A Model Fusion Approach for Enhancing Credit Approval Decision Making

TL;DR

This paper introduces Combinatorial Fusion Analysis (CFA), a model fusion framework that combines multiple machine learning algorithms to improve credit approval prediction accuracy, outperforming traditional methods.

Contribution

The paper proposes a novel model fusion framework, CFA, that effectively combines pre-trained models for enhanced credit approval decision-making.

Findings

CFA achieves 89.13% accuracy in credit approval prediction.

CFA outperforms conventional machine learning and ensemble methods.

The framework demonstrates high effectiveness in credit default risk management.

Abstract

Credit default poses significant challenges to financial institutions and consumers, resulting in substantial financial losses and diminished trust. As such, credit default risk management has been a critical topic in the financial industry. In this paper, we present Combinatorial Fusion Analysis (CFA), a model fusion framework, that combines multiple machine learning algorithms to detect and predict credit card approval with high accuracy. We present the design methodology and implementation using five pre-trained models. The CFA results show an accuracy of 89.13% which is better than conventional machine learning and ensemble methods.