A Linear-dependence-based Approach to Design Proactive Credit Scoring Models

Roberto Saia, Salvatore Carta

2016

Abstract

The main aim of a credit scoring model is the classification of the loan customers into two classes, reliable and unreliable customers, on the basis of their potential capability to keep up with their repayments. Nowadays, credit scoring models are increasingly in demand, due to the consumer credit growth. Such models are usually designed on the basis of the past loan applications and used to evaluate the new ones. Their definition represents a hard challenge for different reasons, the most important of which is the imbalanced class distribution of data (i.e., the number of default cases is much smaller than that of the non-default cases), and this reduces the effectiveness of the most widely used approaches (e.g., neural network, random forests, and so on). The Linear Dependence Based (LDB) approach proposed in this paper offers a twofold advantage: it evaluates a new loan application on the basis of the linear dependence of its vector representation in the context of a matrix composed by the vector representation of the non-default applications history, thus by using only a class of data, overcoming the imbalanced class distribution issue; furthermore, it does not exploit the defaulting loans, allowing us to operate in a proactive manner, by addressing also the cold-start problem. We validate our approach on two real-world datasets characterized by a strong unbalanced distribution of data, by comparing its performance with that of one of the best state-of-the-art approach: random forests.

Paper Citation

in Harvard Style

Saia R. and Carta S. (2016). A Linear-dependence-based Approach to Design Proactive Credit Scoring Models . In Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016) ISBN 978-989-758-203-5, pages 111-120. DOI: 10.5220/0006066701110120

in Bibtex Style

@conference{kdir16,
author={Roberto Saia and Salvatore Carta},
title={A Linear-dependence-based Approach to Design Proactive Credit Scoring Models},
booktitle={Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016)},
year={2016},
pages={111-120},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006066701110120},
isbn={978-989-758-203-5},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016)
TI - A Linear-dependence-based Approach to Design Proactive Credit Scoring Models
SN - 978-989-758-203-5
AU - Saia R.
AU - Carta S.
PY - 2016
SP - 111
EP - 120
DO - 10.5220/0006066701110120