Атты III халықаралық ғылыми-тәжірибелік конференция жинағЫ
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III TOM
- Бұл бет үшін навигация:
- T. Bakibayev Abstraction
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UDK 004. 5 BIG DATA AND MACHINE LEARNING IN BANKING SPHERE Arsenov Temirlan Erlanovich 2nd year master student of Almaty Management University Almaty, Kazakhstan Supervisor - T. Bakibayev Abstraction: Detecting fraud is no easy task. In fact, fraudulent transactions are quite rare and affect only an extremely small part of the organization‘s activities. But even a small percentage of such operations can quickly lead to huge financial losses if the company lacks the necessary tools and systems. And that's the problem. Criminals are very creative. As soon as the usual schemes become ineffective, they immediately change tactics. True, there is good news - thanks to the development of machine learning technologies, systems can learn, adapt and find new ways to prevent fraud. Keywords: Machine learning, banks, Big Data, algorithms, fraud Most organizations still use rule-based algorithms as their primary fraud detection tool. The rules easily reveal known patterns, but they are completely ineffective when it comes to new, yet unknown schemes; they do not know how to adapt to new conditions and are not able to resist the actions of fraudsters, who are becoming more sophisticated. Only machine learning systems can handle this. Today, machine learning is very popular. Most vendors claim that they somehow use it in their fraud detection solutions. Machine learning was invented a long time ago. However, one should not think that since machine learning has been used for a long time in this field, you can now rest on our laurels. In fact, everything is completely the opposite. Machine learning is a critical component in a fraud detection toolkit. Data volumes are growing, and the task of identifying fraud becomes more complex. When it comes to creating machine learning systems, the key to success is data. The more data, the better the model, and the fraud detection model is no exception. With the growth of information and the level of complexity, security specialists need scalable machine learning platforms. Although traditional tools are quite effective when working with thousands of records and several megabytes of data, in the real world, problems are measured in gigabytes or even terabytes of data. There is no one single algorithm or machine learning method that will work 100% in all cases. The key to success is the ability to try out a lot of individual algorithms and their combinations, as well as test them on various data sets. Specialists in data research require a set of a variety of controlled and uncontrolled teaching methods, as well as various technologies for constructing features. Finally, the creative aspect of using machine learning to detect fraud should not be overlooked. It is about new and unusual ways of using machine learning - for example, combining the methods of teaching models with a teacher and without a teacher, which is much more efficient than using separate methods. The more data, the better the model, and the fraud detection model is no exception. With the growth of information and the level of complexity, security specialists need scalable machine learning platforms. It would seem that everything is obvious, but it is a most difficult task for many organizations. After developing a machine learning model, it must be integrated into the work environment. If your data is stored in Hadoop, it makes sense to develop a machine learning model that you can later integrate into Hadoop without any problems. Similarly, in the case of streaming data in real time, a machine learning system capable of operating in real time or |