SHS Web Conf.
Volume 65, 2019The 8th International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2 2019)
|Number of page(s)||7|
|Section||Machine Learning for Prediction of Emergent Economy Dynamics|
|Published online||29 May 2019|
Fraud detection models and payment transactions analysis using machine learning
Department of Economic Cybernetics, Taras Shevchenko National University of Kyiv, Kyiv, Ukraine
* Corresponding author: firstname.lastname@example.org
The work’s aim is to research a set of selected mathematical models and algorithms that examine the data of a single payment transaction to classify it as fraud or verified. Described models are implemented in the form of a computer code and algorithms, and therefore can be executed in real-time. The main objective is to apply different methods of machine learning to find the most accurate, in other words, the one in which the cross-validation score is maximal. Thus, the main problem to resolve is the creation of a model that could instantly detect and block a given fraudulent transaction in order to provide better security and user experience. At first, we determine the classification problem: which initial data we have, how we can interpreter it to find the solution. The next part is dedicated to presenting the methods for solving the classification problem. In particular, we describe such approaches as Logistic Regression, Support Vectors Method (SVM), K-Nearest neighbours, Decision Tree Classifier and Artificial Neural Networks; provide the notion of how these methods operate the data and yield the result. At the end, we apply these methods to the provided data using Python programming language and analyze the results.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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