Artificial intelligence will become the mainstream of the future. It will appear in connected homes, cars, and anywhere else. Although it is not as eye-catching as an alien, artificial intelligence plays an important role in detecting fraud. Containing fraud is a protracted war. The two sides of the war, the good camp and the bad guys, are constantly adjusting to allow artificial intelligence to play its biggest role.
Artificial intelligence currently has three main methods to combat fraud, corresponding to the field of artificial intelligence development. They are:
1. Rules and reputation list
2. Supervise machine learning
3. Unsupervised machine learning
Rules and reputation list
Today, many modern organizations use rules and reputation lists to combat fraud, which is similar to the “expert systemâ€, which first entered the field of artificial intelligence in the 1970s. An expert system is a computer program that combines the rules set by domain experts. They are easy to get up and running, and they have the ability to understand, but they are also limited by the inflexibility and the need for a lot of manpower.
"Rules" are a logical statement of a person's code that is used to detect fraudulent accounts and behavior. For example, an organization might have a rule: "If an account costs more than a thousand dollars to buy a thing, coordinates are in Nigeria, and the login time is less than 24 hours, then the deal is blocked."
The list of reputations, similarly, is based on known bad behavior. A reputation list is a list that shows some IP addresses, device types, and other individual characteristics and their corresponding reputation scores. Then, if the IP address of an account appears on the blacklist, you can block them.
While rules and reputation lists are a good way to detect and prevent fraud, it's easy to be played by cybercriminals. Today, digital services abound, making the registration process easier. As a result, fraudsters can create dozens or even thousands of accounts in a fraction of the time and then use those accounts to learn rules and reputation lists. Offenders have easy access to cloud-hosted services, virtual private networks, anonymous email services, device emulators, and mobile device flash to escape the suspicion of a reputation list.
Since the 1990s, expert systems have fallen out of favor in many areas, losing to more advanced technologies. Obviously, we should have better anti-fraud tools. However, many anti-fraud teams in Hyundai have been using this basic method to detect fraud, which has led to a large number of manual assessments, false positives, and poor detection results.
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