDeep learning enables Synthetic dataSynthetic data applies to BankingStatistics supports Synthetic dataStatistics supports Network methodsStatistics applies to BankingStatistics applies to Financial systemsNetwork methods applies to BankingNetwork methods informs Private equityPrivate equity informs Exit strategiesFinancial systems informs Private equityFinancial systems informs Exit strategiesArtificial intelligenceMachine learningDeep learningSynthetic dataBankingStatisticsNetwork methodsPrivate equityExit strategiesFinancial systems

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03 · Publications

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01
2026Journal of the Royal Statistical Society Series A: Statistics in Society

A network-based distributional inference approach to model country-level cyber risk

Alessandro Spelta, Silvia Facchinetti, Silvia Angela Osmetti, et al.

This article leverages Wasserstein Propagation in Social Network to propose a novel distributional framework for the inference of cyber risk across interconnected economic systems. Cyber attacks represent an increasing threat to global security and economic stability, making the assessment of cyber risk…

Network methodsStatistics
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04 · Articles

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01
5 October 202650 min read53 equations

Leakage-Aware Reconstruction of Account-Level Transaction-Amount Distributions via Wasserstein Harmonic Propagation

Andrea Mazzali

This study examines the transductive reconstruction of account-level transaction-amount distributions when target profiles are unavailable for a subset of accounts while non-target covariates remain available. Each sender is represented by a distribution on four globally defined ordinal amount classes. Behavioural similarity…

NetworksStatisticsArtificial intelligenceBanking
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