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Financial risk in agriculture is generated by the interaction of market, credit, liquidity, climate and institutional factors. These risks affect farm income stability, debt-servicing capacity, investment returns and long-term sustainability. Recent climate extremes, input-price shocks and agricultural price volatility have increased the need for early-warning tools that combine financial and agronomic information. Although digital agriculture platforms, FMIS/ERP systems, satellite monitoring, IoT sensors and AI analytics improve data availability, many platforms still lack an integrated module that translates accounting data into interpretable financial-risk categories. This study addresses that gap by proposing a rule-based financial risk identification model designed for subsequent integration into digital agriculture platforms. Objectives: Objectives: This study develops a methodological model and a prototype system for identifying financial risks in agricultural enterprises. Methods/Approach: The model constructs parametric, index-based and interval spaces from standard financial indicators and implements them as production rules in the FIRA prototype. Results: The prototype calculates key risk ratios, assigns them to predefined interval states and generates an explainable financial-risk profile for an enterprise. Discussion: The approach complements digital agriculture and FMIS/ERP research by adding a transparent financial early-warning module that can be integrated with platforms such as ZEMELA. Conclusions: The proposed approach provides a practical basis for monitoring liquidity, solvency and cash-flow risk in agricultural enterprises. Its main value is decision support: it can assist farmers, advisers, lenders and insurers in early screening and follow-up financial planning. Empirical validation across farms and subsectors remains a necessary next step.