Cognitive Intelligence–Based Decision Framework for Agricultural Credit Systems Using Predictive Customer Analytics

Dr. Wang Hui; Dr. Li Zhen

Authors

Dr. Wang Hui School of Computer and Cognitive Computing Shanghai Advanced Technology University Shanghai, China
Dr. Li Zhen Department of Artificial Intelligence Tsinghua Institute of Intelligent Systems Beijing, China

Keywords:

Cognitive Intelligence, Agricultural Lending, Predictive Analytics, Customer Relationship Management

Abstract

Agricultural credit systems are increasingly dependent on intelligent decision-support mechanisms to manage financial risk, optimize lending strategies, and improve customer relationship management (CRM) efficiency. Traditional rule-based credit evaluation frameworks are insufficient in capturing dynamic behavioral patterns of borrowers, especially in data-intensive rural financial ecosystems. This research proposes a Cognitive Intelligence–Based Decision Framework (CIDF) that integrates predictive customer analytics with adaptive decision modeling for agricultural lending systems.

The proposed framework leverages multi-source data ingestion, cognitive feature extraction, and predictive risk modeling to enhance creditworthiness evaluation. Inspired by prior advancements in AI-driven agricultural lending systems, particularly the predictive CRM-based decision mechanisms discussed by Chakravartula and Raghu (2026), this study extends the concept by embedding cognitive reasoning layers into predictive analytics pipelines. The framework combines machine learning classifiers, behavioral clustering, and decision fusion techniques to enable adaptive credit scoring.

A hybrid methodology is employed, incorporating cognitive knowledge representation models (Wang, 2011) and decision fusion strategies (Guang et al., 2015). The system architecture also draws insights from cross-layer optimization techniques in distributed systems (Lin et al., 2006), ensuring scalable and efficient processing of financial datasets. The evaluation focuses on predictive accuracy, risk classification performance, and computational efficiency.

Experimental simulations demonstrate that the proposed CIDF improves credit risk prediction accuracy while reducing false-positive loan rejection rates. Additionally, the framework enhances decision transparency and interpretability, making it suitable for regulatory-compliant agricultural finance environments.

The study contributes to the domain by introducing a cognition-enhanced predictive analytics model tailored for agricultural lending systems. It bridges the gap between traditional credit scoring mechanisms and modern AI-driven financial intelligence systems.

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