Credit decisioning, a crucial process in loan and credit approval, traditionally involves labor-intensive manual evaluations based on the Four C's of Credit Granting—character, capacity, collateral, and capital. However, advancements in big data, digital tools, and intelligent analytics are reshaping this process through automation, offering significant benefits such as faster and more objective decisions, improved approval rates for qualified borrowers, and reduced risks for financial institutions. Automated credit decisioning models streamline routine tasks, enhance efficiency, and enable lenders to leverage a broader range of data sources, including non-traditional ones like social media, to better assess creditworthiness. By integrating machine learning and artificial intelligence, these models can extract predictive credit signals, though human expertise remains essential for identifying and validating these signals. As more organizations adopt these automated processes, financial institutions can achieve greater accuracy in credit assessments, reduce costs, and enhance customer service, positioning themselves competitively against fintech companies and new market entrants.