Ufa, Russian Federation
The article describes the factors that largely affect the process of investigating crimes (using the example of crimes in the field of computer information). The peculiarity of these criminal acts is shown in the form of a high degree of information uncertainty of the initial data on the mechanism of committing a crime or the identity of the offender. It is concluded that it is possible to increase the effectiveness of the investigation of crimes in the field of computer information (as well as other types of criminal acts) using predictive forensic models, which are formalized information systems designed to form a plan of investigative actions and a probabilistic assessment of their effectiveness.
crime investigation, information technology, artificial intelligence, AI, explainable artificial intelligence, XAI, decision support systems, DSS, predictive forensic models
1. Shevchenko E. S. Socio-technological determinants of investigative actions in the investigation of cybercrimes. Aktual'nye problemy rossijskogo prava. 2016; 10 (71): 160-169. (In Russ.).
2. Vozgrin I. A. Criminalistic methods of crime investigation. Minsk: Vyshehjshaya shkola Publ.; 1983. 215 p. (In Russ.).
3. Shatalov A. S. Criminalistic algorithms and programs: theory, practice, and applied aspects. Moscow: Liga Razum Publ.; 2000. 250 p. (In Russ.).
4. Exarchopulo A. A., Makarenko I. A., Zainullin R. I., et al. Criminalistics. Theoretical course: monograph. Ufa: NII PPG Publ.; 2022. 650 p. (In Russ.).
5. Artificial intelligence – predictive analytics and systems: analytical collection of the Competence Center of the National Technological Initiative of the Moscow Institute of Physics and Technology. Digital Economy. Available at: https://cdo2day.ru/wp-content/uploads/2020/11/almanach_ai_5.pdf (accessed 06.02.2026). (In Russ.).
6. Yarushkina N. G., Moshkin V. S., Guskov G. Yu., et al. Research and development of models, methods, algorithms for assessing the quality of datasets for predictive analytics of indicator dynamics. Ulyanovsk: Ulyanovsk State Technical University Publ.; 2024. 235 p. (In Russ.).
7. Petrovsky A. B. Theory of decision-making. Moscow: Academy Publ.; 2009. 398 p. (In Russ.).
8. Litvinov A. N., Stepanyuk R. L. Forecasting and planning in criminalistics. Moscow: YURKNIGA Publ., 2004. 160 p. (In Russ.).
9. Manokhin V. Conformal forecasting in Python. Moscow: DMK Press Publ., 2024. 306 p. (In Russ.).
10. Sarker I. H. AI-driven cybersecurity and threat intelligence: cyber automation, intelligent decision-making and explainability. Perth: Edith Cowan University; 2024. 200 p.
