loading
  • Login
  • Sign-Up

Research.Publish.Connect.

Paper

Authors: Roberto Saia and Salvatore Carta

Affiliation: Università di Cagliari, Italy

ISBN: 978-989-758-203-5

Keyword(s): Business Intelligence, Credit Scoring, Fraud Detection, Data Mining, Metrics.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Business Intelligence Applications ; Computational Intelligence ; Data Mining in Electronic Commerce ; Evolutionary Computing ; Information Extraction ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Soft Computing ; Symbolic Systems

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 matri x 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. (More)

PDF ImageFull Text

Download
Sign In Guest: Register as new SCITEPRESS user or Join INSTICC now for free.

Sign In SCITEPRESS user: please login.

Sign In INSTICC Members: please login. If not a member yet, Join INSTICC now for free.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 54.198.1.167. INSTICC members have higher download limits (free membership now)

In the current month:
Recent papers: 1 available of 1 total
2+ years older papers: 2 available of 2 total

Paper citation in several formats:
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 (IC3K 2016)ISBN 978-989-758-203-5, pages 111-120. DOI: 10.5220/0006066701110120

@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 (IC3K 2016)},
year={2016},
pages={111-120},
doi={10.5220/0006066701110120},
isbn={978-989-758-203-5},
}

TY - CONF

JO - Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (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

Sorted by: Show papers

Note: The preferred Subjects/Areas/Topics, listed below for each paper, are those that match the selected paper topics and their ontology superclasses.
More...

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.

Show authors

Note: The preferred Subjects/Areas/Topics, listed below for each author, are those that more frequently used in the author's papers.
More...