  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.
