Financial Fraud Detection on Social Networks Based on a Data Mining Approach

The article summarizes the arguments and counter-arguments within scientific debate on issue of researching financial frauds in Internet. main goal research is to develop methodological principles for identifying cyber fraud social networks based analysis comments identify relevant text patterns that may indicate manipulation attempts further fraud. urgency solving this problem due fact mass involvement Internet users interactions virtual environment has contributed development various criminal schemes, as well personal data initially entered during registration information published can be used by a fraudster carry out illegal transactions. study carried following logical sequence: collecting with corresponding request under publications network using Instaloader tool; combining into groups content similarity; conducting preliminary processing (decomposing simpler components (tokens) reducing similar word forms their dictionary form); determination level similarity cosine building clusters presence signs networks. Instagram was chosen fraudulent operations showed offers appeals from specific people promoted help spam are dangerous. Based results study, it concluded national regulators need strengthen public control Internet, improve security system at technical latest machine learning methods commit actions subsequent imposition sanctions such