Edraleen P. Gonzales and Maria Kristina M. Cay (Authors)
Abstract
Credit scoring has become a crucial tool for modern financial risk assessment, affecting loan choices at both conventional and non-bank financial institutions (NBFIs). This study looks at the connection between credit scoring and risk intelligence in Laguna Province’s non-bank financial institutions (NBFIs). This study uses the FICO model to examine key aspects of credit scoring, such as payment history, amount due, duration of credit history, new credit, and credit mix. This study examines how these elements relate to risk intelligence, which encompasses how much risk a person is willing to accept, how they perceive hazards, what they learn, how they adapt, ambiguity, decision-making quality, and risk prediction accuracy. Statistical approaches such as mean frequency, standard deviation, and Pearson’s Correlation Coefficient were used to study the relationship between credit rating and risk intelligence. Data were obtained from 114 respondents, including credit assessors from several NBFIs, using a standardized survey questionnaire as the major study tool. The research used purposive sampling to ensure that participants had appropriate knowledge and operational experience in credit risk assessment inside NBFIs. The findings show a substantial correlation between credit score components and risk intelligence, implying that thorough credit evaluations might help NBFIs make better choices and become more resilient. This research provides empirical data that supports improving credit assessment systems, with important consequences for credit evaluators, financial institutions, regulators, and borrowers.
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Keywords: Credit Scoring, Risk Intelligence, FICO Model, Non-Bank Financial Institutions, Credit Score