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Optimizing the compliance third-party supervision workflow of involved enterprises using artificial intelligence ant
Danqi Chen1, Weichen Jia1, Qi Chen1
1School of Media and Law, NingboTech University, Ningbo, 315000, China.
Artificial intelligence ant colony optimization (ACO) enhances enterprise compliance third-party supervision workflows. ACO significantly improves data quality, model performance, and overall supervision effectiveness compared to non-optimized methods.
Area of Science:
- Computer Science
- Artificial Intelligence
- Operations Research
Background:
- Third-party supervision is crucial for enterprise compliance.
- Existing workflows face challenges in data analysis and efficiency.
- Optimization is needed to improve the effectiveness of compliance supervision.
Purpose of the Study:
- To optimize the compliance third-party supervision workflow using the ant colony optimization (ACO) algorithm.
- To evaluate the performance improvements in data quality, model performance, scheme effectiveness, and supervision effectiveness.
Main Methods:
- Introduction of ant colony optimization (ACO) principles and advantages.
- Definition of a heuristic information matrix for data collection and analysis optimization.
- Simulation experiments to analyze the feasibility and effectiveness of ACO in third-party supervision workflows.
Main Results:
- ACO-optimized workflows demonstrated superior performance across key metrics.
- Metrics included Mean Square Error (MSE), Accuracy, Recall, F1 Score, AUC-ROC, Cost-Effectiveness Ratio (CER), Net Present Value (NPV), Supervision Coverage Rate (SCR), and Compliance Rate Change (CRC).
- Specific results showed improvements in data quality, model performance, and overall supervision effectiveness.
Conclusions:
- The ant colony optimization (ACO) algorithm effectively enhances compliance third-party supervision workflows.
- ACO provides a superior approach for data collection, analysis, and supervision, leading to better enterprise compliance outcomes.
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