Improving Classifier Performance Assessment of Credit Scoring Models

In evaluating credit scoring predictive power it is common to use the Re-ceiver Operating Characteristics (ROC) curve, the Area Under the Curve(AUC) and the minimum probability-weighted loss. The main weakness of the rst two assessments is not to take the costs of misclassication errors into account and the last one depends on the number of defaults in the credit portfolio. The main purposes of this paper are to provide a curve, called curve of Misclassication Error Loss (MEL), and a classier performance measure that overcome the above-mentioned drawbacks. We prove that the ROC dominance is equivalent to the MEL dominance. Furthermore, we derive the probability distribution of the proposed predictive power measure and we analyse its performance by Monte Carlo simulations. Finally, we apply the suggested methodologies to empirical data on Italian Small and Medium Enterprisers.Performance Assessment, Credit Scoring Modules, Monte Carlo simulations, Italian Enterprisers

Downturn Loss Given Default: Mixture distribution estimation

The internal estimates of Loss Given Default (LGD) must reflect economic downturn conditions, thus estimating the “downturn LGD”, as the new Basel Capital Accord Basel II establishes. We suggest a methodology to estimate the downturn LGD distribution to overcome the arbitrariness of the methods suggested by Basel II. We assume that LGD is a mixture of an expansion and recession distribution. In this work, we propose an accurate parametric model for LGD and we estimate its parameters by the EM algorithm. Finally, we apply the proposed model to empirical data on Italian bank loan

Generalized Extreme Value Regression for Binary Rare Events Data: an Application to Credit Defaults

The most used regression model with binary dependent variable is the logistic regression model. When the dependent variable represents a rare event, the logistic regression model shows relevant drawbacks. In order to overcome these drawbacks we propose the Generalized Extreme Value (GEV) regression model. In particular, in a Generalized Linear Model (GLM) with binary dependent variable we suggest the quantile function of the GEV distribution as link function, so our attention is focused on the tail of the response curve for values close to one. The estimation procedure is the maximum likelihood method. This model accommodates skewness and it presents a generalization of GLMs with log-log link function. In credit risk analysis a pivotal topic is the default probability estimation. Since defaults are rare events, we apply the GEV regression to empirical data on Italian Small and Medium Enterprises (SMEs) to model their default probabilities.

Bankruptcy Prediction of Small and Medium Enterprises Using a Flexible Binary Generalized Extreme Value Model

We introduce a binary regression accounting-based model for bankruptcy prediction of small and medium enterprises (SMEs). The main advantage of the model lies in its predictive performance in identifying defaulted SMEs. Another advantage, which is especially relevant for banks, is that the relationship between the accounting characteristics of SMEs and response is not assumed a priori (e.g., linear, quadratic or cubic) and can be determined from the data. The proposed approach uses the quantile function of the generalized extreme value distribution as link function as well as smooth functions of accounting characteristics to flexibly model covariate effects. Therefore, the usual assumptions in scoring models of symmetric link function and linear or pre-specied covariate-response relationships are relaxed. Out-of-sample and out-of-time validation on Italian data shows that our proposal outperforms the commonly used (logistic) scoring model for different default horizons

Detecting Consumers' Financial Vulnerability using Open Banking Data: Evidence from UK Payday Loans

Behind the debt trap concept is the rationale that payday loans exacerbate consumers' financial vulnerability. To investigate this relationship, we propose a Mixed Poisson Hidden Markov approach to model the number of payday loans a borrower obtains in each period. Given the lack of agreement in the literature on financial vulnerability, we introduce financial distress as an unobserved binary variable using a hidden Markov process (vulnerable and non-vulnerable). Using data from 90,523 anonymised transactions for 1,817 UK consumers, we find that the effect of certain time-varying covariates depends greatly on the borrower's hidden state. For instance, luxury expenses and non-recurring income increase the need for payday loans when financially vulnerable, but the opposite is true when not vulnerable. Additionally, we demonstrate that almost 60\% of payday loan borrowers remain vulnerable for 12 or more consecutive weeks, with two-thirds experiencing consistent financial difficulties. Finally, our analysis underscores the need for a nuanced approach to payday lending that recognises the varying levels of vulnerability among borrowers, which can prove helpful for policymakers and lenders to enhance responsible lending practices