Modern corporate environments are characterized by a high volume of data. Today, risk assessment involves multisourced, high dimensional data containing financial indicators, market trends, ESG data, and time changes. In such datasets, analytical methods that are traditional, matrix-based, often fail to capture the latent structure and multi-modal interdependencies. This paper proposes a new approach that incorporates tensor decomposition techniques such as CP (CANDECOMP/ PARAFAC), Tucker and Tensor-Train models into decision analytics for corporate risk assessment. In particular, we build a pipeline that utilizes tensors to transform heterogeneous corporate data into multi-way arrays which are easier to interpret as opposed to conventional methods, making risk modeling more compact. We validate our model on real life data sets from multinational companies and our model outperforms traditional principal component analysis and machine learning methods in classification accuracy, feature extraction as well as latent factor interpretability. Our results also confirm that tensor methods are beneficial for strategic risk assessment as they retain crucial dependencies among financial, temporal, and environmental dimensions. The results support the claim that tensor decomposition is crucial in high-dimensional financial analysis, and these systems require accurate, and explainable data-driven risk intelligence.