Core Viewpoint - The execution of property penalties is crucial for achieving the purpose of punishing crimes, and the supervisory responsibility lies with the prosecutorial authorities [1] Group 1: Case Study and Findings - A case involving a convict, Zhai, who was sentenced to death with a two-year reprieve and had all personal property confiscated, revealed discrepancies in his financial status while incarcerated [2] - Despite being sentenced to confiscation of all personal property, Zhai's monthly spending in prison was significantly high, indicating he had financial means, which raised suspicions among prosecutors [2][3] - Data analysis revealed that Zhai had engaged in actions to transfer assets to evade the execution of property penalties, prompting further investigation by prosecutors [2][3] Group 2: Implementation of Big Data Models - The use of big data legal supervision models has expedited the identification of anomalies in property penalty executions, reducing the investigation time from five days to one [3][4] - The establishment of a specialized model for supervising small fines has led to the successful execution of over 1.83 million yuan in fines since its implementation in February 2024 [4] - The big data model aggregates extensive data on inmate spending and living standards to accurately assess the financial capabilities of inmates regarding property penalties [4] Group 3: Challenges and Educational Efforts - Some inmates exhibit a lack of concern for fine payments, believing that serving their sentence absolves them of financial obligations, while others attempt to evade penalties through asset transfers [5] - To enhance awareness of property penalty execution laws, educational videos have been created and displayed to inmates' families, and guidelines for fine payments have been posted in visitation areas [5] - In 2024, the Beijing People's Procuratorate has implemented 14 big data legal supervision models across criminal execution supervision, with over 70% of cases being identified through these models [5]