Paper: arXiv 2406.09765

Authors: Liyang Wang, Yu Cheng, Ao Xiang, Jingyu Zhang, Haowei Yang

Abstract

This paper explores the application of Natural Language Processing (NLP) in financial risk detection. By constructing an NLP-based financial risk detection model, this study aims to identify and predict potential risks in financial documents and communications. First, the fundamental concepts of NLP and its theoretical foundation, including text mining methods, NLP model design principles, and machine learning algorithms, are introduced. Second, the process of text data preprocessing and feature extraction is described. Finally, the effectiveness and predictive performance of the model are validated through empirical research. The results show that the NLP-based financial risk detection model performs excellently in risk identification and prediction, providing effective risk management tools for financial institutions. This study offers valuable references for the field of financial risk management, utilizing advanced NLP techniques to improve the accuracy and efficiency of financial risk detection.

Complexity vs Empirical Score

  • Math Complexity: 2.5/10
  • Empirical Rigor: 2.0/10
  • Quadrant: Philosophers — conceptual discussion, limited math and data

Why this score: The paper provides a very high-level summary of well-known NLP techniques (TF-IDF, BoW, Word2Vec) without offering novel methodological contributions. The provided text lacks concrete empirical results, datasets, backtests, or detailed mathematical derivations beyond standard introductory formulas.

Research Flowchart

  flowchart TD
    A[Research Goal: Detect Financial Risk via NLP] --> B[Inputs: Financial Docs & Communications]
    B --> C[Data Preprocessing & Feature Extraction]
    C --> D[NLP & Machine Learning Model Design]
    D --> E[Empirical Validation & Prediction]
    E --> F[Outcomes: High Accuracy Risk Detection Tool]

Cited References (16)

  1. Task allocation planning based on hierarchical task network for national economic mobilization (2024) — link
  2. Research on the Application of Computer Vision Based on Deep Learning in Autonomous Driving Technology (2024) — link
  3. AI-based NLP section discusses the application and effect of bag-of-words models and TF-IDF in NLP tasks (2024) — link
  4. Infrared Image Super-Resolution via Lightweight Information Split Network (2024) — link
  5. Research on Credit Risk Early Warning Model of Commercial Banks Based on Neural Network Algorithm — → 2405.10762
  6. Big Data and Machine Learning in Defence (2024) — link
  7. Adaptive speed planning for Unmanned Vehicle Based on Deep Reinforcement Learning (2024) — link
  8. Localization through particle filter powered neural network estimated monocular camera poses (2024) — link
and 8 more
  1. Deception Detection from Linguistic and Physiological Data Streams Using Bimodal Convolutional Neural Networks (2023) — link
  2. Industrial Sector (2020) — link
  3. Contextual Hourglass Network for Semantic Segmentation of High Resolution Aerial Imagery (2018) — link
  4. Instance Needs More Care: Rewriting Prompts for Instances Yields Better Zero-Shot Performance (2023) — link
  5. Automated pneumonia detection in chest x-ray images using deep learning model
  6. The cloud-based design of unmanned constant temperature food delivery trolley in the context of artificial intelligence
  7. via Lightweight
  8. The application of artificial intelligence technology in assembly techniques within the

1 of 16 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.