Empowering Credit Scoring Systems with Quantum-Enhanced Machine Learning
This work addresses credit scoring challenges in the FinTech and Neobank sector, but it appears incremental as it builds on existing quantum kernel projections with preliminary results.
The paper tackles the problem of credit scoring with scarce and skewed data by proposing a novel quantum-enhanced machine learning approach called Systemic Quantum Score (SQS), which shows preliminary results indicating potential advantage over classical models like XGBoost in extracting patterns from fewer data points.
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.