title: Development of recommendations for identifying and making signs of illegal activities of investment companies
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
Creators: name: Ð¢ÑÑÐºÑÑÐµÐ², ÐÑÑÐ°Ñ
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2023
Subjects: ÑÐ¸Ð½Ð°Ð½ÑÐ¾Ð²Ð°Ñ Ð¿Ð¸ÑÐ°Ð¼Ð¸Ð´Ð°
ÑÑ ÐµÐ¼Ð°
Ð¸Ð½Ð²ÐµÑÑÐ¸ÑÐ¸Ð¸
Ð¼Ð¾ÑÐµÐ½Ð½Ð¸ÑÐµÑÑÐ²Ð¾
ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ
Ð¼ÐµÑÐ¾Ð´Ñ
Ð¿Ð»Ð°ÑÑÐ¾ÑÐ¼Ð°
Ð¼Ð¾Ð´ÐµÐ»Ñ
Ð´ÐµÑÐµÐ²Ð¾
ÑÐµÑÐµÐ½Ð¸Ðµ
ÑÐ»ÑÑÐ°Ð¹Ð½ÑÐ¹ Ð»ÐµÑ
Ð³ÑÐ°Ð´Ð¸ÐµÐ½ÑÐ½Ð¾Ðµ Ð±Ð¸Ð½Ð½Ð¸Ð½Ð³
pyramid scheme
scheme
investment
fraud
classification
methods
platform
model
tree
solution
random forest
gradient binning

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description: The purpose of this thesis is to analyze and identify common strategies of fraudsters for further classification of investment companies for fraud. The following tasks were solved: - The theoretical basis of the "phenomenon" of financial pyramid, and its structure was analyzed. The basic methods of fraud evaluation and indicators characterizing investment companies in general were defined, hypotheses were formulated; - Based on the literature review data was collected for the study. The distribution of parameter values by region, as well as the methodology used in the study have been analyzed. The rationale for the choice of methods is given. - Data was collected in a digital environment and processed and structured using MS Microsoft Excel spreadsheet software. Exploratory data analysis (EDA) and investment company classification models were carried out with the help of pandas library tools and scikit-learn written in the python programming language. - A preliminary analysis of the data has been carried out, on the basis of which "outliers" that could affect the results of the study are excluded and initial conclusions are formed. The relevance of this work is due to the annual increase in financial losses of both citizens and the state due to fraud. Classification models were built based on the results of preliminary data analysis. The best classification model has been selected and recommendations for identifying dishonest companies have been formulated.