Integrating Machine Learning with Financial Data Analytics
HARSHITA CHERUKURI
, DR. AJAY KUMAR CHAURASIA, , DR. TIKAM SINGH,
Machine Learning (ML) • Financial Data Analytics • Decision-Making • Risk Management • Forecasting • Investment Strategies • Supervised Learning • Regression • Classification • Unsupervised Learning • Clustering • Anomaly Detection • Credit Scoring • Fraud Detection • Algorithmic Trading
In the evolving landscape of financial services, integrating machine learning (ML) with financial data analytics offers transformative potential for enhancing decision-making processes and operational efficiency. This paper explores the synergy between machine learning algorithms and financial data analytics, emphasizing how this integration can drive innovation and strategic advantage in the industry. As financial institutions increasingly rely on vast amounts of data, traditional analytical methods often fall short in handling the complexity and volume of modern datasets. Machine learning provides advanced techniques for extracting actionable insights from this data, leading to more accurate forecasting, risk management, and investment strategies.
The study delves into various machine learning models, including supervised learning algorithms such as regression and classification, and unsupervised techniques like clustering and anomaly detection. These models are applied to financial data for tasks such as credit scoring, fraud detection, algorithmic trading, and customer segmentation. By leveraging ML techniques, financial organizations can uncover hidden patterns and trends that traditional methods might overlook, resulting in more precise risk assessments and more effective strategic planning.
The paper also addresses the challenges associated with integrating machine learning into financial data analytics, including issues related to data quality, model interpretability, and regulatory compliance. It highlights the importance of robust data preprocessing and feature engineering to ensure the reliability and validity of machine learning models. Additionally, the paper discusses the need for transparency in model decision-making processes to build trust among stakeholders and meet regulatory requirements.
Through case studies and practical examples, the research demonstrates how machine learning applications have successfully transformed financial data analytics in real-world scenarios. The findings underscore the potential for machine learning to not only enhance analytical capabilities but also to drive innovation in financial services by enabling more dynamic and responsive approaches to market changes and customer needs.
"Integrating Machine Learning with Financial Data Analytics", JETNR - JOURNAL OF EMERGING TRENDS AND NOVEL RESEARCH (www.JETNR.org), ISSN:2984-9276, Vol.1, Issue 6, page no.a1-a11, June-2024, Available :https://rjpn.org/JETNR/papers/JETNR2306001.pdf
Volume 1
Issue 6,
June-2024
Pages : a1-a11
Paper Reg. ID: JETNR_230840
Published Paper Id: JETNR2306001
Downloads: 000196
Research Area: Science and Technology
Country: ghaziabad, up, India
ISSN: 2984-9276 | IMPACT FACTOR: 8.27 Calculated By Google Scholar | ESTD YEAR: 2023
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.27 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: RJPN (IJPublication) Janvi Wave
