AI and Machine Learning Can Prevent Fraud Attacks, Says Cupid Chan

The final prize for cybercriminals is to gain access to other people's money - so it's no wonder that account control attacks are on the rise. In this article, we will explain how banks can apply machine learning to defend against account control attacks.

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With the increasing number of transactions, users and third-party integrations, threats to the security of the financial system are also increasing. Cupid Chan who is the Managing Partner 4C Decision and also expert in AI and ML says that credit card fraud alone can cause a loss of more than $25 Billion a year. In this context, machine learning should be used by financial institutions in the strategy of security, risk management and compliance. That's because machine learning algorithms are trained to detect fraud.

Banks, for example, can use this technology to track account transaction parameters in real-time. In this way, they can identify fraudulent behavior with high precision, warning the customer and even preventing the transaction

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Machine learning is also used in training financial system so that it can detect a large number of micro and point money laundering techniques, such as smurfing.

Otherwise, data scientists also train systems to detect and isolate cyber threats, which can compromise the availability of resources and services and the digital security of banks.

For companies, smart machines can bring several benefits, especially in the security department, as it has now become a complicated task to try to manually differentiate real threats from unusual patterns due to the huge amount of data generated.

Several corporations have begun to explore its use to identify these problems more quickly and accurately. From machine learning, it is possible to automatically adjust the requirements according to the risks that each type of organization faces.

In network security, it is possible to use traffic profiles to recognize potential threats. In addition, user behavior analysis can be performed to detect internal threats, in addition to using this technology to filter spam and identify malware.

Advanced machine learning can also perform pattern recognition in the network flow, analyze historical data, logs, signatures and other sources to identify trends and detect problems.

Due to the advancement of technologies such as IoT (Internet of Things), BYOD and Big Data, the volume of data generated grows rapidly, making it virtually impossible to perform the analysis of this information manually. Therefore, the main advantage of using machine learning is its ability to process and analyze huge volumes of data quickly.

However, like any new technology, some challenges are still faced by companies that work with machine learning, how to make adjustments before the machines can accurately detect security problems. Therefore, initial work is necessary to prepare the processes that will be carried out automatically.

Another point of attention is the constant changes in the behavior of users and system traffic. These changes can signal problems that do not exist and, therefore, IT must classify these false alerts so that they are differentiated from the real threats.

Cupid Chan believes that despite the challenges mentioned, the progress that machine learning can bring to corporations is many, because, after initial configurations, machines adapt without the need for manual processes and are less prone to errors than human actions.