Developing an Intelligent Model for Assessing Bank Customers’ Credit Risk Using Classification Algorithms and Financial Behavioral Data
Keywords:
Credit risk, banking credit scoring, classification algorithms, financial behavioral data, machine learning, intelligent bankingAbstract
This study aimed to develop an intelligent model for assessing bank customers’ credit risk using classification algorithms, financial behavioral data, data governance, and explainable decision-making mechanisms. This qualitative study was conducted using thematic analysis. The participants included 21 banking experts, credit managers, risk management specialists, data analysts, banking information technology experts, and fintech professionals in Tehran. Participants were selected through purposive and snowball sampling. Data were collected through semi-structured interviews focused on the requirements, dimensions, challenges, and implementation mechanisms of intelligent credit risk assessment in banking. Interviews continued until theoretical saturation was achieved. Initial saturation occurred in the eighteenth interview, and three additional interviews were conducted to confirm the stability and adequacy of the extracted categories. All interviews were transcribed verbatim and analyzed using NVivo software through open, axial, and selective coding. The analysis resulted in five main categories: financial behavioral data infrastructure, intelligent credit classification engine, data governance and data quality, explainability and algorithmic fairness, and integration of the model into banking decision-making processes. Participants emphasized that credit risk cannot be assessed adequately through traditional financial indicators alone. Instead, an intelligent model should incorporate customers’ actual financial behavior, including payment discipline, transaction patterns, account balance volatility, repayment history, digital banking interactions, income stability, and liquidity behavior. Classification algorithms such as logistic regression, decision trees, random forests, support vector machines, and gradient boosting were considered useful when combined with interpretability tools, validation procedures, and human oversight. The proposed model suggests that intelligent credit risk assessment creates value for banks when predictive accuracy is balanced with transparency, data quality, bias control, regulatory compliance, and operational usability in credit decision-making.
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References
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