Deep Learning and Financial Stability

ArXiv ID: ssrn-3723132 “View on arXiv”

Authors: Unknown

Abstract

The financial sector is entering a new era of rapidly advancing data analytics as deep learning models are adopted into its technology stack. A subset of Artifi

Keywords: Deep Learning, Data Analytics, Fintech, Natural Language Processing (NLP), Financial Modeling, Multi-Asset

Complexity vs Empirical Score

• Math Complexity: 2.5/10
• Empirical Rigor: 1.0/10
• Quadrant: Philosophers
• Why: The paper is a conceptual policy analysis that identifies theoretical transmission pathways (e.g., data aggregation, model design) for systemic risk without presenting mathematical models, statistical metrics, or backtesting results. It focuses on qualitative governance frameworks rather than quantitative implementation.

  flowchart TD
    A["Research Goal: Deep Learning in Financial Stability"] --> B["Data Inputs & Methodology"]
    B --> C["Computational Processes"]
    C --> D["Key Findings & Outcomes"]
    
    B --> B1["Multi-Asset Data"]
    B --> B2["NLP on Financial Text"]
    B --> B3["Alternative Data Sources"]
    
    C --> C1["Deep Learning Models"]
    C --> C2["Financial Stability Metrics"]
    C --> C3["Risk Assessment Algorithms"]
    
    D --> D1["Enhanced Risk Prediction"]
    D --> D2["Systemic Stability Insights"]
    D --> D3["Fintech Innovation Pathways"]
    
    style A fill:#e1f5fe
    style D fill:#e8f5e8