Research
On-device research index

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

Trend · papers per month

18355370 · Jun 202019922001200920172026
48 results for regulatory fairness

Monotonic neural additive models simplify machine learning for credit scoring.

problem Complex machine learning methods make models less transparent and fair.
method Introducing monotonic neural additive models that simplify neural networks while maintaining regulatory compliance.
result Monotonic neural additive models achieve similar accuracy to complex neural networks but are more transparent and fair.

New fairness criterion for risk-sensitive decisions in regulated industries.

problem Ensuring equitable outcomes in risk-sensitive decision-making.
method Marginal fairness for generalized distortion risk measures, two-step decision-making process.
result Ensures fairness in decision-making under risk measures, regardless of protected attributes.

The paper discusses fairness in bank stress tests, comparing various methods to address institutional differences.

problem Fair aggregation of bank-specific stress test models into a common model.
method Comparing various notions of regression fairness, including estimating and discarding centered bank fixed effects.
result The method of estimating and discarding centered bank fixed effects is preferable for linear models, improving forecast accuracy and equal treatment.

Paper proposes a method to identify and treat latent discriminating features in machine learning models.

problem Fairness issues in machine learning models trained on historical data containing sensitive attributes.
method A novel algorithm that identifies and treats latent discriminating features, agnostic of the learning algorithm.
result Experimental results show near-ideal fairness measurement compared to other methods.

Paper develops methods for fair insurance pricing without direct access to sensitive attributes.

problem Fairness in insurance pricing with restricted access to sensitive attributes.
method Develops statistical methods for estimating discrimination-free premiums using privatized sensitive attributes.
result The proposed methods enable fair insurance pricing while respecting privacy and regulatory constraints.

Extends Demographic Parity for fairer wage predictions with expert knowledge.

problem Inadequate fairness metrics limit user domain knowledge and ignore intersectional fairness.
method Develops a parametric method to incorporate expert knowledge in fair predictions.
result Offers a robust solution for real-life applications with limited data and spending constraints.

LDA-XGB1 balances fairness and accuracy in lending models.

problem Fair lending practices and model interpretability in binary classification.
method Biobjective optimization using binning and information value, leveraging XGBoost.
result Achieves effective balance between accuracy, fairness, and interpretability.

Tackling climate change is at the top of many agendas. In this context, emission trading schemes are considered as promising tools. The regulatory framework for an emission trading scheme introduces a market for emission allowances and creates a need for risk management by appropriate financial contracts. In this work,…

2010-11-26abs ↗pdf ↗

Regulating crypto and DeFi for inclusive economic advancement.

problem Innovative financial systems pose challenges to traditional regulatory frameworks.
method Formulating regulatory structures that balance innovation and consumer protection.
result Regulatory frameworks are essential for leveraging crypto and DeFi for inclusive economic growth.

Machine Learning community is recently exploring the implications of bias and fairness with respect to the AI applications. The definition of fairness for such applications varies based on their domain of application. The policies governing the use of such machine learning system in a given context are defined by the c…

2018-09-12abs ↗pdf ↗

Study aims to measure and mitigate biases in motor insurance pricing.

problem Ethical biases in motor insurance pricing that affect fairness and regulatory compliance.
method Statistical methodologies and data analysis to measure and mitigate biases.
result Developed tools to measure and mitigate ethical biases in motor insurance pricing.

Order matching systems form the backbone of modern equity exchanges, used by millions of investors daily. Thus, their operation is strictly controlled through numerous regulatory directives to ensure that markets are fair and transparent. Despite these efforts, market manipulation remains an open problem. In this work,…

2019-03-29abs ↗pdf ↗

Study explores fairness in financial deep learning through multi-scale trust quantification.

problem Ensuring fairness in financial deep learning models, especially under regulatory compliance.
method Conducts multi-scale trust quantification on a deep neural network for credit card default prediction.
result Demonstrates the feasibility and utility of multi-scale trust quantification for financial deep learning fairness.

This study examines the execution phase of corporate share buy-backs, highlighting inefficiencies and costs.

problem Lack of research on share buy-back execution practices and associated costs.
method Comparative analysis of execution practices and fees charged to corporations and investors.
result Uncovered inefficiencies and frictional costs in share buy-back executions, advocating for transparency and fairness.

This paper tackles post-trade allocation inefficiencies and presents a uniform return allocation method.

problem Return divergence among accounts after trade allocation.
method Systematic treatment of trade allocation risk, presenting a uniform return allocation method.
result Uniform allocation of returns irrespective of the number of accounts and trade sizes.

