FinTech

Financial markets break down in predictable ways during stress. When asset correlations spike toward 1.0 during crises, covariance matrices lose rank and risk models struggle. When liquidity evaporates, pricing engines hit undefined states. Standard fixes—eigenvalue floors, ε-regularization, arbitrary caps—solve the immediate numerical problem but introduce bias that compounds through your models. ZeroProofML takes a different approach: when mathematical singularities occur, the system maintains rigorous computation by explicitly tagging results as finite, ±∞, or indeterminate. This preserves the mathematical warning signal rather than smoothing it away, while keeping your calculations stable and deterministic.

Potencial applications:

• Portfolio risk: Calculate valid risk metrics even when correlation matrices become singular during stress events—the mathematical warning is preserved, not smoothed away
• Options trading: Price derivatives consistently through expiry when Greeks explode, eliminating the ad-hoc caps that create arbitrage opportunities
• Credit modeling: Estimate default probabilities for low-default portfolios (sovereigns, investment-grade) without regulatory-questionable floor values
• Execution algorithms: Maintain stable liquidity metrics when markets thin out, capturing regime changes that smoothed models miss