Explainable Risk Classification in Financial Reports

• URL: http://arxiv.org/abs/2405.01881v3
• Date: Wed, 18 Dec 2024 05:28:53 GMT
• Title: Explainable Risk Classification in Financial Reports
• Authors: Xue Wen Tan, Stanley Kok,
• Abstract summary: Every publicly traded company in the US is required to file an annual 10-K financial report, which contains a wealth of information about the company.<n>We propose an explainable deep-learning model, called FinBERT-XRC, that takes a 10-K report as input, and automatically assesses the post-event return volatility risk of its associated company.
• Score: 1.2200609701777907
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Every publicly traded company in the US is required to file an annual 10-K financial report, which contains a wealth of information about the company. In this paper, we propose an explainable deep-learning model, called FinBERT-XRC, that takes a 10-K report as input, and automatically assesses the post-event return volatility risk of its associated company. In contrast to previous systems, our proposed model simultaneously offers explanations of its classification decision at three different levels: the word, sentence, and corpus levels. By doing so, our model provides a comprehensive interpretation of its prediction to end users. This is particularly important in financial domains, where the transparency and accountability of algorithmic predictions play a vital role in their application to decision-making processes. Aside from its novel interpretability, our model surpasses the state of the art in predictive accuracy in experiments on a large real-world dataset of 10-K reports spanning six years.

