2 citations · 2 across the 8 of their papers we have counts for
8 papers
Learning as a Geometric Phase Transition: Renormalization Group Flow and Anisotropic Symmetry Breaking in Deep Networks
G. Le Pera, C. Nordio
We formulate feature learning as a geometric critical phenomenon of the lifted tensor-product learning metric. The central object is not a scalar overlap, but the target-active geo…
Temporal-Aligned Meta-Learning for Risk Management: A Stacking Approach for Multi-Source Credit Scoring
O. Didkovskyi, A. Vidali, N. Jean +1
This paper presents a meta-learning framework for credit risk assessment of Italian Small and Medium Enterprises (SMEs) that explicitly addresses the temporal misalignment of credi…
Bridging Human Cognition and AI: A Framework for Explainable Decision-Making Systems
N. Jean, G. Le Pera
Explainability in AI and ML models is critical for fostering trust, ensuring accountability, and enabling informed decision making in high stakes domains. Yet this objective is oft…
A Framework for Waterfall Pricing Using Simulation-Based Uncertainty Modeling
Nicola Jean, Giacomo Le Pera, Lorenzo Giada +1
We present a novel framework for pricing waterfall structures by simulating the uncertainty of the cashflow generated by the underlying assets in terms of value, time, and confiden…
Unlocking NACE Classification Embeddings with OpenAI for Enhanced Analysis and Processing
Andrea Vidali, Nicola Jean, Giacomo Le Pera
The Statistical Classification of Economic Activities in the European Community (NACE) is the standard classification system for the categorization of economic and industrial activ…
Cross-Domain Behavioral Credit Modeling: transferability from private to central data
O. Didkovskyi, N. Jean, G. Le Pera +1
This paper introduces a credit risk rating model for credit risk assessment in quantitative finance, aiming to categorize borrowers based on their behavioral data. The model is tra…
Machine Learning approach for Credit Scoring
A. R. Provenzano, D. Trifirò, A. Datteo +7
In this work we build a stack of machine learning models aimed at composing a state-of-the-art credit rating and default prediction system, obtaining excellent out-of-sample perfor…
An Artificial Intelligence approach to Shadow Rating
Angela Rita Provenzano, Daniele Trifirò, Nicola Jean +4
We analyse the effectiveness of modern deep learning techniques in predicting credit ratings over a universe of thousands of global corporate entities obligations when compared to…