Dive into the Future of Agricultural Finance with AI and Digital Twins

Introduction:

?Agricultural commodity finance plays a crucial role in sustaining global food supply chains and maintaining stability in agricultural markets. However, as many agricultural products originate from developing or emerging market economies, producers and local traders often face financial constraints and lack adequate support from local financial institutions. Consequently, while there is significant demand for finance from Western financial institutions, traditional credit risk assessment methods often struggle to address the unique challenges of this sector. This article explores our findings from Efides.io customer interviews looking into the utilisation of AI and Agricultural Digital Twin as automated support for credit risk assessment in agricultural finance. It delves into how these technologies leverage data analytics, satellite imagery, and weather modeling to enhance risk management practices in the industry.

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Assessing Counterparty Risk

Innovating credit risk assessment in agricultural commodity finance necessitates a holistic approach, encompassing Know Your Customer (KYC), credit risk evaluation, and country risk analysis. KYC procedures involve thorough due diligence to confirm customers' identity, assess financial stability, and understand business activities. However, accessing compliance information from producers and local traders in the global agri-commodity finance realm often poses challenges, resulting in additional costs and delays. Credit risk assessment entails analysing financial statements, credit history, and collateral, but the availability of local information, regulations, and language barriers complicates the process. Additionally, country risk assessment requires specialized expertise to evaluate political, economic, and legal risks, which may not always be readily available to financial institutions (FIs). Faced with low transaction values and profitability, FIs may prioritize more lucrative deals over exhaustive assessments, potentially overlooking critical risks.

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Transaction Risk Assessment

Transaction risk assessment in agricultural commodity finance encompasses various aspects, including transaction structuring, documentary credit analysis, and supply chain risk management. Transaction structuring involves navigating complex terms like pricing, payment conditions, and quality standards, which become even more challenging in the international agricultural trade due to the diverse regulatory landscapes and cultural differences across regions. The process of documentary credit analysis aims to ensure compliance with trade finance regulations spanning different jurisdictions, yet it encounters hurdles such as verifying documentation accuracy amidst language barriers and the involvement of multiple parties in transactions. Challenges in this area include the need to address regulatory compliance, ensure the authenticity of documents, manage the intricacies of transaction terms, and prevent fraudulent activities.

On the other hand, supply chain risk management focuses on identifying vulnerabilities such as supplier reliability, transportation delays, and adherence to regulatory standards. These challenges are compounded by geopolitical factors such as political instability and trade disputes, as well as environmental considerations like weather fluctuations. Mitigating risks in supply chain management requires proactive measures and comprehensive analysis to maintain the stability and efficiency of agricultural commodity trade finance. Additionally, the uncertainty surrounding yield output during the early stages of crop growth adds another layer of complexity and risk to the trade finance process, necessitating innovative solutions and risk mitigation strategies.

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New Emerging Credit Risk Assessment Methods:

Agricultural commodity financing poses unique challenges due to the inherent volatility of harvests and market prices, often not fully captured by traditional credit risk assessment methods. To address these challenges, various innovative approaches have emerged. Remote sensing and weather data analysis utilise satellite imagery and weather forecasts to assess crop health and predict risks associated with extreme weather events. AI and Big data leverage vast public and private datasets to develop predictive models, while blockchain technology ensures secure data sharing and smart contract automation. Digital platforms and online marketplaces analyse transaction data and develop alternative credit scoring models, enhancing risk assessment accuracy and financial inclusion. Together, these innovative approaches foster transparency, accountability, and resilience in agri-commodity trade finance.

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Agricultural Digital Twin

While agricultural digital twins have not yet been directly integrated into trade finance credit risk assessment, their potential as a significant innovation in this field cannot be overstated. Essentially, agricultural digital twins serve as virtual replicas of real-world farms, amalgamating data from diverse sources such as soil moisture sensors, weather stations, LEO satellite imagery, farm management software, and external data like market prices and historical weather patterns. This comprehensive data integration enables the development of simulation models capable of predicting crop yields, resource requirements, and potential risks. These digital twins offer farmers an unprecedented ability to visualize plant growth accurately, enabling precise monitoring of crop development, identification of growth patterns, and anticipation of yield outcomes. Moreover, they facilitate remote problem diagnosis and risk assessment by incorporating real-time sensor data, satellite imagery, and weather forecasts. This proactive approach empowers farmers to identify issues like pest infestations, nutrient deficiencies, or adverse weather conditions early on, facilitating timely interventions to mitigate risks and optimize crop yields.

The utility of digital twins extends far beyond individual farms, encompassing the entire agricultural supply chain, including ports, food processing facilities, and buyer facilities. By leveraging IoT technology and ship tracking, stakeholders can monitor the movement of agricultural commodities both on land and at sea, analyze transportation routes, and identify potential bottlenecks or disruptions. This comprehensive approach offers unprecedented transparency into supply chain risks. With enhanced visibility and early problem detection, stakeholders can proactively address supply chain disruptions, optimize logistics, and ensure the integrity and efficiency of the agricultural supply chain. Additionally, digital twins contribute to increasing transparency, enhancing risk management practices, facilitating early detection of issues, and promoting environmental, social, and governance (ESG) standards, while simultaneously reducing transaction costs and minimising food wastage.

