The Application of Business Analytics in the Era of Big Data
- 10.2991/assehr.k.211209.274How to use a DOI?
- Business Analytics; Big Data; Banking Industry
With the advent of the big data era, data-based business analytics is more and more widely used in all industries, in which banking with the loan business is one of the most important businesses. In order to conduct more intelligent risk control, banks often build prediction models based on loan records to assess whether future clients will default on loans, and the factors generally considered include income level, loan amount, interest rate, etc. This ultimately helps banks to optimize the loan business, avoid credit risks, and reduce losses. This paper focuses on the solutions of choosing appropriate prediction models, classifying the clients, and predicting their defaults. It clarifies the four-step framework of business analytics in the first part. Then this paper introduces three typical statistical analysis models, including the logistic regression model, the decision tree and random forest model, and the K-mean cluster model. The bank loan risk control dataset includes 20,000 borrowers and the details of personal information and loan information. Based on the dataset, the best prediction results are obtained using a random forest model that area under the curve is 0.741 and the clients are divided into four clusters. The logistic regression indicates a negative coefficient between the annual income and the default, while the coefficients are positive between other factors, like the loan amount and the interest rate and the default. In practical application, these models can be combined to give full play to their advantages and make a better prediction.
- © 2021 The Authors. Published by Atlantis Press International B.V.
- Open Access
- This is an open access article under the CC BY-NC license.
Cite this article
TY - CONF AU - Tianhuiqi Chen AU - Bowen Gu AU - Zhenxin Jin PY - 2021 DA - 2021/12/15 TI - The Application of Business Analytics in the Era of Big Data BT - Proceedings of the 2021 3rd International Conference on Economic Management and Cultural Industry (ICEMCI 2021) PB - Atlantis Press SP - 1704 EP - 1711 SN - 2352-5428 UR - https://doi.org/10.2991/assehr.k.211209.274 DO - 10.2991/assehr.k.211209.274 ID - Chen2021 ER -