Artificial Intelligence and Machine Learning in Green Credit Scoring: Opportunities and Behavioral Challenges

Within the current global framework, green credit serves as a key lever for financing the sustainable transition of the economy. Thus, the nation of Morocco has considered green investment as the main cornerstone of its economic strategy. Yet, the dual requirements of financial performance and environmental impact make its assessment more complicated. Indeed, the introduction of artificial intelligence (AI) and machine learning (ML) into credit scoring offers new opportunities in terms of efficiency, accuracy, and detection of sustainability-related risk, according to the current literature. However, when it comes to the notion of “green”, these innovations are met with behavioral resistance and different perceptions within financial institutions, namely banks, where trust in algorithmic models and risk culture plays a crucial decisive role. The main aim of this research is to analyze to what extent behavioral variables impact the acceptance of AI- and ML- based scoring models specifically applied to green credit. To address this problem, the study adopts a mixed-method approach combining a quantitative survey and semi-structured interview with a total of 120 stakeholders. The results revealed that green credit scoring, unlike traditional credit scoring, relies on decision-making processes that integrate ethical values and increased sensitivity to environmental reputation risk.