Finance 4.0: Predictive Analytics for Financial Risk Management Using AI

The data economy was born when the economy changed perspectives from production scale to data scale. Traditional analytical frameworks can no longer absorb rapid data growth and changes. Therefore, attractive frontier models, algorithms and frameworks emerged, such as finance 4.0. The leading responsibilities of finance 4.0 are worry-free cash flow, o-2-s (one to seconds) financing and capital return, which need to analyse business risks effectively, supporting enterprises to the public. In order to promote market economy, legally comply with internal control of listed companies, effectively mitigate domestic problems of inappropriate transactions and promote sustainable development of all industries, a new paradigmatic model, predictive analytics, was developed based on finance 4.0. Quantitative analysis methods are developed using time series data to predict future training targets, containing classical models, machine learning models and deep learning models. Yet their limitations are also obvious. Reporting speed is qualitative and excessive report length. Therefore, writing photography, a new measurement approach, is applied to address these deficiencies. In modern society, as a fundamental industry of the national economy, finance is the most active, massive and complex information management system. It experiences the evolution from guided type, to planning type, controlling type. Financial policies and processes are becoming more complex, strictly and cautiously. Focusing on asset allocation, cash flow financing, traded commodity efficiency, insurance claims and patterns monitoring are the most important tasks. As the explosion of both innocuous and harmful financial data, especially rapid, irregular and implicit unstructured data, traditional analytical frameworks can no longer absorb data growth and changes. Therefore, frontier analytical models, algorithms and frameworks emerge to handle them, such as artificial intelligence, big data and finance 4.0. As the fundamental industry of the national economy, finance is the most active, massive and complex information management system. Mutual dependencies of finance, economic entities, governments, and public affairs build complex networks. Therefore, financial risks come from systemic fragility, and early warning, traceability analysis and intervention controlling are the important tasks. In addition to sustaining economic security, it can analyse bubbles, crises, cascades, contagions. Abundances of structured and unstructured data need to store, access, manage, clean, integrate, analyse, visualize and report. As an emerging market of data economy, China experiences the rapid growth of all exchanged information and media.

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Publication Details

Journal
European Journal of Analytics and Artificial Intelligence
Published
2025-01-01
DOI
https://doi.org/10.63665/ejaai.v01i01.01
Citations
1
Primary Topic
Impact of AI and Big Data on Business and Society
Type
article
Field-Weighted Citation Impact
2.11
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article

Finance 4.0: Predictive Analytics for Financial Risk Management Using AI

Jeevani Singireddy
1 citations
European Journal of Analytics and Artificial Intelligence
Impact of AI and Big Data on Business and Society
2.11
article

Finance 4.0: Predictive Analytics for Financial Risk Management Using AI

Jeevani Singireddy
article en
1 citations

Abstract

The data economy was born when the economy changed perspectives from production scale to data scale. Traditional analytical frameworks can no longer absorb rapid data growth and changes. Therefore, attractive frontier models, algorithms and frameworks emerged, such as finance 4.0. The leading responsibilities of finance 4.0 are worry-free cash flow, o-2-s (one to seconds) financing and capital return, which need to analyse business risks effectively, supporting enterprises to the public. In order to promote market economy, legally comply with internal control of listed companies, effectively mitigate domestic problems of inappropriate transactions and promote sustainable development of all industries, a new paradigmatic model, predictive analytics, was developed based on finance 4.0. Quantitative analysis methods are developed using time series data to predict future training targets, containing classical models, machine learning models and deep learning models. Yet their limitations are also obvious. Reporting speed is qualitative and excessive report length. Therefore, writing photography, a new measurement approach, is applied to address these deficiencies. In modern society, as a fundamental industry of the national economy, finance is the most active, massive and complex information management system. It experiences the evolution from guided type, to planning type, controlling type. Financial policies and processes are becoming more complex, strictly and cautiously. Focusing on asset allocation, cash flow financing, traded commodity efficiency, insurance claims and patterns monitoring are the most important tasks. As the explosion of both innocuous and harmful financial data, especially rapid, irregular and implicit unstructured data, traditional analytical frameworks can no longer absorb data growth and changes. Therefore, frontier analytical models, algorithms and frameworks emerge to handle them, such as artificial intelligence, big data and finance 4.0. As the fundamental industry of the national economy, finance is the most active, massive and complex information management system. Mutual dependencies of finance, economic entities, governments, and public affairs build complex networks. Therefore, financial risks come from systemic fragility, and early warning, traceability analysis and intervention controlling are the important tasks. In addition to sustaining economic security, it can analyse bubbles, crises, cascades, contagions. Abundances of structured and unstructured data need to store, access, manage, clean, integrate, analyse, visualize and report. As an emerging market of data economy, China experiences the rapid growth of all exchanged information and media.

European Journal of Analytics and Artificial IntelligenceVol. 01(01)
Engineering Software Research and Development (United States) (US)
Openalex Percentile: Top 14%
Impact of AI and Big Data on Business and Society
2.11
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