Leveraging Predictive Analytics to Identify Early Warning Signals of Loan Default in Nigerian Commercial Banks

Abstract

In Nigeria, the banking industry is in trouble because defaults on loans are a risk to financial stability, especially given the increasing volatility in the economy. With NPLs on the rise, the economic outlook appears grim, predictive analytics could offer a solution by foreseeing the possibility of a loan default, thereby enabling loan default risk mitigation. Incorporating relevant materials from the Central Bank of Nigeria (CBN) and the Basel Accords, along with research from the African Development Bank and local studies on credit risk modeling, the author creates an operational model of predictive default detection, creates an organizational readiness assessment tool, presents three synthesis tables from a database of 100 banking experts organizing overall readiness, expected benefits, and barriers to implementation, and proposes a set of guidelines for a phased implementation strategy. This study argues that, with proper governance, predictive analytics could optimize default prediction accuracy, substantially decrease NPL ratios, and improve recovery rates, though resolving data quality concerns, model lack of explainability, legal compliance, and machine learning skill gaps.