Designing an Intelligent Corporate Credit Rating Model Using Financial Data, Non-Financial Data, and Corporate Governance Indicators
Keywords:
Corporate credit rating, intelligent credit scoring, financial data, non-financial data, corporate governance, machine learning, thematic analysisAbstract
This study aimed to design an intelligent corporate credit rating model based on the integration of financial data, non-financial data, and corporate governance indicators from the perspective of financial, credit, and technology experts. This qualitative study was conducted using thematic analysis. The participants consisted of 18 experts in credit risk management, financial analysis, auditing, corporate governance, and financial technology in Tehran, selected through purposive and snowball sampling. Data were collected through semi-structured interviews and continued until theoretical saturation was achieved. The interviews were transcribed verbatim and analyzed using NVivo software through open coding, subtheme development, and main theme extraction. Trustworthiness was strengthened through member checking, peer review, audit trail documentation, and constant comparison of codes. Data analysis led to the identification of four main categories: integration of financial and non-financial data in credit assessment, the role of corporate governance indicators in moderating credit risk, intelligent rating architecture and algorithmic explainability, and organizational and regulatory requirements for implementation. Participants emphasized that models based solely on financial ratios cannot fully represent repayment capacity, management quality, cash flow sustainability, operational risk, and information transparency. Board independence, audit committee quality, ownership concentration, disclosure transparency, and history of commitment fulfillment were also identified as key governance indicators in corporate credit rating. The findings indicate that designing an intelligent corporate credit rating model requires a transition from traditional ratio-based approaches to a multidimensional, data-driven, and explainable framework. Such a model can support reliable credit decision-making only when predictive accuracy is accompanied by data quality, algorithmic transparency, human oversight, regulatory compliance, and interpretability for credit managers.
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