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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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67133200266 · Jun 202019922001200920172026
48 results for Continuous Compliance Index

Develops a continuous compliance index for Islamic equity screening.

problem Binary rulebooks lead to inconsistent compliance assessment of firms.
method Integrates six leading financial and business activity standards into a single continuous index.
result Firms with the same pass/fail label can differ significantly in compliance strength.

Estimates treatment effects in randomized experiments with non-compliance.

problem Estimating distributional treatment effects in experiments with imperfect compliance.
method Proposes a regression-adjusted estimator based on distribution regression with Neyman-orthogonal moment conditions.
result Achieves semiparametric efficiency bound and demonstrates favorable performance in simulations and real data.

RSI uses Bayesian inference to monitor compliance in rule-governed domains.

problem Structural obstacles in compliance monitoring, including unlabeled outcomes and selective withholding of evidence.
method Rule-State Inference (RSI) treats formalized rules as Bayesian priors and infers compliance states through mean-field variational inference.
result RSI delivers formal guarantees of adaptability, consistency, and convergence, validated on a synthetic enterprise benchmark.

Study assesses environmental management accounting practices in Bangladesh.

problem Low environmental management accounting practices in Bangladeshi manufacturing companies.
method Developed a compliance checklist and evaluated practices using binary scoring.
result Environmental management accounting practices are poor in Bangladeshi manufacturing companies.

Within the Own Risk and Solvency Assessment framework, the Solvency II directive introduces the need for insurance undertakings to have efficient tools enabling the companies to assess the continuous compliance with regulatory solvency requirements. Because of the great operational complexity resulting from each comple…

2013-09-27abs ↗pdf ↗

New approach for estimating individual treatment effects in low compliance settings.

problem Estimating individual treatment effects in scenarios with low compliance.
method Proposes a new approach using Structural Causal Model and do-calculus to estimate Individual Prescription Effect (IPE) with asymptotic variance guarantees.
result Consistently improves state-of-the-art in low compliance settings.

New approach to algorithmic fairness for human-AI collaboration considers compliance with human decisions.

problem Current fairness approaches assume perfect human compliance, but real-world compliance is often poor.
method Defines compliance-robustly fair algorithms and proposes an optimization strategy to improve fairness.
result Algorithmic recommendations can improve fairness even if humans do not fully comply with fair algorithms.

Motivated by clinical trials, we study bandits with observable non-compliance. At each step, the learner chooses an arm, after, instead of observing only the reward, it also observes the action that took place. We show that such noncompliance can be helpful or hurtful to the learner in general. Unfortunately, naively i…

2016-02-09abs ↗pdf ↗

Framework audits synthetic datasets for trustworthiness across various use cases.

problem Assessing the trustworthiness of synthetic datasets and models.
method Holistic auditing framework focusing on bias, fidelity, utility, robustness, and privacy.
result Introduces a trustworthiness index and model selection process for controllable trade-offs.

Combines IV and observational data to estimate CATEs with low compliance and unobserved confounding.

problem Estimating CATEs in personalized medicine and analytics with observational data and weak IVs.
method Two-stage framework: first learns biased CATEs from observational data, then corrects using IV data.
result Effective in estimating CATEs with low compliance and unobserved confounding.

This paper introduces scikit-hubness, a Python package for efficient nearest neighbor search in high-dimensional spaces. Hubness is an aspect of the curse of dimensionality, and is known to impair various learning tasks, including classification, clustering, and visualization. scikit-hubness provides algorithms for hub…

2019-12-02abs ↗pdf ↗

Improved IV estimates by weighting on compliance reduces noise in treatment effect estimation.

problem Noisy IV estimates in settings with non-random treatment receipt.
method Weighting observations by estimated compliance, leveraging machine learning for compliance estimation.
result Compliance weighting reduces IV variance, improving precision of treatment effect estimates.

Bayesian framework improves financial risk management and compliance.

problem Uncertainty in financial risk forecasting and compliance.
method Integrated Bayesian analytics framework for precise uncertainty quantification.
result Proposed DLM model produces more accurate VaR estimates compared to baseline models.

