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Credit Loan Modeling: How Different Factors Shape a Client's Behaviors

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Fangge XiongAuthors Info & Claims

ICITEE '20: Proceedings of the 3rd International Conference on Information Technologies and Electrical Engineering

Pages 434 - 438

https://doi.org/10.1145/3452940.3453023

Published: 17 May 2021 Publication History

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Abstract

An increasing number of credit loans have been developed in recent years as a tool to solve personal and business economic problems. Credit scoring models play a big part in evaluating one's credit score by analyzing one's information and making decisions about customer loan applications. Thus, understanding consumer loan behavior can prevent a loss of the bank. This study shows an analysis of certain factors related to the election of credit risks and potential consumers. The final results are established by identifying relationships, trends, or anomalies from datasets from Kaggle and using a machine learning approach. From this study, people could tell the general trend of loan applications and determine the probability of repaying loans.

References

[1]

Brown, M., & Zehnder, C. (2007). Credit reporting, relationship banking, and loan repayment. Journal of Money, Credit and Banking, 39(8), 1883--1918. https://doi.org/10.1111/j.1538-4616.2007.00092.x

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Personal loan statistics for 2020 | the ascent. (2020, August 3). The Motley Fool. https://www.fool.com/the-ascent/research/personal-loan-statistics/

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Index Terms

Credit Loan Modeling: How Different Factors Shape a Client's Behaviors
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Lexical semantics

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ICITEE '20: Proceedings of the 3rd International Conference on Information Technologies and Electrical Engineering

December 2020

687 pages

ISBN:9781450388665

DOI:10.1145/3452940

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 May 2021

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ICITEE2020

ICITEE2020: The 3rd International Conference on Information Technologies and Electrical Engineering

December 3 - 5, 2020

Hunan, Changde City, China

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References

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Recommendations

The Implications of Credit Risk Modeling for Banks’ Loan Loss Provisions and Loan-Origination Procyclicality

An Integrated Model for Hybrid Securities

Collateral Valuation and Borrower Financial Constraints: Evidence from the Residential Real Estate Market

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