Natural Language Processing for Financial Regulation

ArXiv ID: 2311.08533 “View on arXiv”

Authors: Unknown

Abstract

This article provides an understanding of Natural Language Processing techniques in the framework of financial regulation, more specifically in order to perform semantic matching search between rules and policy when no dataset is available for supervised learning. We outline how to outperform simple pre-trained sentences-transformer models using freely available resources and explain the mathematical concepts behind the key building blocks of Natural Language Processing.

Keywords: Natural Language Processing (NLP), Semantic Matching, Financial Regulation, Pre-trained Transformers, Unsupervised Learning, Financial Compliance (Services)

Complexity vs Empirical Score

• Math Complexity: 6.0/10
• Empirical Rigor: 3.0/10
• Quadrant: Lab Rats
• Why: The paper explains advanced NLP concepts and mathematical formulations like co-occurrence matrices, SVD, and Skip-gram training objectives, but provides no implementation details, backtesting results, or code, focusing instead on theoretical understanding for financial regulation.

  flowchart TD
    Start["Research Goal: Semantic Matching<br>for Financial Regulation<br>(No Labeled Dataset)"]
    
    Data -->|Free Text Resources| Embedding
    subgraph Data["Data / Inputs"]
        Rules["Regulatory Rules"]
        Policies["Policy Documents"]
    end

    subgraph Methodology["Methodology"]
        Embedding["Fine-tune Sentence Transformers<br>(Unsupervised Learning)"]
        Match["Semantic Matching<br>Vector Similarity Search"]
        Eval["Evaluate against<br>Baseline Models"]
    end

    subgraph Outcomes["Key Findings / Outcomes"]
        Result["Outperforms<br>Pre-trained Baselines"]
        Insight["Mathematical Understanding<br>of NLP Building Blocks"]
    end

    Start --> Data
    Embedding --> Match
    Match --> Eval
    Eval --> Result
    Eval --> Insight