Regulatory compliance is an organization's adherence to laws, regulations, guidelines and specifications relevant to its business. Compliance officers responsible for maintaining adherence constantly struggle to keep up with the large amount of changes in regulatory requirements. Keeping up with the changes entail two …

2019-08-20abs ↗pdf ↗

New model predicts dynamic tax evasion with audits and imitation.

problem Static treatment of tax compliance and evasion in Bertotti and Modanese model.
method Piecewise Deterministic Markov Processes (PDMPs) for audits and imitation mechanisms.
result Model shows persistent fluctuations and stationary distribution, not extreme equilibrium.

Researchers prove an equivariant index theorem on Euclidean space.

problem Calculating the equivariant index of the Bott-Dirac operator on R2n\mathbb{R}^{2n}.
method Continuous field of CC^*-algebras and equivariant index theorem.
result Explicit calculation of the equivariant index of the Bott-Dirac operator on R2n\mathbb{R}^{2n}.

VERAFI improves financial AI by verifying calculations and compliance.

problem Financial AI systems generate errors and violations during reasoning.
method VERAFI combines dense retrieval, reranking, and automated reasoning policies.
result VERAFI achieves 94.7% factual correctness, 81% relative improvement.

Paper tackles continual learning with single-index models, proving regret bounds.

problem Continual learning with single-index models across multiple tasks.
method Proposes a randomized strategy to learn a common single-index and task-specific link functions.
result Proves regret bounds for the proposed strategy under various loss function assumptions.

ML Compass helps organizations choose AI models that balance utility, cost, and compliance.

problem Selecting AI models that meet user utility, deployment costs, and compliance requirements.
method Develops ML Compass, a framework for constrained optimization over a capability-cost frontier, using internal measures and empirical data.
result ML Compass produces deployment-aware recommendations that differ from capability-only rankings, clarifying trade-offs between capability, cost, and safety.

The paper proves continuity of Morse index for Ricci shrinkers.

problem Lower and upper semi-continuity of the Morse index for gradient Ricci shrinkers.
method Adapting and refining recent arguments on CMC hypersurfaces and polynomially weighted Sobolev spaces, with techniques for non-compact shrinkers.
result Identifies a condition ensuring the Morse index of asymptotically conical shrinkers is bounded below by the f-index of their asymptotic cone.

Examines AI regulation in finance, highlighting risks and gaps in current laws.

problem Rapid AI adoption in finance introduces risks and compliance challenges.
method Reviews current legislation, industry guidelines, and real-world use cases.
result Need for adaptive, technology-neutral policies to balance innovation and consumer protection.

We recall the Chernoff-Marsden definition of weak symplectic structure and give a rigorous treatment of the functional analysis and geometry of weak symplectic Banach spaces. We define the Maslov index of a continuous path of Fredholm pairs of Lagrangian subspaces in continuously varying Banach spaces. We derive basic …

2013-01-30abs ↗pdf ↗

Model analyzes trading frictions in cap-and-trade markets, showing how they interact to affect market effectiveness.

problem Analyzing how trading frictions impact cap-and-trade market effectiveness.
method Developed a dynamic stochastic model with multiple trading frictions, characterized access choices in closed form, and quantified using EU ETS data.
result Trading frictions interact to amplify or dampen market responses, and their combined effect is non-additive.

FunBaT extends Tucker decomposition to handle continuous-indexed tensor data.

problem Handling continuous-indexed tensor data that doesn't fit traditional Tucker decomposition.
method FunBaT treats continuous-indexed data as interactions between a core tensor and a group of latent functions modeled by Gaussian processes (GP). It converts each GP into a state-space prior and uses advanced message-passing techniques for scalable inference.
result FunBaT effectively handles real-world data with continuous indexes, demonstrating its advantage in synthetic and real-world applications.

We find various lower and upper bounds for the index of Wente tori that contain a continuous family of planar principal curves. We then prove a result that gives an algorithm for computing the index sharply.

2008-04-26abs ↗pdf ↗

AI-driven framework improves enterprise financial audits and risk identification.

problem Manual auditing is inefficient and limited by data complexity and evolving fraud tactics.
method Machine learning algorithms (SVM, RF, KNN) applied to a dataset of audit project counts, violations, and fraud instances.
result Random Forest achieves best performance with F1-score of 0.9012, identifying fraud and compliance anomalies.

Local EGOP learns functions varying along a few directions.

problem Efficient estimation of functions varying along a few directions in high-dimensional space.
method Local EGOP learning, a recursive algorithm using EGOP quadratic form as metric and inverse-covariance.
result Local EGOP learning achieves intrinsic dimensional learning rates under noisy manifold hypothesis.