Integrating ESG into AI-Based Financial Prediction: A Conceptual Framework for Sustainable and Bias-Aware Modeling

This paper aims to develop a conceptual framework for integrating Environmental, Social, and Governance (ESG) factors into AI-based financial prediction models. Over the past decade, artificial intelligence has reshaped how financial analysts approach prediction tasks, from stock pricing to credit risk assessment. However, an important limitation remains. The vast majority of AI-driven models still anchor their logic in conventional financial metrics, leaving aside a category of information that markets and regulators increasingly take seriously: ESG factors. Specifically, this study proposes to embed ESG dimensions, environmental responsibility, social conduct, and governance quality, directly into AI-based financial prediction architectures. The methodology is theoretical. No dataset is tested here; instead, the framework is constructed through a critical reading of literature spanning sustainable finance, machine learning, and responsible investment scholarship. Three observations emerge from this exercise. When ESG signals are excluded, predictive models work with an incomplete picture, one that may systematically undervalue firms with strong non-financial profiles. Beyond completeness, there is a bias problem: training data that reflects historical market behavior tends to carry the blind spots of that market. ESG integration offers one pathway to partially correct this. Finally, short-term financial indicators alone struggle to capture what actually drives firm value across longer horizons. Accordingly, this conceptual framework provides a structured foundation for future empirical research and offers a starting point for researchers, investors, and policymakers seeking to develop more responsible and sustainability-oriented AI-based financial systems.