Title: The application of logistic regression and K-means clustering for the grading assessment of police-involved credit risk

Authors: Xinmeng Wang; Mingyue Qiu

Addresses: School of Information Technology, Nanjing Forest Police College, Nanjing, China ' School of Information Technology, Nanjing Forest Police College, Nanjing, China

Abstract: In the era of the fast development of data technologies, there is an urgency to continuously elevate the level of standardisation and legislation in building a social credit system since credit default behaviour is still widespread across society. It is critical to evaluate and classify citizens' credit risk with the help of domain experts. It can provide an effective basis for public security organs. Because of its well-known stability and interpretability, this study employs logistic regression for the analysis of the data and propose pertinent crime prevention and control measures. A logistic regression model is built and applied to predict and assess individuals' credit risk, and the model's prediction accuracy is 76.7%. Next, K-means clustering is used to divide the scores into different clusters to facilitate the grading of credit default risks of individuals. Based on our method, the relevant authorities can impose hierarchical prevention and management for ex-offenders and key personnel, which provides new, robust support for intelligent governance.

Keywords: credit risk; logistic regression analysis; K-means clustering; smart governance.

DOI: 10.1504/IJGUC.2026.152716

International Journal of Grid and Utility Computing, 2026 Vol.17 No.2, pp.149 - 158

Received: 28 Jun 2023
Accepted: 29 Jan 2024
Published online: 07 Apr 2026 *

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