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Open Access Journal Article

Predictive Analytics for Credit Scoring

by Sophia Smith 1,*
1
Sophia Smith
*
Author to whom correspondence should be addressed.
MAD  2021 3(1):20; https://doi.org/10.69610/j.mad.20210622
Received: 29 April 2021 / Accepted: 27 May 2020 / Published Online: 22 June 2021
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Abstract

The rapid development of technology has revolutionized the financial industry, leading to an increased reliance on predictive analytics in credit scoring models. This paper explores the application of predictive analytics in credit scoring, highlighting its significance in enhancing risk assessment and decision-making processes. The study delves into the various techniques utilized in predictive analytics, such as regression analysis, machine learning algorithms, and data mining. By analyzing historical data and identifying patterns, predictive analytics enables lenders to make more accurate and informed decisions regarding creditworthiness. The paper further discusses the challenges and limitations associated with predictive analytics in credit scoring, emphasizing the importance of data quality, privacy concerns, and ethical considerations. Additionally, it examines the potential impact of predictive analytics on the financial sector and its role in promoting financial inclusion. The study concludes that predictive analytics has the potential to transform the credit scoring landscape, offering a more efficient and reliable system for lenders and borrowers alike.

Keywords: Predictive Analytics; Credit Scoring; Risk Assessment; Machine Learning; Data Mining.;
Copyright: © 2021 by Smith. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Cite This Paper
APA Style
Smith, S. (2021). Predictive Analytics for Credit Scoring. Management Analytics and Decision, 3(1), 20. doi:10.69610/j.mad.20210622
ACS Style
Smith, S. Predictive Analytics for Credit Scoring. Management Analytics and Decision, 2021, 3, 20. doi:10.69610/j.mad.20210622
AMA Style
Smith S. Predictive Analytics for Credit Scoring. Management Analytics and Decision; 2021, 3(1):20. doi:10.69610/j.mad.20210622
Chicago/Turabian Style
Smith, Sophia 2021. "Predictive Analytics for Credit Scoring" Management Analytics and Decision 3, no.1:20. doi:10.69610/j.mad.20210622

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ACS Style
Smith, S. Predictive Analytics for Credit Scoring. Management Analytics and Decision, 2021, 3, 20. doi:10.69610/j.mad.20210622
AMA Style
Smith S. Predictive Analytics for Credit Scoring. Management Analytics and Decision; 2021, 3(1):20. doi:10.69610/j.mad.20210622
Chicago/Turabian Style
Smith, Sophia 2021. "Predictive Analytics for Credit Scoring" Management Analytics and Decision 3, no.1:20. doi:10.69610/j.mad.20210622
APA style
Smith, S. (2021). Predictive Analytics for Credit Scoring. Management Analytics and Decision, 3(1), 20. doi:10.69610/j.mad.20210622

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References

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