A new approach to rating scale definition with quantum-inspired optimization
This addresses a complex combinatorial optimization problem for financial institutions, but it is incremental as it applies an existing QUBO formulation to a specific domain.
The paper tackles the problem of defining rating scales for creditworthiness assessment in finance by formulating it as a Quadratic Unconstrained Binary Optimization (QUBO) model, and validates the approach with classical heuristics, showing consistent solution quality compared to brute-force methods.
In finance, assessing the creditworthiness of loan applicants requires lenders to cluster borrowers using rating scales. Financial institutions must define the scales in compliance with strict institutional constraints, resulting in solving a complex combinatorial constrained optimization problem. This contribution studies how to solve this problem using a Quadratic Unconstrained Binary Optimization (QUBO) model, a formulation suitable for quantum hardware. We validate this approach by testing the proposed formulation with classical heuristics. We then benchmark the results against a brute-force method to demonstrate consistent solution quality and highlight the framework's suitability for more complex scenarios.