Research for MSMEs Credit Strategy Based on RFM Model

DOI

10.2991/978-94-6463-222-4_48How to use a DOI?

Keywords

credit strategy; quantitative analysis; RFM model; K-means algorithm

Abstract

The small size of MSMEs and the lack of collateralizable assets make it difficult for banks to reliably classify the credit ratings of MSMEs. This paper constructs an RFM model to accurately classify the credit rating of MSMEs, extracts the behavioural characteristics of enterprises, and uses the K-means clustering algorithm to subdivide the credit rating of enterprises, ultimately realising the classification of MSMEs with different credit ratings. From the perspective of bank profit and risk control, differentiated credit strategies are formulated for different credit grades of MSMEs to maximise the overall bank profit.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Guan Li
AU  - Jianming Chen
AU  - Xinxin Huang
PY  - 2023
DA  - 2023/08/28
TI  - Research for MSMEs Credit Strategy Based on RFM Model
BT  - Proceedings of the 2023 2nd International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2023)
PB  - Atlantis Press
SP  - 445
EP  - 458
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-222-4_48
DO  - 10.2991/978-94-6463-222-4_48
ID  - Li2023
ER  -