Research and Design of a Financial Intelligent Risk Control Platform Based on Big Data Analysis and Deep Machine Learning

TL;DR

This paper presents a comprehensive design of a financial risk control platform leveraging big data and deep machine learning to enhance risk detection, monitoring, and management in financial institutions.

Contribution

It introduces a novel integrated platform that combines big data analysis, real-time processing, and machine learning for improved financial risk management.

Findings

Effective integration of internal and external financial data.

Real-time risk monitoring and alert system.

Enhanced accuracy in customer risk profiling.

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