arXiv Machine Learning

Shapley in Context: Explaining Financial Language with Domain Expertise

arXiv:2607. 00856v1 Announce Type: cross Abstract: In recent years, large language models have achieved remarkable success and have seen growing adoption in financial applications.

arXiv AI

Jul 21

Detection, Attribution, Narration: An End-to-End Pipeline for Explainable Money Mule Identification

arXiv:2607. 17586v1 Announce Type: cross Abstract: Money mule accounts are critical facilitators of financial fraud, yet detecting them at scale remains challenging due to the heterogeneous nature of transactional and behavioural data.

By Yuge Zhang, Yuanxing Zhang, Yichao Jin, Khairul Amsyar Mohd Razis, Nicholas Qi An Choo, Kai Yin Anders Wong, Xinyan Tang, Kenneth Zhu Ke, Wee Keong Dennis Lee, Jingyuan Zhao

llms

arXiv Machine Learning

Aug 11

RA-FinBERT: Rule-aware LoRA adaptation for low-resource financial sentiment classification

arXiv:2608. 09834v1 Announce Type: cross Abstract: Financial sentiment analysis converts unstructured financial news into quantitative signals that can support market analysis and decision-making.

By Fan Zhang, Jiaming Li

llms nlp fine-tuning

arXiv AI

Aug 11

How Simple Can It Get? From Interpretable Equations to Readable Rules for Financial Decision Making

arXiv:2608. 09433v1 Announce Type: cross Abstract: In regulated domains such as finance, a model that cannot be explained cannot be deployed, yet many interpretable classifiers defeat their own purpose by producing formulas with dozens of features that no regulator could read.

By Adia Lumadjeng, Ilker Birbil, Erman Acar

efficiency

arXiv Machine Learning

Jul 16

Foundation Models for Credit Risk Prediction: A Game Changer?

arXiv:2605. 18147v2 Announce Type: replace Abstract: Predictive models play a pivotal role in credit risk management, guiding critical decisions through accurate estimation of default probabilities and losses.

By Bart Baesens, Andreas Goethals, Stefan Lessmann, Simon De Vos, Cristi\'an Bravo, David Martens, Victor Medina-Olivares, Christophe Mues, Maria Oskarsd\'ottir, Seppe vanden Broucke, Tony Van Gestel, Tim Verdonck, Wouter Verbeke

llms computer-vision nlp benchmarks

arXiv AI

Jun 30

When Summaries Distort Decisions: Information Fidelity in LLM-Compressed Financial Analysis

arXiv:2606. 29251v1 Announce Type: new Abstract: Financial decision-makers face more information than they can directly inspect, making context compression necessary.

By Hoyoung Lee, Suhwan Park, Seunghan Lee, Jun Seo, Jaehoon Lee, Sungdong Yoo, Minjae Kim, CheolWon Na, Zhangyang Wang, Zach Golkhou, Minkyu Kim, Sotirios Sabanis, Alejandro Lopez-Lira, Dhagash Mehta, Soonyoung Lee, Chanyeol Choi, Wonbin Ahn, Yongjae Lee

llms agents

arXiv AI

Aug 7

Counterfactual Analysis via Large Language Models

arXiv:2608. 05367v1 Announce Type: new Abstract: Counterfactual analysis aims to predict potential outcomes under hypothetical scenarios, offering valuable insights for decision-making.

By Zonghao Yang

llms

arXiv Machine Learning

Jul 17

How Much of a 10-K Matters? Aggregation-Dependent Value of Full-Text versus Risk-Factor Sentiment

arXiv:2607. 14174v1 Announce Type: new Abstract: Financial sentiment extraction has largely relied on news text and supervised extraction against return labels alone, leaving 10-K filings -- and volatility, the target risk disclosure is arguably best suited to informing -- comparatively unexplored.

By Sanggyu Sean Choi

arXiv AI

Jun 10

A Unified Multi-Modal Framework for Intelligent Financial Systems: Integrating Reinforcement Learning, High-Frequency Trading, and Game-Theoretic Approaches with Cross-Modal Sentiment Analysis

arXiv:2606. 10412v1 Announce Type: new Abstract: The rapid evolution of financial technology demands sophisticated artificial intelligence systems capable of handling diverse challenges across multiple domains simultaneously.

By Fanrong Liu, Zhang Yuwei, Mingni Luo

llms rag nlp reinforcement-learning multimodal benchmarks

arXiv AI

Jul 22

Benchmarking Generalization in Financial Statement Fraud Detection: robust evaluation and novel tasks

arXiv:2607. 19259v1 Announce Type: cross Abstract: Financial statement fraud detection (FSFD) is crucial for market integrity but faces challenges from increasingly sophisticated schemes and under-utilized textual data in financial reports.

By Guy Stephane Waffo Dzuyo (Forvis Mazars, LORIA CNRS Universit\'e de Lorraine), Ga\"el Guibon (LORIA CNRS Universit\'e de Lorraine, LIPN CNRS Universit\'e Sorbonne Paris Nord), Christophe Cerisara (LORIA CNRS Universit\'e de Lorraine), Luis Belmar-Letelier (Forvis Mazars)

llms benchmarks

arXiv AI

22h ago

Communicating Credit Risk with Large Language Models: Evaluation of Explanations from Standard and Alternative Data-Based Models

arXiv:2608. 17715v1 Announce Type: cross Abstract: Credit decisioning is a high-stakes task in which model outputs must be accurate and explainable to support compliant decisions.

By Sahab Zandi, Noah Kostesku, Christophe Mues, Mar\'ia \'Oskarsd\'ottir, Cristi\'an Bravo

llms fine-tuning safety

arXiv Machine Learning

Jul 31

FinSMART: Financial Sentiment Analysis for Algorithmic Trading through Market-Aligned Reinforcement Learning

arXiv:2607. 28127v1 Announce Type: cross Abstract: Recent advances in Generative AI have substantially improved financial sentiment analysis through post-trained financial large language models (LLMs).

By Giorgos Iacovides, Wuyang Zhou, Danilo Mandic

llms nlp reinforcement-learning benchmarks

arXiv AI

Jun 17

The Stanford EDGAR Filings Dataset: Reconstructing U.S. Corporate and Financial Disclosures into Layout-Faithful and Token-Efficient Pretraining Data

arXiv:2606. 18192v1 Announce Type: new Abstract: As high-quality public web corpora become increasingly exhausted, clean long-context documents have become a scarce and expensive source of training data for large language models (LLMs).

By Nick Bettencourt, Xiaowei Ding, Kay Giesecke

llms computer-vision benchmarks

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