Loan Default Prediction Practices and Machine-Learning Readiness of Microfinance Banks in Bauchi State, Nigeria
DOI:
https://doi.org/10.33003/fujafr-2026.v4i3.431.524-536Keywords:
Microfinance Banks, Loan Default Prediction, SME credit, Machine learning, Credit RiskAbstract
Purpose: This study investigated loan-default prediction practices and machine-learning readiness of MFBs in Bauchi State, Nigeria with emphasis on loan appraisal and approval practices, loan default factors, information and risk-prediction practices, and institutional and technological machine-learning readiness.
Methodology: An exploratory qualitative research design was adopted. Data were collected through key informant interviews with MFB personnel involved in loan appraisal, credit assessment, approval, monitoring and recovery. The interview data were analysed thematically using NVivo 14, following Braun and Clarke's thematic analysis framework. The analysis generated four major themes: loan appraisal and approval practices; contextual factors contributing to loan default; information and tools used for loan-default risk assessment; and institutional and technological readiness for machine-learning adoption.
Results and conclusion: Findings indicate that MFBs rely on repayment history, borrower and business information, guarantor or collateral information, loan-monitoring records, and loan officers' professional judgement when assessing default risk. Default was associated with business and income instability, household financial pressures, loan diversion, borrower-related factors and broader economic conditions. The findings further indicate that potential machine-learning adoption is constrained by data quality and availability, ICT infrastructure, staff analytical capabilities and management support. The study concludes that effective machine-learning-based loan-default prediction requires more than access to predictive algorithms; it requires reliable and standardised credit data, appropriate digital infrastructure, skilled personnel and organisational readiness.
Implication of findings: The study recommends incremental adoption of machine-learning decision-support systems alongside strengthened data management, staff capacity development and systematic loan monitoring.
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