Research and Design of a Financial Intelligent Risk Control Platform Based on Big Data Analysis and Deep Machine Learning
- URL: http://arxiv.org/abs/2409.10331v1
- Date: Mon, 16 Sep 2024 14:41:41 GMT
- Title: Research and Design of a Financial Intelligent Risk Control Platform Based on Big Data Analysis and Deep Machine Learning
- Authors: Shuochen Bi, Yufan Lian, Ziyue Wang,
- Abstract summary: This article explores how to fully utilize big data technology to achieve complete integration of internal and external data of financial institutions.
This article adopts big data mining and real-time streaming data processing technology to monitor, analyze, and alert various business data.
- Score: 2.766666938196471
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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
Related papers
- Analysis of Financial Risk Behavior Prediction Using Deep Learning and Big Data Algorithms [7.713045399751312]
This paper explores the feasibility and effectiveness of using deep learning and big data algorithms for financial risk behavior prediction.
A deep learning-based big data risk prediction framework is designed and experimentally validated on actual financial datasets.
arXiv Detail & Related papers (2024-10-25T08:52:04Z) - Design and Optimization of Big Data and Machine Learning-Based Risk Monitoring System in Financial Markets [9.599753686171217]
This paper designs and optimize a risk monitoring system based on big data and machine learning.
It effectively integrates large-scale financial data and advanced machine learning algorithms.
Research findings demonstrate that the system significantly enhances efficiency and accuracy in risk management.
arXiv Detail & Related papers (2024-07-28T00:04:34Z) - Enhancing Financial Inclusion and Regulatory Challenges: A Critical Analysis of Digital Banks and Alternative Lenders Through Digital Platforms, Machine Learning, and Large Language Models Integration [0.0]
This paper explores the dual impact of digital banks and alternative lenders on financial inclusion and the regulatory challenges posed by their business models.
It discusses the integration of digital platforms, machine learning (ML), and Large Language Models (LLMs) in enhancing financial services accessibility for underserved populations.
arXiv Detail & Related papers (2024-04-18T05:00:53Z) - Best Practices and Lessons Learned on Synthetic Data [83.63271573197026]
The success of AI models relies on the availability of large, diverse, and high-quality datasets.
Synthetic data has emerged as a promising solution by generating artificial data that mimics real-world patterns.
arXiv Detail & Related papers (2024-04-11T06:34:17Z) - AlphaFin: Benchmarking Financial Analysis with Retrieval-Augmented Stock-Chain Framework [48.3060010653088]
We release AlphaFin datasets, combining traditional research datasets, real-time financial data, and handwritten chain-of-thought (CoT) data.
We then use AlphaFin datasets to benchmark a state-of-the-art method, called Stock-Chain, for effectively tackling the financial analysis task.
arXiv Detail & Related papers (2024-03-19T09:45:33Z) - Dynamic Datasets and Market Environments for Financial Reinforcement
Learning [68.11692837240756]
FinRL-Meta is a library that processes dynamic datasets from real-world markets into gym-style market environments.
We provide examples and reproduce popular research papers as stepping stones for users to design new trading strategies.
We also deploy the library on cloud platforms so that users can visualize their own results and assess the relative performance.
arXiv Detail & Related papers (2023-04-25T22:17:31Z) - Auditing and Generating Synthetic Data with Controllable Trust Trade-offs [54.262044436203965]
We introduce a holistic auditing framework that comprehensively evaluates synthetic datasets and AI models.
It focuses on preventing bias and discrimination, ensures fidelity to the source data, assesses utility, robustness, and privacy preservation.
We demonstrate the framework's effectiveness by auditing various generative models across diverse use cases.
arXiv Detail & Related papers (2023-04-21T09:03:18Z) - Financial data analysis application via multi-strategy text processing [0.2741266294612776]
This paper mainly focuses on the stock trading data and news about China A-share companies.
We present our efforts and plans in deep learning financial text processing application scenarios using natural language processing (NLP) and knowledge graph (KG) technologies.
arXiv Detail & Related papers (2022-04-25T01:56:36Z) - A big data intelligence marketplace and secure analytics experimentation
platform for the aviation industry [0.0]
This paper introduces the ICARUS big data-enabled platform that offers a novel aviation data and intelligence marketplace.
It holistically handles the complete big data lifecycle from the data collection, data curation and data exploration to the data integration and data analysis.
arXiv Detail & Related papers (2021-11-18T18:51:40Z) - FinQA: A Dataset of Numerical Reasoning over Financial Data [52.7249610894623]
We focus on answering deep questions over financial data, aiming to automate the analysis of a large corpus of financial documents.
We propose a new large-scale dataset, FinQA, with Question-Answering pairs over Financial reports, written by financial experts.
The results demonstrate that popular, large, pre-trained models fall far short of expert humans in acquiring finance knowledge.
arXiv Detail & Related papers (2021-09-01T00:08:14Z) - Reinforcement-Learning based Portfolio Management with Augmented Asset
Movement Prediction States [71.54651874063865]
Portfolio management (PM) aims to achieve investment goals such as maximal profits or minimal risks.
In this paper, we propose SARL, a novel State-Augmented RL framework for PM.
Our framework aims to address two unique challenges in financial PM: (1) data Heterogeneous data -- the collected information for each asset is usually diverse, noisy and imbalanced (e.g., news articles); and (2) environment uncertainty -- the financial market is versatile and non-stationary.
arXiv Detail & Related papers (2020-02-09T08:10:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.