Implementation of organization and end-user computing-anti-money laundering monitoring and analysis system security control

• Published in: PloS one 2021-12, Vol.16 (12), p.e0258627-e0258627
• Main Author: Sun, Ling
• Format: Article
• Language: English
• Subjects: Access control; Accuracy; Algorithms; Computer and Information Sciences; Crime; Customers; Data analysis; Data mining; Data transmission; Decision analysis; Decision trees; Digital signatures; Engineering and Technology; Experimental data; Financial institutions; Financial services; Insurance; Insurance companies; Machine learning; Money laundering; Monitoring; Operating systems; Organized crime; Physical Sciences; Prevention; Registration; Research and Analysis Methods; Security; Security management; Social Sciences; Subsystems; Surveillance; Systems Analysis; Technology; Technology assessment; User profiles
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0258627

The Monitoring and Analysis Centre for the fight against money laundering is a valid financial information body which is responsible for collecting, analysing and providing financial information and conducting international exchanges of financial information for relevant undertakings. By constructing the analysis of the monitoring of the local and foreign currency and of the data transmission subsystem in the plan for the transitional period against In the light of the above, the Commission will continue to monitor the implementation of the acquis in the light of the progress made in implementing the acquis future new systems. The purpose of this paper is to study the research and implementation of the security control of the anti-money laundering monitoring and analysis system. This article studies the application of decision tree analysis technology in the anti-money laundering monitoring system of insurance companies to achieve the purpose of improving the anti-money laundering monitoring technology and capabilities of insurance companies. The emergence of data mining technology provides a new system solution for anti-money laundering monitoring. For insurance anti-money laundering, how to find potential money laundering cases in suspicious and large surrender transactions is key. The experimental data show that the decision tree method is the best predictor of the customer category between the insurance application and the surrender days. The decision tree analysis technology has greatly improved the security monitoring capabilities of the insurance in the anti-money laundering monitoring system. Experimental data shows that the security control capabilities of the anti-money laundering monitoring and analysis system make the accuracy of the entire model reach 95%, the accuracy of large and suspicious transactions reaches 88.6%, and the correct classification of customers is about 99.6%.