COMPARATIVE ANALYSIS OF AI- DRIVEN AND TRADITIONAL FINANCIAL CREDIT RISK MODEL IN REAL ESTATE SUPPLY CHAINS

Abstract: The assessment of credit risk in the real estate supply chain is an essential part of financial risk management that influences investment decisions, financial stability, and the health of the overall real estate segment. Traditional financial credit risk models have long been used for the assessment of borrower credibility and potential default prediction with historical financial data, credit score, and some various financial ratios, while other methods could complement this approach. Although these conventional approaches have some merit, they frequently fail in capturing real-time market fluctuations, new emerging risks, and complex interdependencies that build creditworthiness. The introduction of artificial intelligence (AI) and machine-learning technologies has planted the seeds of change in the credit risk analysis horizon. AI-based models have given way to advanced analytical techniques that use big data, predictive analytics, and real-time insights to assess risk dynamically and more accurately. This particular paper gives a thorough comparison between the AI-driven and the traditional financial credit risk models alongside their methodologies and performance on prediction, adaptability, and limitation. Credit risk assessment is AI-driven because it utilizes machine learning algorithms to process both structured and unstructured data of large sizes to identify so-called hidden behaviours that conventional models are not able to detect. Real-time market conditions as well as transaction behaviours and macroeconomic indicators are incorporated in AI risk models to improve accuracy and timeliness of risk evaluation. Such models also help financial institutions, lenders, and investors of the real estate sector in decision-making, thus reducing possible financial losses and improving total risk management strategies. On the contrary, traditional models remain relevant since they are regulatory-compliant, transparent, and rely on well-documented financial indicators. They might be slower in reacting to changing market conditions, yet they maintain an aspect of interpretability that is usually absent in AI models. The regulatory authorities and financial institutions are sceptical of the black box of AI models within which lies the accountability, ethical considerations, and potential biases woven into machine-learning algorithms. Data privacy issues and regulatory frameworks concerning AI adoption in financial risk assessment remain reverse challenges that require immediate attention. By systematically comparing AI techniques with the classic credit risk models, the study delineates some of the parameters of distinction, including accuracy, scalability, cost-effectiveness, and applicability in the real world for the real estate sector. Two comparison tables depict the efficiency and application of the two approaches, along with usefulness in contrasting their efficacy. The results, though, suggest that AI-based credit risk models possess superior predictive accuracy, adaptability, and risk mitigation when weighed against traditional methods; yet, those features need to be balanced against regulatory oversight and ethical viewpoints to allow for successful implementation. Ultimately, the aforementioned study shows that innovation and regulatory compliance should be seen as two sides of the same coin for credit risk evaluation. The application of AI for the financial risk evaluation process reconstructively resembles giving an identity to the rehabilitation of the entire real estate supply chain by making decision-making more proactive and also helping in mitigating defaults. However, the transition phase from conventional models to AI-driven models needs a holistic understanding of both these approaches, along with their relative pros and cons. With an active evolution of AI technologies, future works may focus on developing transparent, non-biased, and interpretable AI systems that comply with available industry regulations and ethical principles, so that their adoption in real estate credit risk management can be considered responsible. Keywords: Risk of Credit, Supply Chain in the Real Estate sector, Financial Stability, Conventional Templates of Credit, Models for Credit Powered by AI, Machine Learning, Big Data Analytics, Predictive Analytics, Risk Evaluation Recurrently, Default Risk Mitigation, Decision making in Investments, Credit Scoring, Financial Ratios, Risk Management Strategies, Efficiency of Models, Ethics in AI, Regulatory Compliance.