PREDICTIVE ANALYTICS FOR CREDIT RISK ASSESSMENT: ENHANCING FAIRNESS, TRANSPARENCY, AND OVERSIGHT IN CREDIT DECISION OUTCOMES

Abstract

In today’s business activities including financial services, credit risk assessments and decisions regarding credit outcomes have been transformed from traditional to data driven after the emerging and growing popularity of artificial intelligence (AI) and machine learning (ML) in the sector finance. With the growing admirations of both AI and ML in the sector of finance, there are concerning issues related to ethics and regulations. Many questions are being raises about data fairness, accountability and transparency. The paper attempts to identify responsible AI within the framework of credit risk assessment, and the impact of the situation on the change in the algorithmic bias, data quality, and interpretability technologies using explainable XAI methods to regulate compliance among other aspects. The problem under investigation is the discrimination ability of an artificial intelligence platform, when it is trained using biased or insufficient sample data, leading to some segments of the population being unable to access loan credit. According to the socio-technical systems theory, the technical, regulatory and human components are combined in this work in a way that makes a global vision combining independent variable (algorithmic bias), (data quality), (transparency mechanisms), (compliance practices)(human oversight) and dependent variable credit decisions outcomes. Possible results are to analyze the presence of algorithmic biases, facilitate openness, create accountability mechanisms to guarantee adherence to regulations, and to encourage financial inclusion among those who participate in the credit decision process. The approach is quantitative since survey-based data from financial institutions form the primary data source, augmented with secondary data on credit decision process. Data will be analyzed in Python, using Pandas for data manipulation, NumPy for numerical computing, Scikit-learn to apply machine learning. Advanced deep learning would be done with TensorFlow or PyTorch if required. In order to test hypotheses, and inference regarding interrelationships between variables, regression and structural equation modeling (SEM) are suggested as statistical tools. The implications of the study are both theoretical and practical. In theory, it also adds to the growing literature of responsible AI in finance decision-making. More generally, it provides a window for financial institutions and regulators to develop fair, transparent and compliant AI systems that do not introduce bias but build trust. The authors indicate that future work is needed to validate this framework in different contexts, examine long-term effects of responsible AI on financial inclusion, and compare cross regional regulatory responses for globally responsible use of AI in credit risk assessment.

Keywords:

Algorithmic Bias, Data Quality, Explainable AI (XAI), Regulatory Compliance, Human Oversight, Credit Decision Outcomes, Responsible AI, Financial Inclusion