Research on Forecasting and Precise Prevention of Telecommunication Network Fraud Cases at the Grassroots Level Based on the ARIMA Model

The current wave of telecommunication network fraud has swept across the country, with the focus of incidents gradually penetrating into county and rural areas. Grassroots public security organs have become the main force in combating and managing telecommunication network fraud crimes. Although the current prevention and control efforts have begun to show results, the methods of fraud are constantly innovating, making it difficult to guard against. Grassroots public security needs to continue to exert efforts, deeply analyze the characteristics of crimes, the patterns of incidents, and case data, and use big data technology to promote the modernization of combating and managing work. In this context, the author applies the time series ARIMA model to analyze and predict the number of various types of fraud cases in grassroots telecommunication network fraud cases, and calculates the number of incidents of each type of fraud in the next twelve time periods. Based on the predicted results, targeted and precise measures are formulated for high-incidence types of fraud, with data prediction as the starting point, aiming at precise and targeted strategies, and gradually building a new mechanism for precise anti-fraud.