PREDICTION OF FINANCIAL FRAUD RISKS AMONG OLDER ADULTS: THE APPLICATION OF GA-XGBOOST MODEL
2024
Predicting Financial Fraud Risks in Older Adults
publication
Evidence: moderate
Author Information
Author(s): Zhao Jin, Li Yuanyuan, Jin Hao, Zhang Xuemin
Primary Institution: Hebei University of Technology, TianJin, Tianjin, China
Hypothesis
Can a GA-XGBoost model accurately identify financial fraud risks among older adults?
Conclusion
The GA-XGBoost model effectively predicts financial fraud risks, highlighting key factors such as education level and loneliness.
Supporting Evidence
- Lower education level, older age, and loneliness increase financial fraud risks.
- Increased awareness of rights protection and better sleep quality have protective effects.
- The model was compared with established machine learning algorithms like LightGBM and Random Forest.
Takeaway
This study created a smart model to help find out who might be tricked into financial scams, especially older people.
Methodology
The study used a genetic algorithm to optimize the XGBoost model for predicting financial fraud risks.
Participant Demographics
Older adults in China, with factors like education level and loneliness considered.
Digital Object Identifier (DOI)
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