Application case of machine learning in real-time fraud detection

"Machine learning" is a computer science that refers to machines that learn about data and perform tasks that typically require human intelligence to accomplish. Now, the technology is in a period of rapid development: According to Gartner, by 2022, more than half of the data and analytics services will be replaced by machines instead of people, which will increase by 10%.

The advent of machine learning and its implementation in consumer-oriented applications has brought convenience to today's real-time economy. The use of machine learning in this area greatly reduces the chances of fraud before the victim is affected by fraud. In fact, more than 60% believe that waiting for what is going to happen will affect their perception of potential brands—especially when it comes to identity theft or financial fraud.

Real-time decision making requires machine learning and artificial intelligence

Machine learning and artificial intelligence are transforming businesses, brands and the industry as a whole. They have the ability to dramatically reduce labor costs, generate unexpected new ideas, and discover new models and create predictive models from raw data types. In addition, they can perform data analysis and implement real-time automated decisions that have never been achieved before. When machine learning and artificial intelligence are applied to actual data in an automated, low-latency manner, the results may affect the ongoing business. If you can use machine learning and artificial intelligence correctly, this will bring business and organization A real competitive advantage.

Examples of the impact of machine learning and real-time data analysis on high-risk businesses can be seen in fraud in various industries. The following are application examples of machine learning and artificial intelligence in fraud detection.

Prevent identity theft and fraud in financial operations

Huawei is the world's leading provider of communications, information and technology solutions that use the translytical database for real-time fraud analysis of credit card and mobile payment transactions - when you swipe, insert, or scan your phone, it displays authorization or rejection: This decision is made by a machine learning model that identifies fraud based on historical fraud data. This model is trained in a large data system that receives derived information from a memory translation database. The model is then loaded into the database as a stored procedure or user-defined function, a process that is repeated many times a day.

Continuous training in machine learning models is very important. Since fraudsters have been changing the method of fraud, we also need to constantly update the machine learning fraud detection model to ensure high quality decisions and low false positives, so continuous training is very important. An important feature of machine learning is the focus on prevention and detection. Banks with anti-fraud models have enough information to proactively detect fraud cases, rather than discovering fraud cases afterwards, which also increases customer satisfaction while reducing financial risk. Blocking fraud before it occurs not only saves financial institutions costs, but also helps minimize product exposure to ensure brand value.

Reduce fraud in digital advertising

Just like banks, adtech suppliers must deal with fraud quickly. Here, the perpetrator is an advertising robot with malicious code inside, which is as fraudulent as humans. Advertising agencies and advertisers therefore lose millions of dollars each year and are ultimately affected by Internet fraud loops such as Methbots, whose brand reputation is compromised. For example, these ad robots can programmatically bully current popular videos, publishers sell ads on videos, and simulate mouse-to-video interactions by programming mouse movements and fake social media information. Another example of an Adtech vendor is click fraud—the fraudster reaches the number of clicks he wants by letting people manually or automatically click on the ad.

In order to detect and process real-time click fraud, advertisers need to monitor each click of the customer, and if an abnormal click is detected, the solution will be quickly resolved. And, the solution must be fast, accurate, and flexible, which is enough to deal with modern fraudulent attacks. Detecting and blocking this type of fraud requires a database that can contain a large amount of legitimate and fraudulent traffic and determine which traffic belongs to which category before authorizing the advertising spend.

With machine learning and artificial intelligence, companies can detect anomalous data in as little as five to ten milliseconds and make the right decisions based on their information, even predicting results. In short, artificial intelligence and machine learning are a powerful tool, and together with a translytical database with a fast memory, it will make many important advances in many areas.