Financial Distress Prediction Models for Nigerian Banks: A Multi-Dimensional Approach Using NPLs, Liquidity, and Capital Adequacy

@article{1709327,
      author = {Emeka R. Offor},
      title = {Financial Distress Prediction Models for Nigerian Banks: A Multi-Dimensional Approach Using NPLs, Liquidity, and Capital Adequacy},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {4},
      pages = {1745-1750},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1709327.pdf},
      abstract = {This study develops and validates predictive models for financial distress in Nigerian Deposit Money Banks (DMBs) using a multi-dimensional approach incorporating non-performing loans (NPLs), liquidity ratios, and capital adequacy measures. Utilizing panel data from 16 quoted Nigerian banks over the period 2010-2021, we employ logistic regression, discriminant analysis, and machine learning techniques to construct early warning systems for bank financial distress. The study defines financial distress using multiple criteria including regulatory intervention, negative equity, and sustained losses. Results indicate that NPL ratios, liquidity coverage ratios, and capital adequacy ratios are significant predictors of financial distress, with the combined model achieving 87.5% prediction accuracy. The developed models demonstrate superior performance compared to single-variable approaches, with NPLs showing the highest individual predictive power (AUC = 0.823), followed by capital adequacy (AUC = 0.756) and liquidity measures (AUC = 0.698). The findings provide valuable insights for bank management, regulators, and policymakers in developing proactive risk management strategies and regulatory frameworks for the Nigerian banking sector.},
      keywords = {Financial distress prediction, Nigerian banks, Non-performing loans, Liquidity risk, Capital adequacy, Early warning systems},
      month = {October},
  }