Author(s)

Jia Yu, JiaCong Jiang, BoRui Zhao^*

Affiliation(s)

School of Economics, Jinan University, Guangzhou 511400, Guangdong, China.

Corresponding Author

BoRui Zhao

ABSTRACT

In recent years, China's household debt has witnessed explosive growth, increasing financial vulnerability and posing a potential threat to economic stability. Based on the China Family Tracking Survey 2022 database, this study innovatively synthesizes three algorithms, namely the statistically based 3-criteria, Isolation Forest, and Local Outlier Factor (LOF), to detect anomalies with higher accuracy, and further applies a variety of data analysis techniques, such as cluster analysis, association rule mining, and Random Forest algorithm, to conduct a systematic and in-depth study of household economic anomalies. We also utilize various data analysis techniques such as cluster analysis, association rule mining, and random forest algorithm to conduct a systematic and in-depth study of household economic anomalies. Consequently, the study proposes policy recommendations such as strengthening financial education, providing precise support, and establishing a risk warning mechanism. These measures are expected to assist high-risk households, foster healthy economic development, and contribute to national economic stability.

KEYWORDS

Household economic anomalies; Anomaly detection; Optimal algorithm; Debt; Income-consumption deviation patterns

CITE THIS PAPER

Jia Yu, JiaCong Jiang, BoRui Zhao. The detection of household economic anomalies based on the CFPS 2022 database: an example of income-consumption deviation pattern analysis. World Journal of Economics and Business Research. 2025, 3(5): 43-49. DOI: https://doi.org/10.61784/wjebr3074.

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