Modeling innovation financing in the Agri-FinTech environment

Abstract

Relevance. Armed aggression against Ukraine has inflicted devastating damage on the agricultural sector, with total losses exceeding USD 40.2 billion, while conventional bank lending proved structurally inadequate for supporting agri-innovation. Under these conditions, adopting FinTech-enabled non-bank financing mechanisms becomes a strategic necessity for the recovery and digital transformation of the agrarian sector. Purpose. This paper aims to develop and justify an integrated project financing and business analytics model for innovative agribusiness ventures, grounded in the convergence of financial technologies, predictive modelling and non-bank FinTech instruments. Given wartime economic disruption, agricultural losses surpassing USD 40.2 billion and the removal of 25-30% of farmland from productive use, designing effective alternative financing frameworks is critically important for restoring Ukraine's agricultural sector. Methods. The research employs a systemic approach to financial modelling of innovation projects; correlation-dispersion analysis to quantify digitalisation's effect on agricultural GDP; scenario planning and Monte Carlo simulation (10,000 iterations) to stress-test financial constructs; comparative assessment of conventional versus alternative financing channels; and synthesis of theoretical frameworks with applied FinTech tools tailored to agribusiness conditions. Results. An original conceptualisation of the financial model for an innovative agribusiness project is advanced: it is defined as an organisational-economic algorithm that integrates quantitative and qualitative project parameters, accounting for the specific nature of biological and operational assets in agriculture. A four-block Agri-FinTech architecture is characterised, covering crowdfunding and P2P platforms, blockchain and smart contracts, real agricultural asset tokenisation, and GIS-enabled land bank management. A five-step capital structure optimisation algorithm is constructed, incorporating self-financing (at least 30-35%), FinTech instruments, bank credit and grant funding. A four-scenario financial matrix is designed: optimistic (20-25% probability), baseline (50-55%), pessimistic (20%) and crisis stress-test (5%). Four systemic barriers to Agri-FinTech uptake in Ukraine are identified: uneven digital infrastructure, regulatory deficiencies, asymmetric access to state support, and wartime uncertainty. Five policy recommendations are advanced, including tailored crowdfunding legislation, a network of digital agri-finance hubs, a standardised digital enterprise profile, index-linked agricultural insurance on FinTech platforms, and incorporation of FinTech indicators into state programme assessments. Conclusions. The research validates the proposition that rigorous financial modelling, operating in conjunction with FinTech instruments, effectively curbs investment risk and widens agribusiness access to capital. The model's practical utility lies in its direct applicability to post-war agricultural recovery planning, bridging proven investment analysis methods (NPV, IRR, PI, DPP) with the capabilities of contemporary Agri-FinTech platforms.