Intelligent data analysis for mitigating commercial risks in sea agency operations

Анотація

The article examines the development and implementation of an intelligent system for commercial risk analysis in sea agency operations based on a hybrid combination of deep learning methods. The relevance is determined by increasingly complex maritime commercial relations, high uncertainty, heterogeneous data, and the limited effectiveness of classical approaches to multidimensional risk assessment. The purpose of the study is to develop an intelligent system capable of integrating financial and operational time series, counterparty tabular data, and textual information to generate consistent predictive assessments of commercial risks. The proposed approach formalizes overall commercial risk as an aggregated nonlinear function of credit–counterparty, liquidity, operational–financial, and market–macroeconomic risk components. The system pipeline includes data collection, preprocessing, intelligent modeling, integration, and interpretation of results. Its analytical core combines recurrent neural networks, deep models for tabular data, and transformer architectures within a unified hybridization module. Experimental results demonstrate that the hybrid model outperforms individual approaches in forecasting accuracy and risk-state classification, as reflected in lower MAE and RMSE values and a higher area under the ROC curve. The results confirm the synergistic effect of integrating deep learning models and substantiate the feasibility of applying the system to support proactive managerial decision-making in sea agency companies. Keywords: ensemble machine learning, deep learning, Sea Agency, decision support system, lightgbm, gradient boosting, operational risk mitigating, service-ergatic systems, stacking generalization, predictive analytics, port logistics.

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