An evaluation of loan monitoring and evaluation systems in rural agricultural banking: a case study of United Bank for Africa
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Background of the Study
Effective loan monitoring and evaluation systems are crucial for maintaining the sustainability of agricultural lending in rural areas. United Bank for Africa (UBA) has implemented comprehensive monitoring frameworks designed to track loan performance, assess borrower behavior, and enable timely interventions. These systems combine traditional monitoring techniques with modern digital tools such as real-time data analytics, automated reporting, and mobile monitoring applications (Oluseyi, 2023). The objective is to ensure that loans disbursed for agricultural purposes are effectively utilized and that borrowers adhere to repayment schedules.
UBA’s approach integrates periodic field visits, digital performance tracking, and continuous risk assessment to maintain a robust credit portfolio. The bank uses advanced software to gather data on repayment trends, seasonal income variations, and market conditions, which are then used to fine-tune credit policies. In addition, regular feedback from borrowers and field agents is incorporated into the monitoring system to improve service delivery and borrower support. This multi-layered monitoring process not only reduces the incidence of non-performing loans but also builds trust between the bank and its rural clients (Akinola, 2024).
Despite these innovations, challenges remain in implementing a fully effective monitoring system in rural environments. Issues such as data gaps, unreliable connectivity, and the limited capacity of field staff to accurately capture and report information can undermine the system’s accuracy. Inconsistent application of monitoring procedures across different regions further complicates efforts to standardize evaluations. This study evaluates the effectiveness of UBA’s loan monitoring and evaluation systems in rural agricultural banking, aiming to identify best practices and propose enhancements for better credit risk management (Ibrahim, 2025).
Statement of the Problem
Although UBA has invested in advanced loan monitoring systems, the bank continues to face challenges in ensuring comprehensive oversight of its agricultural loan portfolio in rural areas. Data collection is often inconsistent due to infrastructural limitations and varying levels of staff training in remote regions (Oluseyi, 2023). These inconsistencies result in gaps in information that delay the identification of repayment problems and hinder timely interventions. Moreover, the integration of digital monitoring tools with traditional methods remains problematic, leading to discrepancies in data and incomplete risk assessments.
Additionally, the lack of a standardized protocol for monitoring across different regions creates variations in loan performance metrics, which complicates the bank’s ability to evaluate the true effectiveness of its credit policies. External factors, such as unpredictable weather patterns and market fluctuations, further exacerbate the difficulties in accurately monitoring loan performance. As a result, the bank’s efforts to mitigate credit risk are often reactive rather than proactive, which contributes to higher default rates and reduced overall efficiency (Akinola, 2024; Ibrahim, 2025). This study seeks to identify the critical barriers to effective loan monitoring and evaluation in rural agricultural banking and to propose strategies for improving data accuracy and intervention timeliness.
Objectives of the Study
• To assess the effectiveness of current loan monitoring systems in rural agricultural banking.
• To identify challenges in data collection and integration within monitoring frameworks.
• To recommend improvements for enhancing the accuracy and responsiveness of monitoring systems.
Research Questions
• How effective are UBA’s current loan monitoring systems in identifying repayment issues?
• What are the main challenges in collecting and integrating monitoring data?
• What measures can improve the timeliness and accuracy of loan evaluations?
Research Hypotheses
• H1: Advanced monitoring systems significantly reduce non-performing loans.
• H2: Inconsistent data collection negatively impacts loan performance assessments.
• H3: Standardized monitoring protocols improve intervention efficiency.
Scope and Limitations of the Study
This study examines UBA’s loan monitoring systems in selected rural agricultural regions. Data are obtained from bank performance reports, field agent interviews, and digital system analytics. Limitations include regional disparities in data quality and infrastructural challenges.
Definitions of Terms
• Loan Monitoring Systems: Tools and processes used to track the performance and repayment of loans.
• Agricultural Banking: Financial services provided to support farming activities.
• Non-performing Loans: Loans in which scheduled repayments are not made.
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