A Bipolar Pythagorean Neutrosophic Algebraic Lattice Model with Hybrid Entropy Weighting for Credit Risk Evaluation for Agricultural SMEs Supply Chain Finance

In agricultural supply chain finance (SCF), small and medium-sized enterprises (SMEs) often face severe credit risk due to unstable production, market volatility, and limited access to formal financial records. Traditional risk assessment models fail to handle uncertainty, contradiction, and bipolarity in real-world data, especially for agriculture. To address this gap, we propose a new mathematical model based on bipolar Pythagorean neutrosophic theory. This model introduces an algebraic lattice structure to represent the complex relationships between risk criteria and integrates a novel hybrid entropy weighting method. The framework allows for the evaluation of both positive and negative aspects of credit risk using bipolar neutrosophic sets, while preserving mathematical rigor through lattice-based operations. Several new formulas for entropy, aggregation, and distance are developed to support accurate and flexible assessment. This model is especially suitable for agricultural SMEs, where data is often vague, contradictory, or incomplete. A numerical case study is provided to show the practical performance of the proposed model.