Related papers

• Multi-Channel Graph Neural Network for Financial Risk Prediction of NEEQ Enterprises [0.0]
  We propose a multi-channel deep learning framework that integrates structured financial indicators, textual disclosures, and enterprise relationship data for comprehensive financial risk prediction.<n>We show that our model significantly outperforms traditional machine learning methods and single-modality baselines in terms of AUC, Precision, Recall, and F1 Score.<n>This work provides theoretical and practical insights into risk modeling for SMEs and offers a data-driven tool to support financial regulators and investors.
  arXiv  Detail & Related papers  (2025-07-17T04:57:51Z)
• Explainable AI for Comprehensive Risk Assessment for Financial Reports: A Lightweight Hierarchical Transformer Network Approach [1.2200609701777907]
  Every publicly traded U.S. company files an annual 10-K report containing critical insights into financial health and risk.<n>We propose Tiny eXplainable Risk Assessor (TinyXRA), a transformer-based model that automatically assesses company risk from these reports.<n>TinyXRA incorporates skewness, kurtosis, and the Sortino ratio for more comprehensive risk assessment.
  arXiv  Detail & Related papers  (2025-06-30T12:13:35Z)
• FinAI-BERT: A Transformer-Based Model for Sentence-Level Detection of AI Disclosures in Financial Reports [6.324803752309524]
  This study introduces FinAI-BERT, a domain-adapted transformer-based language model designed to classify AI-related content at the sentence level within financial texts.<n>The model was fine-tuned on a manually curated and balanced dataset of 1,586 sentences drawn from 669 annual reports of U.S. banks.
  arXiv  Detail & Related papers  (2025-06-29T09:33:29Z)
• FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation [63.55583665003167]
  We present FinDER, an expert-generated dataset tailored for Retrieval-Augmented Generation (RAG) in finance. FinDER focuses on annotating search-relevant evidence by domain experts, offering 5,703 query-evidence-answer triplets. By challenging models to retrieve relevant information from large corpora, FinDER offers a more realistic benchmark for evaluating RAG systems.
  arXiv  Detail & Related papers  (2025-04-22T11:30:13Z)
• FinTSB: A Comprehensive and Practical Benchmark for Financial Time Series Forecasting [58.70072722290475]
  Financial time series (FinTS) record the behavior of human-brain-augmented decision-making. FinTSB is a comprehensive and practical benchmark for financial time series forecasting.
  arXiv  Detail & Related papers  (2025-02-26T05:19:16Z)
• A Scalable Data-Driven Framework for Systematic Analysis of SEC 10-K Filings Using Large Language Models [0.0]
  We propose a novel data-driven approach to analyze and rate the performance of companies based on their SEC 10-K filings. The proposed scheme is then implemented on an interactive GUI as a no-code solution for running the data pipeline and creating the visualizations. The application showcases the rating results and provides year-on-year comparisons of company performance.
  arXiv  Detail & Related papers  (2024-09-26T06:57:22Z)
• Editable Fairness: Fine-Grained Bias Mitigation in Language Models [52.66450426729818]
  We propose a novel debiasing approach, Fairness Stamp (FAST), which enables fine-grained calibration of individual social biases. FAST surpasses state-of-the-art baselines with superior debiasing performance. This highlights the potential of fine-grained debiasing strategies to achieve fairness in large language models.
  arXiv  Detail & Related papers  (2024-08-07T17:14:58Z)
• FinReport: Explainable Stock Earnings Forecasting via News Factor Analyzing Model [14.217469307568466]
  We aim to build an automatic system, FinReport, for ordinary investors to collect information, analyze it, and generate reports after summarizing. Specifically, our FinReport is based on financial news announcements and a multi-factor model to ensure the professionalism of the report. The FinReport consists of three modules: news factorization module, return forecasting module, risk assessment module.
  arXiv  Detail & Related papers  (2024-03-05T04:33:36Z)
• On the Societal Impact of Open Foundation Models [93.67389739906561]
  We focus on open foundation models, defined here as those with broadly available model weights. We identify five distinctive properties of open foundation models that lead to both their benefits and risks.
  arXiv  Detail & Related papers  (2024-02-27T16:49:53Z)
• Numerical Claim Detection in Finance: A New Financial Dataset, Weak-Supervision Model, and Market Analysis [4.575870619860645]
  We construct a new financial dataset for the claim detection task in the financial domain. We propose a novel weak-supervision model that incorporates the knowledge of subject matter experts (SMEs) in the aggregation function. Here, we observe the dependence of earnings surprise and return on our optimism measure.
  arXiv  Detail & Related papers  (2024-02-18T22:55:26Z)
• Preserving Knowledge Invariance: Rethinking Robustness Evaluation of Open Information Extraction [50.62245481416744]
  We present the first benchmark that simulates the evaluation of open information extraction models in the real world. We design and annotate a large-scale testbed in which each example is a knowledge-invariant clique. By further elaborating the robustness metric, a model is judged to be robust if its performance is consistently accurate on the overall cliques.
  arXiv  Detail & Related papers  (2023-05-23T12:05:09Z)
• Causal Fairness Analysis [68.12191782657437]
  We introduce a framework for understanding, modeling, and possibly solving issues of fairness in decision-making settings. The main insight of our approach will be to link the quantification of the disparities present on the observed data with the underlying, and often unobserved, collection of causal mechanisms. Our effort culminates in the Fairness Map, which is the first systematic attempt to organize and explain the relationship between different criteria found in the literature.
  arXiv  Detail & Related papers  (2022-07-23T01:06:34Z)
• A transformer-based model for default prediction in mid-cap corporate markets [13.535770763481905]
  We study mid-cap companies with less than US $10 billion in market capitalisation. We look to predict the default probability term structure over the medium term. We understand which data sources contribute most to the default risk.
  arXiv  Detail & Related papers  (2021-11-18T19:01:00Z)
• FinQA: A Dataset of Numerical Reasoning over Financial Data [52.7249610894623]
  We focus on answering deep questions over financial data, aiming to automate the analysis of a large corpus of financial documents. We propose a new large-scale dataset, FinQA, with Question-Answering pairs over Financial reports, written by financial experts. The results demonstrate that popular, large, pre-trained models fall far short of expert humans in acquiring finance knowledge.
  arXiv  Detail & Related papers  (2021-09-01T00:08:14Z)
• Supporting Financial Inclusion with Graph Machine Learning and Super-App Alternative Data [63.942632088208505]
  Super-Apps have changed the way we think about the interactions between users and commerce. This paper investigates how different interactions between users within a Super-App provide a new source of information to predict borrower behavior.
  arXiv  Detail & Related papers  (2021-02-19T15:13:06Z)
• Financial Data Analysis Using Expert Bayesian Framework For Bankruptcy Prediction [0.0]
  We propose another route of generative modeling using Expert Bayesian framework. The biggest advantage of the proposed framework is an explicit inclusion of expert judgment in the modeling process. The proposed approach is well suited for highly regulated or safety critical applications such as in finance or in medical diagnosis.
  arXiv  Detail & Related papers  (2020-10-19T19:09:02Z)

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.