Adaptive System for Online Transaction Risk Assessment Based on Intelligent Analysis

Each year, the volume of financial transactions continues to grow steadily, accompanied by a corresponding rise in cyber threats, particularly fraud. As a result, identifying high-risk transactions in electronic commerce has become increasingly relevant. This study presents an adaptive approach to assessing the risks of online transactions based on intelligent data analysis, including machine learning methods. The proposed system employs a multi-level structure that incorporates behavioral profiling, semantic transaction evaluation, and integration of results to generate a final risk indicator. The approach focuses on identifying deviations from typical user patterns, correlating historical data with current activity, and responding flexibly to suspicious behavior and anomalies in real-time. This methodology aims to improve the accuracy of fraud detection, reduce the number of false positives, and ensure that the model remains adaptive in the face of growing threats in a dynamic environment.