What to Do When Your Credit Risk Model Works Today, but Breaks Six Months Later

Credit risk models often fail over time because they are optimized for simple classification accuracy instead of the more crucial task of correctly ranking borrowers by risk. A novel solution borrows principles from physics, using symplectic geometry—the same math that keeps simulated planets in stable orbits—to prevent this model degradation. This approach reformulates neural network training as a Hamiltonian system, employing a special structure-preserving optimizer and loss function to maintain the relative ordering of risk predictions. Tested on real-world loan data, this Hamiltonian-inspired model demonstrated far superior long-term stability, degrading three times slower than standard methods like XGBoost.