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Industrial research
AI Lab for Finance & Business

Head of operations
Paolo Bajardi

Scientific Leaders
Paolo Bajardi, Francesco Bonchi, André Panisson

Created in the context of the new Industrial Research area of the ISI Foundation, the AI Lab for Finance & Business is a joint effort of ISI Foundation and Intesa Sanpaolo Innovation Center (owned by Intesa Sanpaolo).
This Lab is an industrial research lab aimed at developing novel cutting-edge data science and machine learning techniques for specific real-world cross-industries challenges with special focus on the financial domain. The Lab consists of a team of senior and junior data science researchers, hired by ISI Foundation, and in collaboration with Innovation Center Lab domain experts.

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In collaboration with


Advanced Early Warning System

Leveraging the fully anonymised temporally-resolved networked data of firm-to-firm transactions, the project aims at investigating cascading effects on credit risk. The goal is to devise an actionable pipeline able to extract meaningful features from non-conventional data to outline novel predictive models of economic-financial performance.

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Counterparty Exposure Exploration

The research activities aim to design and prototype a decision support tool for monitoring and exploring derivative contracts. Financial institutions stipulate a wide variety of derivative contracts with different counterparties (central counterparties, financial counterparties, and corporate counterparties), different underlying instruments, and different risk factors.

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The research project aims at designing and prototyping a digital system that allows different parties to compute a global assessment for a common individual without sharing the local. The system will be based on cryptographic tools (e.g. homomorphic encryption techniques) and accomplish with current data protection laws (e.g., GDPR).

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Risk Overlay

The project will investigate the application of machine learning techniques to the problem of tail risk hedging for investment portfolios. The goal is to develop an innovative algorithmic pipeline to identify risk drivers in financial portfolios and to devise optimal strategies to mitigate such risks.

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XVA Calibration

Modelling valuation adjustments (collectively known as XVA) represents one of the toughest challenges in derivatives pricing and financial engineering at large, as it involves modelling net future exposure with portfolio effects, based on consistent specifications of market price dynamics under the risk neutral measure across several currencies and asset classes.

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