Boosted Decision Trees for Credit Scoring

View Sample PDF

Author(s): Luca Di Persio (University of Verona, Italy)and Alberto Borelli (University of Verona, Italy)
Copyright: 2022
Pages: 23
Source title: Handbook of Research on New Challenges and Global Outlooks in Financial Risk Management
Source Author(s)/Editor(s): Mara Madaleno (GOVCOPP, University of Aveiro, Portugal), Elisabete Vieira (GOVCOPP, University of Aveiro, Portugal)and Nicoleta Bărbuță-Mișu (University of Galati, Romania)
DOI: 10.4018/978-1-7998-8609-9.ch013

Keywords: Accounting and Finance / Business & Management / Business Science Reference / Risk Management & Analysis

Purchase

View Boosted Decision Trees for Credit Scoring on the publisher's website for pricing and purchasing information.

Abstract

The chapter developed a tree-based method for credit scoring. It is useful because it helps lenders decide whether to grant or reject credit to their applicants. In particular, it proposes a credit scoring model based on boosted decision trees which is a technique consisting of an ensemble of several decision trees to form a single classifier. The analysis used three different publicly available datasets, and then the prediction accuracy of boosted decision trees is compared with the one of support vector machines method.

The IRMA Community

Research IRM

Boosted Decision Trees for Credit Scoring

Purchase

Abstract

Related Content

IRMA Sponsors