Trade-based money laundering: development of the «red flags» system

The OECD and the FATF highlight the problem of money laundering via international trade with a view to disguising illicit gains and moving value through the use of trade transactions. For example, inaccurate invoices may be used, which, according to the Global Financial Integrity estimates, resulted in $0,9 trillion to $1,7 trillion losses in 148 countries in 2006–2015. In Russia, the authorities attempt to reduce the risks of money laundering within the framework of international trade through the use of currency regulation, while foreign countries are using a risk-based approach by developing the “red flags” systems that allow financial intelligence agencies, customs and other state bodies as well as subjects of financial market (through which the payments for export-import transactions are made) and the companies participating in international trade themselves to determine whether a transaction entails risks of money laundering. In addition, internal and international inter-agency exchange of information related to money laundering in international trade, including trade and financial data, is being developed.

A Hybrid Data Analytics Framework with Sentiment Convergence and Multi-Feature Fusion for Stock Trend Prediction

Stock market analysis plays an indispensable role in gaining knowledge about the stock market, developing trading strategies, and determining the intrinsic value of stocks. Nevertheless, predicting stock trends remains extremely difficult due to a variety of influencing factors, volatile market news, and sentiments. In this study, we present a hybrid data analytics framework that integrates convolutional neural networks and bidirectional long short-term memory (CNN-BiLSTM) to evaluate the impact of convergence of news events and sentiment trends with quantitative financial data on predicting stock trends. We evaluated the proposed framework using two case studies from the real estate and communications sectors based on data collected from the Dubai Financial Market (DFM) between 1 January 2020 and 1 December 2021. The results show that combining news events and sentiment trends with quantitative financial data improves the accuracy of predicting stock trends. Compared to benchmarked machine learning models, CNN-BiLSTM offers an improvement of 11.6% in real estate and 25.6% in communications when news events and sentiment trends are combined. This study provides several theoretical and practical implications for further research on contextual factors that influence the prediction and analysis of stock trends.

Research on dynamic and secure storage of financial data based on cloud platform

Traditional financial data storage methods are prone to data leakage and narrow data coverage. Therefore, this paper proposes a dynamic and secure storage method of financial data based on cloud platform.In order to improve the ability of enterprise data management, the paper constructs a financial cloud computing platform, mining financial data by rough set theory, and analyzing the results of frequent pattern mining of financial data by fuzzy attribute characteristics.According to the granularity theory, the financial data is classified and processed, and the CSA cloud risk model is established to realize the dynamic and secure storage of financial data.The experimental results show that. The maximum data storage delay of this method is no more than 4.1 s, the maximum data leakage risk coefficient is no more than 0.5, the number of data types can reach 30, and the data storage coverage is improved.

Research on Artificial Intelligence Classification and Statistical Methods of Financial Data in Smart Cities

In order to improve the effect of financial data classification and extract effective information from financial data, this paper improves the data mining algorithm, uses linear combination of principal components to represent missing variables, and performs dimensionality reduction processing on multidimensional data. In order to achieve the standardization of sample data, this paper standardizes the data and combines statistical methods to build an intelligent financial data processing model. In addition, starting from the actual situation, this paper proposes the artificial intelligence classification and statistical methods of financial data in smart cities and designs data simulation experiments to conduct experimental analysis on the methods proposed in this paper. From the experimental results, the artificial intelligence classification and statistical method of financial data in smart cities proposed in this paper can play an important role in the statistical analysis of financial data.

Does Corporate Governance and other Factors Influence Earnings Management? A Study on Indonesia’s Banking Sector

The issue of misreporting financial data and earnings management has become more prominent in recent years. Several studies have been conducted determining the influences of the mechanisms of corporate governance and earnings management in various countries. In this study, it was proven that the existence of a good corporate governance (GCG) mechanism did not suppress earnings management practices in the banking sector industry in Indonesia. However, another factor, dividend policy, can prove effective in suppressing earnings management. The measurement of earnings management in this study was carried out using the Modified Jones model with a population of 43 conventional banks from which research data were taken using a purposive sampling technique sourced from the Indonesia Stock Exchange (IDX). The analysis was carried out using multiple linear regression. The implication of this research is that the implementation of good corporate governance by an entity must be considered given that earnings management practices in Indonesia are still relatively high.

Analysis of the Effects of Employee Share Ownership on the Creation of Financial Value of Moroccan Companies Listed on the Stock Exchange

Employee share ownership (ESOP) is a highly acclaimed international incentive and motivation mechanism for employees thanks to its virtuous impact on company performance and organizational behavior. The work proposes to study the effects of this practice on the financial performance of Moroccan companies in order to assess the impact of employee participation on the creation of financial value. To this end, the authors conducted an empirical study based on two analytical methods using SPSS software. The sample is made up of 33 companies that have published and carried out at least one employee shareholding operation in Morocco since their IPO. The financial data was extracted from the accounting statements of each company for the period from 2015 to 2019. The results show that ESOP is weakly practiced by companies, and the tests do not allow to confirm of the existence of its impact on the performance of the companies studied.

Digital Currencies—More than a Passing Fad?

The financial system is currently in a period of exceptionally rapid technological and organizational change, with the adoption of cloud computing to store and process financial data, artificial intelligence to analyze it, and blockchain to secure it. It is fashionable to assert that digital currencies will be part of that future. But cryptocurrencies like Bitcoin are too volatile to possess the essential attributes of money. Stablecoins have fragile currency pegs that diminish their utility in transactions. And central bank digital currencies are a solution in search of a problem.

PERBANDINGAN METODE EXPONENTIAL SMOOTHING DAN ARIMA PADA PERAMALAN GARIS KEMISKINAN PROVINSI JAWA TENGAH

Forecasting is a technique that uses past data or historical data to determine something in the future. Forecasting methods with time series models consist of several methods, such as Double Exponential Smoothing (Holt method) and ARIMA. DES (Holt method) is a method that is used to predict time series data that has a trend pattern. ARIMA model combines AR and MA models with differencing order d. The poverty line is calculated by finding the total cost of all the essential resources that an average human adult consumes in one year. The lack of poverty reduction in an area is the lack of information about poverty. The selection of the forecasting method was made by considering several things. The Exponential Smoothing method was chosen because this method was able to predict time series financial data well and revise prediction errors. While the ARIMA method is better for short-term prediction, it can predict values that are difficult to explain by economic theory and are efficient in predicting time series financial data. There is still little research on comparing time series data for forecasting methods. Researchers are interested in comparing the Exponential Smoothing and ARIMA methods in implementing poverty line forecasting in Central Java. The two methods are compared by determining the best method for forecasting the poverty line in Central Java. The best forecasting method can be seen from the MAPE value of each method