Compliance Moral Hazard and the Backfiring Mandate
- URL: http://arxiv.org/abs/2604.21789v2
- Date: Sun, 26 Apr 2026 18:07:53 GMT
- Title: Compliance Moral Hazard and the Backfiring Mandate
- Abstract summary: Competing firms that serve shared customer populations face a fundamental information aggregation problem.<n>We develop a mechanism design framework for decentralized risk analytics grounded in anti-money laundering in banking networks.<n>We show competitive pressure amplifies compliance moral hazard and poorly designed mandates can reduce welfare below autarky.
- Score: 6.584016782022881
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Competing firms that serve shared customer populations face a fundamental information aggregation problem: each firm holds fragmented signals about risky customers, but individual incentives impede efficient collective detection. We develop a mechanism design framework for decentralized risk analytics, grounded in anti-money laundering in banking networks. Three strategic frictions distinguish our setting: compliance moral hazard, adversarial adaptation, and information destruction through intervention. A temporal value assignment (TVA) mechanism, which credits institutions using a strictly proper scoring rule on discounted verified outcomes, implements truthful reporting as a Bayes--Nash equilibrium (uniquely optimal at each edge) in large federations. Embedding TVA in a banking competition model, we show competitive pressure amplifies compliance moral hazard and poorly designed mandates can reduce welfare below autarky, a ``backfiring'' result with direct policy implications. In simulation using a synthetic AML benchmark, TVA achieves substantially higher welfare than autarky or mandated sharing without incentive design.
Related papers
- Counterfactual Shapley Credit Assignment [49.39649767581182]
Credit Assignment Problem (CAP) is fundamental to developing efficient and explainable Reinforcement Learning (RL) agents.<n>We introduce Counterfactual Shapley Credit Assignment, a novel framework that attributes credit and blame via the Counterfactual Shapley ($$-value)<n>We derive a consistent estimator that computes $$$-values, enabling a new class of policy methods, $$-PPO, combined with Prioritized Trajectory Replay (PTR)
arXiv Detail & Related papers (2026-07-18T23:28:20Z) - Towards a Risk-Cost Model for Financial Adaptive Authentication [9.133320151595084]
We introduce a formal Risk-Cost Model (RCM) for adaptive authentication in financial systems.<n>The RCM 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), and (ii) sequential decision-making mechanisms that adapt to adversarial probing and distributional drift.
arXiv Detail & Related papers (2026-05-04T02:08:24Z) - Utilizing and Calibrating Hindsight Process Rewards via Reinforcement with Mutual Information Self-Evaluation [50.696688705287755]
We propose Mutual Information Self-Evaluation to overcome the sparse reward challenge in reinforcement learning.<n>MISE enables an agent to learn autonomously from dense internal rewards supplementing sparse extrinsic signals.<n>We prove that utilizing hindsight self-evaluation rewards is equivalent to minimizing an objective that combines mutual information with a KL divergence term between the policy and a proxy reward policy.
arXiv Detail & Related papers (2026-04-13T15:18:51Z) - Quantifying Trust: Financial Risk Management for Trustworthy AI Agents [27.23487816533652]
We introduce ARS, a payment settlement standard for AI-mediated transactions.<n>ARS integrates risk assessment, underwriting, and compensation into a single transaction framework.<n>ARS shifts trust from an implicit expectation about model behavior to an explicit, measurable, and enforceable product guarantee.
arXiv Detail & Related papers (2026-04-05T05:42:20Z) - Byzantine-Robust and Differentially Private Federated Optimization under Weaker Assumptions [89.52532304099522]
Federated Learning (FL) enables clients to collaboratively train a shared model without centralizing their raw data, offering an inherent level of privacy.<n> gradients and model updates can still leak sensitive information, while malicious servers may mount adversarial attacks such as Byzantine manipulation.<n>These vulnerabilities highlight the need to address differential privacy (DP) and Byzantine robustness within a unified framework.<n>We propose Byz-Clip21-SGD2M, a new algorithm that integrates robust aggregation with double momentum and carefully designed clipping.
arXiv Detail & Related papers (2026-03-24T17:39:09Z) - Risk-Adjusted Harm Scoring for Automated Red Teaming for LLMs in Financial Services [0.0]
We propose a risk-aware evaluation framework for security failures in Banking, Financial Services, and Insurance.<n>We combine a domain-specific taxonomy of financial harms, an automated multi-round red-teaming pipeline, and an ensemble-based judging protocol.
arXiv Detail & Related papers (2026-03-11T14:14:13Z) - Quantifying Automation Risk in High-Automation AI Systems: A Bayesian Framework for Failure Propagation and Optimal Oversight [1.6328866317851185]
We propose a parsimonious Bayesian risk decomposition expressing expected loss as the product of three terms.<n>This framework captures execution and oversight risk rather than model accuracy alone.<n>We motivate the framework with an illustrative case study of the 2012 Knight Capital incident as one instantiation of a broadly applicable failure pattern.
arXiv Detail & Related papers (2026-02-22T00:18:23Z) - Outcome-Grounded Advantage Reshaping for Fine-Grained Credit Assignment in Mathematical Reasoning [60.00161035836637]
Group Relative Policy Optimization has emerged as a promising critic-free reinforcement learning paradigm for reasoning tasks.<n>We introduce Outcome-grounded Advantage Reshaping (OAR), a fine-grained credit assignment mechanism that redistributes advantages based on how much each token influences the model's final answer.<n>OAR-G achieves comparable gains with negligible computational overhead, both significantly outperforming a strong GRPO baseline.
arXiv Detail & Related papers (2026-01-12T10:48:02Z) - Adaptive Accountability in Networked MAS: Tracing and Mitigating Emergent Norms at Scale [2.28438857884398]
Large-scale networked multi-agent systems increasingly underpin critical infrastructure.<n>We introduce an adaptive accountability framework that traces responsibility flows through a lifecycle-aware audit ledger.<n>We prove a bounded-compromise theorem showing that whenever the expected intervention cost exceeds an adversary's payoff, the long-run proportion of compromised interactions is bounded by a constant strictly less than one.
arXiv Detail & Related papers (2025-12-21T02:04:47Z) - Cannot See the Forest for the Trees: Invoking Heuristics and Biases to Elicit Irrational Choices of LLMs [83.11815479874447]
We propose a novel jailbreak attack framework, inspired by cognitive decomposition and biases in human cognition.<n>We employ cognitive decomposition to reduce the complexity of malicious prompts and relevance bias to reorganize prompts.<n>We also introduce a ranking-based harmfulness evaluation metric that surpasses the traditional binary success-or-failure paradigm.
arXiv Detail & Related papers (2025-05-03T05:28:11Z) - Chat Bankman-Fried: an Exploration of LLM Alignment in Finance [4.892013668424246]
As jurisdictions enact legislation on AI safety, the concept of alignment must be defined and measured.<n>This paper proposes an experimental framework to assess whether large language models (LLMs) adhere to ethical and legal standards in the relatively unexplored context of finance.
arXiv Detail & Related papers (2024-11-01T08:56:17Z) - Certifiably Byzantine-Robust Federated Conformal Prediction [49.23374238798428]
We introduce a novel framework Rob-FCP, which executes robust federated conformal prediction effectively countering malicious clients.
We empirically demonstrate the robustness of Rob-FCP against diverse proportions of malicious clients under a variety of Byzantine attacks.
arXiv Detail & Related papers (2024-06-04T04:43:30Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.