Paper evaluates adversarial training for tabular credit scoring robustness in P2P lending

Researchers have published an evaluation of how adversarial training affects the robustness of tabular machine learning credit scoring models used in peer-to-peer lending. The study tests these models against multiple attack types that simulate applicants tweaking self-reported information to influence lending decisions. The work highlights a security gap in financial ML systems that depend on user-provided inputs.