Empowering Credit Scoring Systems with Quantum-Enhanced Machine Learning

TL;DR

This paper introduces a quantum-enhanced machine learning approach called Systemic Quantum Score (SQS) that improves credit scoring, especially with limited or skewed data, showing potential advantages over classical models in finance applications.

Contribution

The paper proposes the SQS method, a novel quantum feature space technique, demonstrating preliminary benefits over classical models like XGBoost in financial credit scoring.

Findings

SQS outperforms classical models with small datasets.

SQS demonstrates better pattern extraction in scarce data scenarios.

Preliminary results indicate potential advantages in finance applications.

Abstract

Quantum Kernels are projected to provide early-stage usefulness for quantum machine learning. However, highly sophisticated classical models are hard to surpass without losing interpretability, particularly when vast datasets can be exploited. Nonetheless, classical models struggle once data is scarce and skewed. Quantum feature spaces are projected to find better links between data features and the target class to be predicted even in such challenging scenarios and most importantly, enhanced generalization capabilities. In this work, we propose a novel approach called Systemic Quantum Score (SQS) and provide preliminary results indicating potential advantage over purely classical models in a production grade use case for the Finance sector. SQS shows in our specific study an increased capacity to extract patterns out of fewer data points as well as improved performance over data-hungry algorithms such as XGBoost, providing advantage in a competitive market as it is the FinTech and Neobank regime.