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With the development of AI technology, people have been gradually carrying out applications in various fields of AI. However, the development of AI has also given some people an opportunity to take advantage of it.

With the widespread availability of generative AI technologies, such as GPT-4 and other powerful language models, expectations are high about the potential of AI in various fields, such as text creation, art painting, translation, and replacing traditional human customer service.

However, along with this anticipation of future technologies, we also have to face the risk of possible misuse of AI technologies. What concerns us is that some criminals have started to cleverly use AI technology to commit telecom fraud, which undoubtedly brings new challenges to our prevention. If the related security and privacy issues are not properly addressed, it might negatively affect the prospects of AI and create pressure on AI development.

     AI fraud: why the success rate is extremely high?

The emergence of AI fraud is largely due to the rapid development of AI technologies, especially deep learning and machine learning. Through these advanced technologies, scammers are able to gradually train AI that can mimic human behavior and language, and even produce extremely realistic fake videos or audio. These fakes are extremely realistic, making it incredibly difficult to identify the real from the fake, thus greatly increasing the effectiveness of the fraudsters' deception.

In an increasingly digital society, everyone is generating large amounts of digital information. This provides a wide operating space for fraudsters, who can use these platforms, together with AI technology, to carry out large-scale and precise fraudulent acts.

Whether it is through big data analysis to access the behavior habits of the scammed target and more precisely locate the social class and economic strength they are in, or approaching the target through familiar preferences to gain their trust and ultimately reach the scamming goal, it shows the efficiency and concealment of AI scams.

Fraudsters often have more information and technological resources than their victims, which makes it difficult for victims to detect and defend themselves against such high-tech frauds. Thus, the asymmetry of information and technology has undoubtedly become a major advantage of AI fraud.

The lag of laws and regulations is also a problem that cannot be ignored. Existing laws and regulations often fail to keep up with the rapid development of technology, resulting in AI fraud finding enough room to operate in legal loopholes.

Finally, it cannot be ignored that financial incentives are the main factor driving the actions of fraudsters. Because AI fraud is often difficult to detect, fraudsters can reap higher financial rewards. This huge lure of profit also makes them more and more motivated to carry out such activities.

     AI scams are less costly than traditional scams

AI scams are highly automated. A properly trained AI model is able to run automatically without human intervention. This means that once an AI fraud tool is created and deployed, it can conduct fraud 24/7, without human involvement, thus reducing the human cost of fraud.

In addition, AI fraud is highly scalable. Unlike traditional fraud methods that require human involvement, AI fraud can be easily scaled to large scale in a short period of time.

However, we need to realize that it is not easy to develop and train an effective AI model, which requires a large amount of data, specialized technical knowledge and computing resources, all of which require a certain cost.

The risks and costs of AI fraud are also likely to increase as legal regulation increases and fraud-recognition technology evolves. Thus, despite the cost advantages of AI fraud in some respects, there are still multiple risks and costs to consider in implementing AI fraud.

     The key role of social platforms

In this digital age, social platforms have clearly become a major battleground for AI fraud. The large number of online activities and social interactions people engage in on social platforms provides fraudsters with a rich selection of targets and sources of information, making AI fraud proliferate on social platforms.

The pluralistic and open nature of social platforms makes regulation and prevention difficult. With so many users and different behaviors, fraudsters are often able to blend in and commit crimes. Once identified, they often have already defrauded a large amount of wealth and gotten away with it.

Therefore, social platforms play a crucial role in preventing AI fraud. They need to use advanced technology for prevention, develop and enforce clear user policies, and strengthen education and guidance for users in order to minimize the occurrence of AI fraud.

1. Upgrade technology: Social platforms need to strengthen technical protection. With advanced technologies such as machine learning, platforms can detect and flag abnormal behavior or suspicious accounts. For example, by analyzing users' behavior patterns, machine learning algorithms can look for and identify behaviors that are inconsistent with normal behavior patterns, which often suggest the possibility of fraud.

2. Establish rules: Social platforms should develop and enforce clear user policies. Once violations are found, platforms will impose strict penalties. Other platforms also need to clearly state which behaviors are prohibited and the consequences they will face for violating the rules. For violating users, social platforms need to set up a fair and fast processing mechanism to ensure fair treatment of every user.

3. Cultivate awareness: Social platforms can prevent AI fraud by raising users' awareness of online security. By publishing educational content on how to use social platforms safely and teaching users how to identify and prevent AI fraud, users can effectively reduce their own risk of becoming victims of fraud.

Also, reminding users not to trust strangers easily and warning them to protect their personal information are important measures.