In order to reduce the amount of fraud cases and the number of people defrauded, increase the rate of detection and efforts of crackdown, and achieve the goal of “two decreases and two increases” of the public security department. AsiaInfo makes use of big data and AI technology to accurately identify the information such as traditional telecom fraud, and establish a full process anti-fraud warning and prevention & control system of “exante, mid-interim, exposure and expost”.
Conducting accurate publicity of fraud scenarios in the jurisdiction
Identification and prevention of potential victims, dissuasion by intelligent cloud call/flash message, identification and prevention of phishing fraud information
Identification and tracing of dens, case-related information investigation, case-related information analysis and countermeasures
Comprehensive use of multi-dimensional data modeling, and acceptance of customization of local personalized needs
The use of AsiaInfo’s unique big data intranet software and hardware deployment enables cold start of public security, and realizes high input-output ratio
It is simple to use, fast and lightweight, and is suitable for general use and promotion by public security/police stations in various regions and cities
Reducing reporting rate of telecom fraud
Improving the detection rate
Improving people’s satisfaction
Since the public security units in the cities of a certain province put Anti-fraud System into use, the anti-fraud efforts are significantly effective, the “quantity of numbers involved in the investigation” and the “data of user complaints and reports” have obviously declined.
The public security bureau in the main urban area of a city used the pre-warning system, and conducts highly frequent publicity + accurate publicity for 1 month, which leads to a 22.95% drop in the number of reported cases. Immediate results have been achieved.
A city’s subordinate district and county public security bureaus tried out the precise interim prevention function of the platform for half a month in August 2019. During the trial period, the number of reported cases decreased by 32 from the previous month, with a decrease of 23.07%.