Modelli di stima della probabilità di default. Il caso di una banca locale.

Emiliano Mastrolia


Il lavoro presenta i metodi per stimare la probabilità di default (PD) come parametro cruciale nella gestione del rischio di credito delle banche. Utilizzando un campione di 496 imprese del portafoglio crediti di una banca locale e utilizzando indicatori finanziari sono stati sviluppati un modello discriminante lineare e un modello logistico per la previsione di insolvenza delle imprese. I risultati evidenziano che entrambi i modelli sono in grado di classificare correttamente oltre il 95% delle imprese campionate. Tuttavia, il modello logistico ha prestazioni migliori nel ridurre l'errore di tipo I. Pertanto, nelle piccole banche la regressione logistica rappresenta uno strumento utile per stimare la probabilità di default delle imprese e per migliorare le strategie di pricing e monitoraggio del credito.


Based on a sample of 496 firms from the loans portfolio of a local bank and using financial variables we estimate a linear discriminant function for predicting firm default and we used a logistic model to estimate the probability of default. Results highlight that both the models are able to correctly classify over the 95% of sampled firms. However, logistic regression has a better performance in reducing type I error. Therefore, logistic regression represents a useful tool for estimating the probability of default of firms and for improving credit management strategies.

Parole chiave

Default, credit scoring, analisi discriminante, regression logistica

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ISSN: 1971-5293

ISSNe: 2283-3374

Esperienze d'Impresa, Reg. Tribunale Salerno n. 875 del 3/11/1993