Systemic Risk | Stefano Blando

RiskSentinel - Agentic Systemic Risk Simulator

Sun, 15 Mar 2026 00:00:00 +0000

RiskSentinel is an agentic systemic risk simulator built around a question I find both practical and conceptually interesting: if a large financial node is hit by a shock, how can we make the propagation of that stress visible, comparable, and explainable in real time?

Built for the Microsoft AI Dev Days Hackathon 2026, the project combines three contagion models, topology-aware analytics, and a bounded multi-agent workflow on top of research-grade market network data. The purpose is not only to simulate cascades, but to make them easier to inspect through natural-language interaction, interactive visualization, and model comparison.

What makes the project work is the combination of several layers that are often separated: a proper network simulation engine, an interface that makes the results explorable, and an agentic layer that helps interpret what is happening without pretending to replace the underlying quantitative machinery.

The result is a practical prototype for systemic risk monitoring, sitting between quantitative finance, complex systems, and AI-assisted decision support.

You can explore the project here:

RiskSentinel for Microsoft AI Dev Days 2026

Sun, 15 Mar 2026 00:00:00 +0000

RiskSentinel is the project I built for the Microsoft AI Dev Days Hackathon 2026.

The core idea was to create a system that could make systemic risk more explorable and more tangible: instead of treating contagion as an abstract output in a paper or a notebook, I wanted an interface where shocks could be launched, propagated, compared, and interpreted in real time.

The project combines network science, contagion modeling, and agentic AI on top of research-grade financial network data covering 210 S&P 500 stocks and 3,081 daily snapshots. Under the hood, it integrates three propagation models, interactive network analytics with Streamlit and Plotly, and an agentic workflow built with Microsoft Agent Framework and Azure OpenAI.

What I like most about this project is that it sits exactly at the boundary between my research interests and practical prototyping: financial networks, complex systems, decision support, and AI agents all in the same tool.

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GitHub:
App:

Master Graduation

Fri, 23 Jan 2026 00:00:00 +0000

I am proud to share that on Monday, February 2, 2026, I was awarded the II Level Master in Customer Experience, Statistics, Machine Learning and Artificial Intelligence (CESMA) at the University of Rome Tor Vergata, with a final grade of 110/110 cum laude.

The Thesis

My thesis, titled “Network Topology Analysis and Machine Learning Techniques for Systemic Risk Prediction in U.S. Equity Markets”, explores the application of Graph Neural Networks and complex network theory to identify early warning signals in financial markets.

This work is directly connected to my ongoing research. You can explore the technical details and code in the dedicated sections of this portfolio:

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The master provided a strong foundation in advanced statistical methods and AI, which I am now applying to my PhD research at Scuola Superiore Sant’Anna.

Network Topology Analysis and Machine Learning Techniques for Systemic Risk Prediction in U.S. Equity Markets

Thu, 15 Jan 2026 00:00:00 +0000

Network Topology Analysis for Systemic Risk Prediction

Sat, 10 Jan 2026 00:00:00 +0000

This project corresponds to my CESMA thesis on systemic risk prediction in U.S. equity markets. The underlying question is simple but important: can network topology say something useful about market stress before standard indicators do?

Using daily data from 210 S&P 500 constituents (2013-2025), the project combines dynamic correlation networks with machine learning models ranging from gradient boosting to Graph Neural Networks such as GraphSAGE and GAT. The goal is not just classification accuracy, but economic usefulness under realistic validation and backtesting constraints.

The most interesting result is that network-derived signals appear to carry genuine early-warning information, especially around severe and structurally fragile market states. In the strongest configurations, the framework improves both timing and trading performance relative to simpler baselines.

This project is paired with the related thesis page, where the same work is presented as a publication entry.