One of the key benefits of digital twins for financial institutions' credit risk assessment is their ability to provide hyper-localised risk assessment by accounting for farm-specific factors like soil quality, microclimates, and farming practices. This precision allows for a more accurate evaluation of a borrower's ability to repay based on their unique circumstances. Additionally, digital twins facilitate predictive analytics by simulating different scenarios and predicting the impact of weather events, price fluctuations, and other risks on a farm's production and profitability. This empowers lenders with valuable insights for making informed credit decisions. Moreover, digital twins offer dynamic risk monitoring capabilities, continuously monitoring farm performance and adjusting risk assessments in real time. This early detection of potential issues enables lenders to take proactive measures to mitigate risks effectively.

Agricultural digital twins offer highly detailed, farm-specific, and dynamic risk assessment capabilities, providing a more precise, predictive, and dynamic approach to trade finance credit risk assessment. However, their implementation requires a comprehensive business transformation across farms, traders, and financial institutions. This involves reassessing business processes, people, and technology, and transitioning from traditional manual methods to more advanced digital approaches. While there are initial costs associated with acquiring technology for soil monitoring and satellite imagery, as well as the need to revamp existing processes for more comprehensive analysis, the long-term benefits outweigh these challenges. Additionally, education and training among individuals in farms, trading entities, and financial institutions are necessary to effectively analyse and leverage data for agricultural commodity finance. Despite these obstacles, agricultural digital twins have immense potential to revolutionise agricultural commodity finance, enhancing the sustainability and resilience of global food supply chains. Collaboration among stakeholders is crucial to overcome challenges related to data sharing, technology adoption, and regulatory frameworks. When successful, the new level of actionable insights and improved decision-making capabilities will enhance the resilience and sustainability of the agricultural sector, making the business more profitable for all parties involved.

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Case study examples

Case Study 1: Cargill & The Brazilian Soybean Boom (AI & Risk Mitigation)

Company: Cargill, a global agricultural commodities trader

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Challenge:? Securing financing for small Brazilian soybean farmers entering the international export market.? These farmers often lacked credit history, making traditional lenders hesitant.

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Solution:? Cargill partnered with an AI startup specialising in agricultural risk assessment. The startup developed a model that used satellite imagery, weather data, and historical yield information to create digital twins of individual farms. The AI model then analysed these digital twins to predict crop yields and assess creditworthiness.

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Results:? By using digital twins and AI, Cargill could accurately assess the risk profile of small farmers. This allowed them to provide financing to a broader range of growers, increasing their access to international markets and boosting Brazil's soybean exports. Additionally, Cargill benefitted from a wider pool of reliable suppliers.

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Key Takeaways:? AI can be used to analyse complex agricultural data and create digital twins, enabling lenders to assess risk more effectively and finance previously underserved farmers in international trade.

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Case Study 2: Eco-Kenya & Traceable Coffee Exports (Digital Twin & Transparency)

Company: Eco-Kenya, a Kenyan coffee cooperative

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Challenge:? Securing premium prices for their high-quality coffee beans in the international market.? Buyers often lacked transparency into the farming practices and origins of the beans.

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Solution:? Eco-Kenya partnered with a blockchain technology company to create a digital twin of their entire coffee production process. This digital twin tracked the beans from planting to processing, recording data on soil health, water usage, and fair trade practices.

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Results:? International buyers could access the digital twin data, verifying the eco-friendly and ethical practices behind Eco-Kenya's coffee.? This transparency allowed Eco-Kenya to command premium prices for their beans, increasing farmer income and their competitive edge in the global coffee trade.

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Key Takeaways:? Digital twins,? combined with blockchain technology, can ensure transparent and traceable agricultural products in international trade. This empowers farmers like Eco-Kenya to capture a higher share of the value chain.

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Case Study 3: Rabobank & Dutch Dairy Sustainability (AI & Fraud Prevention)

Company: Rabobank, a Dutch agricultural lender

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Challenge:? Ensuring the sustainability and ethical sourcing of milk from international dairy farms financing by Rabobank.

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Solution:? Rabobank partnered with an AI company specialising in anomaly detection.? The AI analysed satellite imagery, sensor data from dairy farms, and financial transactions to create digital twins of each farm. The AI then monitored these digital twins for anomalies that might indicate unsustainable practices or fraudulent activity.

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Results:? Rabobank could proactively identify farms potentially engaging in deforestation, animal cruelty, or manipulating milk production figures.? This allowed them to promote sustainable practices, protect their reputation, and mitigate financial risks associated with fraudulent dairy products in international trade.

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Key Takeaways:? AI can be used to monitor digital twins of farms in international trade, identifying potential sustainability concerns and fraud, ensuring ethical sourcing practices throughout the supply chain.

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Conclusion:

Innovative credit risk assessment approaches are essential for managing risks in agricultural commodity finance. Leveraging data analytics, satellite imagery, and weather modeling enhances risk management practices, contributing to the sustainability and resilience of agricultural finance in an increasingly dynamic environment.

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