Towards a Risk-Cost Model for Financial Adaptive Authentication

TL;DR

This paper introduces a formal Risk-Cost Model for adaptive financial authentication, integrating economic, adversarial, and regulatory considerations into a unified, dynamic decision-making framework.

Contribution

It presents a novel mathematical foundation that explicitly models fraud, opportunity, and tail risks within an adaptive, risk-aware authentication system.

Findings

The RCM framework explicitly incorporates CVaR for tail risk management.

It enables adaptive authentication decisions under adversarial and distributional shifts.

The model aligns security with economic and regulatory constraints.

Abstract

Authentication in financial systems remains a uniquely high-stakes security challenge, where even marginal increases in false acceptance can result in catastrophic monetary loss. Existing deployments of adaptive authentication, which combine biometrics, behavioral signals, and contextual risk scoring, remain conceptually fragmented and often prioritize regulatory compliance over explicit economic and adversarial risk modeling. To address this structural imbalance, in this paper we introduce a formal Risk-Cost Model (RCM) for adaptive authentication in financial systems. The RCM provides a principled mathematical foundation that integrates three essential components: (i) cost-sensitive risk functions that explicitly capture fraud loss, opportunity cost, and tail risk through Conditional Value-at-Risk (CVaR); (ii) sequential decision-making mechanisms that adapt to adversarial probing and distributional drift; and (iii) quantifiable privacy and regulatory constraints embedded directly within the optimization objective. By reframing authentication as a constrained dynamic risk-cost optimization problem, the RCM moves beyond static classification and compliance-driven design toward systems that are economically grounded, tail-risk aware, and resilient under adversarial uncertainty.