A Systems Engineering Approach for Credit Risk Assessment in Agricultural AIoT Data - Syaukani, Muhmmad; Subiyantoro, Eko (RIGGS: Journal of Artificial Intelligence and Digital Business, 2026)
Evaluation of credit risk is an essential element in the process of granting credit at the Rural Credit Bank (RCB), particularly for the Micro, Small, and Medium Enterprises (MSMEs) sector in rural agriculture. The conventional approach based on historical data in finance is often unable to reflect the real conditions of agricultural business, which are influenced by environmental and productivity factors. Research: This aim: To design a methodology for evaluating risk more comprehensively by utilizing agricultural data based on Agricultural Artificial Intelligence of Things (AIoT) through the Systems Engineering Process (SEP) approach. The SEP methodology serves as a framework for systematic work, encompassing Requirements Analysis, System Design, Implementation, Testing, Deployment, and Maintenance stages to ensure the developed system fulfils the RCB technical and operational data needs. Data from agricultural sensors integrated into an in-system computer support risk analysis and credit decisions in a more objective, data-driven way. Approach: This allows the use of real-time, contextual non-financial data as a supplement to conventional financial data. Design results show that SEP implementation can produce a system evaluation risk structure that is structured, adaptive, and aligned with the RCB business processes. This potential increase in accuracy evaluation risk reduces subjectivity in officer credit and supports improvements in inclusion in finance for the MSME sector and rural agriculture.