Predict and Optimize Financial Services Risk Using AI-driven Technology

Abstract

With the rapid development of internet technology, many industries have embarked on a digital transformation. However, while the Internet has brought convenience to users, it has also become a breeding ground for criminals to commit fraud. On the one hand, a large number of users on the Internet more or less left data, criminals can use this information to practice accurate fraud users, improve the success rate of fraud; On the other hand, online financial transactions such as banking and e-commerce also provide more opportunities for criminals to commit fraud. Therefore, all kinds of fraud methods emerge in an endless flow, through the telephone, information, fishing and other means of fraud, not only to bring hundreds of millions of losses to society every year, but also to the security of people's lives have a huge threat. Monitoring and preventing online fraud is an important part of the cybersecurity industry. For known network fraud, based on the domain name of the phishing site, the account number and mobile phone number that send fraudulent information, simple and effective monitoring and defence can be carried out through the blacklist. However, it is difficult for traditional means to effectively defend against undocumented fraud. With the development of machine learning technology, it is the main research direction of fraud detection methods to discover the information sources and characteristics of information content through machine learning technology, and make real-time and continuous accurate judgments. This paper realises credit fraud detection by generating adversarial network technology, so as to prevent network security risks.