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Frontiers in Emerging Multidisciplinary Sciences

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Machine Learning–Enabled Advisory Mechanisms for Farm Financing Operations via CRM Forecasting Models

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Raka Pratama Wijaya 1
4 Department of Artificial Intelligence and Financial Systems Universitas Teknologi Nusantara, Indonesia

Abstract

Agricultural financing systems are increasingly dependent on intelligent decision-support frameworks to manage risk, optimize credit allocation, and improve customer relationship management (CRM) efficiency. Traditional credit assessment models in farm financing rely heavily on static financial indicators, which fail to capture dynamic behavioral, environmental, and operational variability associated with agricultural borrowers. This research proposes a machine learning–enabled advisory mechanism that integrates predictive CRM forecasting models for enhancing decision-making in farm financing operations.

The proposed framework incorporates hybrid intelligence principles inspired by augmented decision systems (Zheng et al., 2017) and adaptive artificial intelligence evolution strategies (Pan, 2016). The system is designed to process heterogeneous agricultural and financial datasets, including historical repayment behavior, seasonal crop yield variations, and socio-economic indicators. Predictive modeling techniques are applied to classify credit risk levels and recommend optimized lending strategies.

The methodology integrates multi-layered feature engineering, probabilistic forecasting models, and optimization-driven decision modules inspired by scheduling and allocation principles (Kim et al., 2015; Wang et al., 2010). Furthermore, cognitive decision support mechanisms are aligned with human-in-the-loop paradigms (Lam et al., 2015), ensuring interpretability and operational adaptability.

The findings demonstrate that integrating CRM-based predictive intelligence significantly improves loan default prediction accuracy, enhances risk stratification, and reduces operational inefficiencies in agricultural credit processing. The framework also aligns with prior findings in AI-driven agricultural lending systems (Chakravartula & Raghu, 2026), which emphasize the importance of predictive analytics in credit decision optimization.

Overall, the study contributes a scalable and adaptive machine learning architecture for farm financing institutions, enabling data-driven advisory systems that improve both financial sustainability and agricultural productivity outcomes.

How to Cite

Raka Pratama Wijaya. (2026). Machine Learning–Enabled Advisory Mechanisms for Farm Financing Operations via CRM Forecasting Models. Frontiers in Emerging Multidisciplinary Sciences, 3(01), 14–19. Retrieved from https://irjernet.com/index.php/fems/article/view/333
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