Credit risk represents a significant challenge within the banking industry. Effective management of this critical issue necessitates the development of robust strategies and policies aimed at the identification, measurement, and control of this risk. Addressing credit risk demands a comprehensive and rigorous approach encompassing risk identification and assessment, determination of risk levels, and the establishment of appropriate procedures and measures for mitigation. This research investigates the management of credit risk within the Industry and Mine Bank. Employing the data envelopment analysis (DEA) methodology, this study proposes a credit rating model for corporate clients seeking financial facilities from this bank within the Tehran province. To this end, the financial statements of 76 applicant manufacturing firms were analyzed. An initial set of fourteen potentially influential indicators on credit risk was examined. Subsequently, through expert consultation, eight key indicators were selected for evaluation within a DEA model assuming constant returns to scale, utilizing GAMS software. The findings reveal that out of the 76 firms assessed, 9 were identified as efficient, while the remaining 67 were classified as inefficient. The DEA method provides one or a combination of efficient firms as reference points and benchmarks for the inefficient units. The Anderson-Peterson model was applied for the ranking of the efficient units.