This article aims to forecast the information trends related to the most popular cyberattacks, seen as the cyber-crimes’ consequences reflecting on the Internet. The study database was formed based on online users’ search engine requests regarding the terms “Cyberattacks on the computer systems of a financial institution”, “Cyberattacks on the network infrastructure of a financial institution”, and “Cyberattacks on the cloud infra-structure of a financial institution”, obtained with Google Trends for the period from 16 April 2017 to 4 October 2022. The authors examined the data using the Z-score, the QS test, and the method of differences of average levels. The data were found to be non-stationary with outliers and a seasonal component, so exponential smoothing was applied to reduce fluctuations and clarify the trends. As a result, the authors built additive and multiplicative cyclical and trend-cyclical models with linear, exponential, and damped trends. According to the models’ quality evaluation, the best results were shown by the trend-cyclic additive models with an exponential trend for predicting cyberattacks on computer systems and the cloud infrastructure and a trend-cyclic additive model with a damped tendency for predicting cyberattacks on the network infrastructure. The obtained results indicate that the U.S. can expect cybercrimes in the country’s financial system in the short and medium term and develop appropriate countermeasures of a financial institution to reduce potential financial losses.

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The article is devoted to finding the answer to two research questions. What illegal practices are most significant for clusters of developed countries formed by similarities in trends in corruption, shadow economy, money laundering, and crime rates? What social, economic, regulatory, and digital factors most influence them in each group? The pair correlation coefficients for illicit practices indicators confirm the presence of tight and statistically significant relationships in their trends for 36 developed countries. The agglomerative clustering and canonical analysis results identified that tackling the shadow economy is crucial for Estonia, Slovenia, and Lithuania; corruption for Portugal, Hungary, Cyprus, etc.; the shadow sector and crime levels for Denmark, Norway, Finland, Sweden, and New Zealand; corruption, money laundering, and crime for Canada, Germany, the USA, etc.; four illegal practices for Italy, Greece, Turkey, Croatia, Bulgaria, and Romania. The canonical analysis revealed that social and regulatory factors influence the trends of illicit practices in developed countries more than economic and digital ones. Network analysis showed their single moderate influence in most cases. Edge evidence probability analysis confirmed a high probability of a relationship between some pairs of social, economic, regulatory, digital and illegal indicators. However, Bayesian network analysis showed a low likelihood of mutual influence of single factors, confirming the importance of the group influence.