Over the last 23 years, the U.S. Securities and Exchange Commission has required over 34,000 companies to file over 165,000 annual reports. These reports, the so-called "Form 10-Ks," contain a characterization of a company's financial performance and its risks, including the regulatory environment in which a company op…

2016-12-29abs ↗pdf ↗

We introduce a formal framework for analyzing trades in financial markets. An exchange is where multiple buyers and sellers participate to trade. These days, all big exchanges use computer algorithms that implement double sided auctions to match buy and sell requests and these algorithms must abide by certain regulator…

2019-07-18abs ↗pdf ↗

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.

This paper examines market misconduct in DeFi and proposes regulatory solutions.

problem Novel forms of market misconduct in DeFi.
method Comprehensive analysis, comparative study, empirical measurements, and tailored regulatory framework investigation.
result Identification of key areas for regulatory enhancement in DeFi.

Paper develops a framework to discover bioprocessing regulatory mechanisms using symbolic and statistical learning.

problem Challenges in modeling complex intracellular regulation, stochastic system behavior, and limited experimental data.
method Symbolic and statistical learning framework based on stochastic differential equations and Bayesian learning.
result Improved sample efficiency and robust model selection compared to state-of-the-art approaches.

Regulating causal effects through averaged constraints fails to enforce conditional independence.

problem Enforcing conditional independence in regulatory and analytic settings.
method Formulated causal masking as a linear program and analyzed the resulting enforcement problem from both regulator and optimizer perspectives.
result Averaged-constraint optimization often violates stratum-wise requirements while satisfying the averaged one exactly, and detection requires conditional-independence tests.

The paper addresses bias amplification in prediction and decision-making using causal analysis.

problem Bias amplification in automated systems, especially after thresholding.
method Introduces margin complement and causal decomposition of prediction disparities.
result Disparity in predictor Y^\widehat Y can be decomposed into causal influences of XX on SS and MM.

Financial decisions impact our lives, and thus everyone from the regulator to the consumer is interested in fair, sound, and explainable decisions. There is increasing competitive desire and regulatory incentive to deploy AI mindfully within financial services. An important mechanism towards that end is to explain AI d…

2019-06-24abs ↗pdf ↗

TNDE quantifies dynamic gene drivers from single-cell snapshots.

problem Reconstructing time-resolved regulatory effects in biological processes.
method Time-varying Network Driver Estimation (TNDE) using shared graph attention encoder and partial optimal transport.
result TNDE identifies stage-specific driver genes in mouse erythropoiesis.

LLMs can collude in market divisions, maximizing profits.

problem Strategic collusion of LLM agents in multi-commodity markets.
method Examined LLMs in Cournot competition frameworks, analyzing pricing and resource allocation strategies.
result LLMs can monopolize specific commodities without direct human input or explicit collusion commands.

Hybrid approach protects privacy while analyzing smart meter data.

problem Privacy concerns in AMI data analysis under CPUC regulations.
method Anonymization, differential privacy, federated learning, synthetic data, cryptography.
result Comprehensive privacy-preserving analytics framework for AMI data.

Paper constructs a CRRIX index to assess cryptocurrency market risks from regulatory changes.

problem Lack of indices quantifying regulatory risks in cryptocurrencies.
method CRRIX index based on news coverage frequency, using Latent Dirichlet Allocation and Hellinger distance.
result CRRIX successfully captures major policy-changing moments and synchronizes with market volatility.

InfoSEM infers gene regulatory networks without GT labels, improving performance.

problem Inferring GRNs from gene expression data with high accuracy and avoiding biases.
method InfoSEM uses deep generative models with informative priors (textual gene embeddings).
result InfoSEM outperforms existing models by 38.5% across four datasets.

New method constructs confidence bands for ODE models with unknown regulatory effects.

problem Building confidence bands for ODE models with unknown regulatory relations is challenging.
method Localized kernel learning approach combined with de-biasing method.
result The constructed confidence band has the desired asymptotic coverage probability and accurately recovers the regulatory network.

Gene regulatory networks play a crucial role in controlling an organism's biological processes, which is why there is significant interest in developing computational methods that are able to extract their structure from high-throughput genetic data. Many of these computational methods are designed to infer individual …

2019-09-03abs ↗pdf ↗

Funding is a cost to trading desks that they see as an input. Current FVA-related literature reflects this by also taking funding costs as an input, usually constant, and always risk-neutral. However, this funding curve is the output from a Treasury point of view. Treasury must consider Regulatory-required liquidity bu…

2013-10-12abs ↗pdf ↗