Paper: arXiv 2409.10331

Authors: Shuochen Bi, Yufan Lian, Ziyue Wang

Abstract

In the financial field of the United States, the application of big data technology has become one of the important means for financial institutions to enhance competitiveness and reduce risks. The core objective of this article is to explore how to fully utilize big data technology to achieve complete integration of internal and external data of financial institutions, and create an efficient and reliable platform for big data collection, storage, and analysis. With the continuous expansion and innovation of financial business, traditional risk management models are no longer able to meet the increasingly complex market demands. This article adopts big data mining and real-time streaming data processing technology to monitor, analyze, and alert various business data. Through statistical analysis of historical data and precise mining of customer transaction behavior and relationships, potential risks can be more accurately identified and timely responses can be made. This article designs and implements a financial big data intelligent risk control platform. This platform not only achieves effective integration, storage, and analysis of internal and external data of financial institutions, but also intelligently displays customer characteristics and their related relationships, as well as intelligent supervision of various risk information

Complexity vs Empirical Score

  • Math Complexity: 1.0/10
  • Empirical Rigor: 2.0/10
  • Quadrant: Philosophers — conceptual discussion, limited math and data

Why this score: The paper provides a very high-level, generic overview of an architectural concept for a financial risk control platform without presenting explicit mathematical formulas or rigorous deep learning models. The empirical section consists only of a basic system load test rather than backtesting financial risk models or validating machine learning performance.

Research Flowchart

  flowchart TD
    A[Goal: Build Financial Intelligent Risk Control Platform] --> B[Input: Internal & External Financial Data]
    B --> C[Real-Time Streaming Processing]
    B --> D[Big Data Mining & Behavior Analytics]
    C --> E[Deep Machine Learning & Statistical Analysis]
    D --> E
    E --> F[Outcome: Accurate Risk Detection & Real-Time Alerts]
    E --> G[Outcome: Visualization of Customer Profiles & Networks]

Cited References (1)

  1. Heterogeneous Information Network based Default Analysis on Banking Micro and Small Enterprise Users (2022) — link

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