Classification methods applied to credit scoring: A systematic review and overall comparison
By Francisco Louzada, Anderson Ara, Guilherme Barreto Fernandes. 2016
208 citations recorded by the source corpus.
The need for controlling and effectively managing credit risk has led financial institutions to excel in improving techniques designed for this purpose, resulting in the development of various quantitative models by financial institutions and consulting companies. Hence, the growing number of academic studies about credit scoring shows a variety of classification methods applied to discriminate good and bad borrowers. This paper, therefore, aims to present a systematic literature review relating theory and application of binary classification techniques for credit scoring financial analysis. The general results show the use and importance of the main techniques for credit rating, as well as some of the scientific paradigm changes throughout the years.
Subfields: Accounting
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Citations in the corpus
- Interpretable Approaches to Detect Bias in Black-Box Models (2018, background): “Black-box machine learning models permeate our lives and are increasingly being deployed for high stakes decisions, such as credit scoring [13], judicial bail decisions [2], and hospital admissions.”
- Auditing Black-Box Models Using Transparent Model Distillation With Side Information (2017, background): “Risk scoring models have a long history of usage in criminal justice, finance, hiring, and other critical domains that impact people’s lives [12, 35].”