Automating Financial Decision-Making in Renewable Energy: Leveraging AI and Credit Risk Models for Sustainable Investment

Aim: This study investigates the impact of financial automation, artificial intelligence (AI) credit risk models, and predictive analytics on renewable energy investment choice in the United States. It investigates how automation optimizes capital allocation, mitigates investment risk, and enhances financing structures for clean energy projects. Study Design: A systematic peer review of the literature from 2019-2025 on the application of AI in financial decision-making, credit risk modeling, and renewable energy investments, including blockchain technology. Case studies from financial institutions and renewable energy firms using AI-driven risk assessments are included. Methodology: The research gathers articles from academic databases such as Google Scholar, Scopus, SSRN, and Business Source Complete. Some of the selected articles focus on AI in financial automation, credit risk evaluation in renewable energy, and investment patterns in clean energy ventures. Results: The review references various studies demonstrating how financial automation using AI enhances risk evaluation, reduces rates of project failure, and enhances access to sustainable capital for investment. AI-based credit risk models ease the distribution of capital, allowing small and medium-sized businesses (SMEs) to access financing for clean energy initiatives. Predictive analytics in financial decision-making considerably enhances risk evaluation and portfolio diversification. Blockchain application further strengthens transactional transparency and reduces fraud risks in renewable energy financing. Conclusions: AI and financial automation present transformative opportunities for sustainable energy financing through improved investment efficiency and diminished credit risks. However, data reliability and algorithmic biases are challenges that must be addressed to realize their maximum potential. Future research should examine regulatory frameworks and ethical considerations to ensure responsible implementation of AI-driven financial automation.