Revolutionizing Finance: Unleashing Machine Learning's Potential for a New Financial Era

Machine learning has developed into a crucial tool for the financial industry, allowing financial organizations to enhance their processes and offerings. This paper gives a general overview of machine learning's uses and methods in the financial industry. Additionally, the paper includes case studies of effective machine learning applications in the financial industry, emphasizing the advantages of machine learning for fraud detection, risk management, trading and investment, customer service and personalization, credit scoring, and loan approval. The paper stresses how crucial it is to deal with issues like data quality and quantity, explainability and interpretability, regulation and ethical considerations, integration with legacy systems, and a talent and skills deficit. The future scope of machine learning in financial sector is discussed which highlights that how machine learning is going to change future financial landscape for good and will become an integral part of financial sector.

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Publication Details

Published
2024-09-27
DOI
https://doi.org/10.1109/acroset62108.2024.10743732
Citations
3
Primary Topic
FinTech, Crowdfunding, Digital Finance
Type
article
Field-Weighted Citation Impact
1.59
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article

Revolutionizing Finance: Unleashing Machine Learning's Potential for a New Financial Era

Manjit Kour
3 citations
FinTech, Crowdfunding, Digital Finance
1.59
article

Revolutionizing Finance: Unleashing Machine Learning's Potential for a New Financial Era

Manjit Kour
article en
3 citations

Abstract

Machine learning has developed into a crucial tool for the financial industry, allowing financial organizations to enhance their processes and offerings. This paper gives a general overview of machine learning's uses and methods in the financial industry. Additionally, the paper includes case studies of effective machine learning applications in the financial industry, emphasizing the advantages of machine learning for fraud detection, risk management, trading and investment, customer service and personalization, credit scoring, and loan approval. The paper stresses how crucial it is to deal with issues like data quality and quantity, explainability and interpretability, regulation and ethical considerations, integration with legacy systems, and a talent and skills deficit. The future scope of machine learning in financial sector is discussed which highlights that how machine learning is going to change future financial landscape for good and will become an integral part of financial sector.

Chandigarh University (IN)
Decent work and economic growth
Openalex Percentile: Top 12%
FinTech, Crowdfunding, Digital Finance
1.59
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