Enhancing Valuation Methodologies for Appraisal Objects in the Context of Bank Loan Underwriting: Toward More Accurate Credit Risk Assessment

Abstract

The research examines traditional bank loan underwriting valuation limitations before presenting a risk-aligned approach which improves credit risk assessment. The widespread use of cost-based and market-based and income-based valuation models shows systematic problems in risk factor integration thus leading to inaccurate valuations that hinder banking operations. There exists an essential lack of understanding about how traditional asset appraisal approaches ought to work in current credit risk modeling systems particularly during periods of market instability and non-standard asset valuation scenarios. Using a qualitative research plan this project integrates studies on traditional valuation practices with evaluations of advanced tools including AVMs MCDM and AI systems. Current valuation systems cannot adjust to changing market conditions because they need standard rules that regulators approve. The study shows that valuation systems must use data to reflect exact borrower risk patterns and unique asset movement. The suggested recommendations create implications for banking institutions and regulatory entities which want to modernize their lending procedures. Additional experimental tests of these models in different banking situations should become the focus of future investigations to help develop credit systems based on modern standards that handle behavioral risks and ESG elements.