How To Use Artificial Intelligence Technology To Solve The Problem Of Online Fraud?

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Since artificial intelligence cannot deal with cyber fraud alone, the combination of artificial intelligence and human intelligence is the way forward.

Internet fraud is a global threat, and experts believe it is the fastest growing and most dangerous economic crime in the world. It is not difficult to understand the reason. According to current forecasts, by 2021, the global losses caused by Internet fraud will reach US$6 trillion. Artificial intelligence (AI) is considered a key technology driver in the identity verification market, and many people believe that artificial intelligence will help solve the problem of online fraud. However, as artificial intelligence becomes more and more intelligent, fraudsters are also trying to challenge the protection it provides.

Now is a great opportunity for criminals to take advantage of many security protocols operated online, as the coronavirus pandemic has forced them to accelerate their digitalization. Therefore, new account fraud is becoming a major problem. The latest survey data from Action Fraud shows that since March 23 this year, online shopping fraud has caused a loss of 16.6 million pounds.

Where is artificial intelligence suitable for adoption?

From the perspective of identity management, artificial intelligence plays a vital role in the verification process. Its ability to recognize and classify documents by reading complex security features such as holograms and microtexts usually leads to strong and reliable authentication. In addition, major advances in biometric technology have provided a higher level of fraud prevention standards. For example, Liveness Detection is the ability of an artificial intelligence computer system to determine its interaction with real people rather than fake forgeries. It is helping to prevent fraudsters from using stolen photos, fake videos or masks for access. Or create an online account. Living body detection can identify and confirm that this is a living person's face in less than two seconds, and even advanced masks, imposters, imposters and pretenders can be detected with high accuracy.

In order to measure these biometric systems, the false acceptance rate (FAR) is considered critical. This is a specific key performance indicator used to measure the misacceptance of biometric systems. It tracks and evaluates the accuracy of the biometric system to determine the rate at which unauthorized users are authenticated on the system. Current regulations require that the false acceptance rate (FAR) of biometric systems used by the government must be less than 0.1%. AI-driven solutions can even surpass these incredible requirements.

Although artificial intelligence provides unparalleled security to prevent identity fraud, fraudsters have become increasingly adept at forging and forging real holograms, thereby bypassing artificial intelligence and machine learning techniques. Here, the adoption of hybrid methods becomes crucial, and even the most sophisticated fraudsters can hardly defeat the latest developments in artificial intelligence technology and human experts.

The importance of hybrid methods for verifying safety:

It is known that artificial intelligence and machine learning can quickly and accurately identify identity documents, extract relevant data and use biometric technology to compare facial features, but hybrid methods can make this step a step further. When technical inspections are combined with the knowledge of human body recognition experts, not only has the safety net of the company and its customers doubled, the risk has been reduced by half, and the conversion rate of new customers has also increased.

In practice, this means that proprietary technology will use artificial intelligence to scan and identify security features on ID documents with maximum accuracy, for example, including requiring users to tilt their ID documents in various directions in front of the camera for security features such as holograms Become visible. At the same time, specially trained identification experts will check security functions during the video conversation.

When using a combination of machines and humans for identity verification, the most critical difference may be the ability of humans to use their intuition to find differences in individual responses. For example, removing the eye from the camera can suggest to the identification expert that the customer is being duressed, which cannot be identified by certain technologies alone. People can also ask social engineering questions to determine whether the customer is genuine. Using automation alone means that some potential customers will not be able to pass the onboarding process at this stage, but by connecting them with experts who can further authenticate, the conversion rate will become higher. Simply put, through the combination of humans and machines, the highest level of security can be achieved.

There are many options when it comes to authentication. However, for many people, just using automated methods is sufficient. To prevent increasingly complex threats while increasing customer conversion rates, it is time to go beyond the minimum. People must explore what they can learn from and how to replicate the regulatory system and structure of Germany's famous and world-leading German Federal Financial Supervisory Authority to ensure that the financial industry remains robust and able to challenge the growing global cybercrime and fraud threats.

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