Big Data Analytics In Fintech: A Review Of Credit Risk Assessment And Fraud Detection

Main Article Content

Dr. M. Parthiban
Mr. P. Krishnamoorthy
Dr Namrata Kapoor Kohli
Dr. Sunil Adhav
Dr. Khaja Mohinuddeen J


In the rapidly evolving landscape of financial technology (Fintech), the advent of big data analytics has revolutionized credit risk assessment and fraud detection processes. This review research paper provides a comprehensive examination of the application of big data analytics in Fintech, focusing specifically on its role in credit risk assessment and fraud detection. By synthesizing a diverse array of academic literature, industry reports, and empirical studies, this paper offers insights into the latest developments, challenges, and future directions in this dynamic field.

The review begins by elucidating the fundamental principles of big data analytics and its relevance to Fintech. It explores the key characteristics of big data, including volume, velocity, variety, and veracity, and discusses how these characteristics are leveraged to extract actionable insights for credit risk assessment and fraud detection. The paper critically evaluates the methodologies and techniques employed in big data analytics, such as machine learning algorithms, natural language processing, and network analysis, highlighting their strengths and limitations in the context of Fintech applications.

Subsequently, the review delves into the specific applications of big data analytics in credit risk assessment and fraud detection. It examines how predictive analytics models are used to assess creditworthiness, identify default risks, and personalize lending decisions. Additionally, the paper investigates the role of anomaly detection algorithms and behavioral analytics in detecting fraudulent activities and mitigating financial risks.

Furthermore, the review discusses the challenges and ethical considerations associated with the use of big data analytics in Fintech. Issues such as data privacy, algorithmic bias, and regulatory compliance are explored, emphasizing the need for responsible and transparent use of data-driven technologies in financial services.

This review research paper underscores the transformative potential of big data analytics in Fintech, particularly in the domains of credit risk assessment and fraud detection. By harnessing the power of big data, Fintech companies can make more informed lending decisions, enhance fraud detection capabilities, and ultimately foster financial inclusion. However, it also highlights the importance of addressing ethical concerns and regulatory challenges to ensure the responsible and equitable use of big data analytics in the financial industry.


Download data is not yet available.

Article Details

How to Cite
Dr. M. Parthiban, Mr. P. Krishnamoorthy, Dr Namrata Kapoor Kohli, Dr. Sunil Adhav, & Dr. Khaja Mohinuddeen J. (2024). Big Data Analytics In Fintech: A Review Of Credit Risk Assessment And Fraud Detection. Educational Administration: Theory and Practice, 30(5), 3676–3684.
Author Biographies

Mr. P. Krishnamoorthy

Associate Professor, Department of Computer Science and Engineering, Sasi Institute of Technology and Engineering, Tadepalligudem, West Godavari Dt, Andhra Pradesh, Pin: 534 101.

Dr Namrata Kapoor Kohli

Associate Professor, Department of SBFSI, Symbiosis University of Applied Science Indore, Pin:452001 

Dr. Sunil Adhav

AssociateProfessor, Department of Business, School of Business, Dr. Vishwanath Karad MIT World Peace University.  Pune- 411038. Maharashtra, Pin: 411038.

Dr. Khaja Mohinuddeen J

Associate Professor, Department of Management Studies, Ballari Institute of Technology and Management, Ballari, Karnataka, Pin: 583104

Most read articles by the same author(s)