Efficient Insolvency Detection in Iranian Banks through Statistical Process Control: A Real Case Study

Bank insolvency poses significant risks to economic stability, particularly in fluctuating financial ecosystems. This study introduces a novel framework that integrates Statistical Process Control with capital adequacy assessments to improve early detection and resolution of bank insolvency. Unlike traditional models that rely on static thresholds or historical analysis, SPC enables dynamic, real-time monitoring of capital adequacy trends. Our model identifies critical insolvency indicators through SPC methodologies by analyzing ten major Iranian banks over a decade, and categorizes them as well-capitalized, medium-capitalized, and small-capitalized banks. Results reveal that all participating banks show statistically out-of-control processes, suggesting heightened insolvency risks. Specifically, six banks were consistently below the lower specification limit, pointing to the efficacy of SPC in preemptively addressing insolvency challenges. This model advocates ongoing monitoring and strategic financial adjustments to stabilize the overall economic environment.