Simplifying credit scoring rules using LVQ+PSO

TL;DR

This paper introduces LVQ+PSO, a novel method combining neural networks and optimization to simplify credit scoring rules, enabling faster and more efficient classification of customer risk profiles.

Contribution

The paper presents a new approach that reduces the complexity of credit scoring rules using LVQ combined with PSO, improving decision speed and interpretability.

Findings

Achieved a reduced set of classification rules for credit risk.

Demonstrated effectiveness on real Ecuadorian credit data.

Produced satisfactory classification accuracy.

Abstract

One of the key elements in the banking industry rely on the appropriate selection of customers. In order to manage credit risk, banks dedicate special efforts in order to classify customers according to their risk. The usual decision making process consists in gathering personal and financial information about the borrower. Processing this information can be time consuming, and presents some difficulties due to the heterogeneous structure of data. We offer in this paper an alternative method that is able to classify customers' profiles from numerical and nominal attributes. The key feature of our method, called LVQ+PSO, is the finding of a reduced set of classifying rules. This is possible, due to the combination of a competitive neural network with an optimization technique. These rules constitute a predictive model for credit risk approval. The reduced quantity of rules makes this method not only useful for credit officers aiming to make quick decisions about granting a credit, but also could act as borrower's self selection. Our method was applied to an actual database of a credit consumer financial institution in Ecuador. We obtain very satisfactory results. Future research lines are exposed.