Artificial intelligence-driven sustainable climate finance decision-making in Ghana’s financial sector

Abstract While climate finance mobilisation is accelerating in developing economies, practitioners in Sub-Saharan Africa have underutilized the potential of artificial intelligence to improve climate-related financial decisions, as this capability is not yet fully explored and does not exist in many developing economies. This study aims to understand the factors that shape the behavioural intention of financial professionals to use AI in climate finance decision-making by augmenting the Technology Acceptance Model with the constructs of trust in AI and institutional readiness. The study used a cross-sectional and hypothesis testing survey approach with 317 respondents from banking, insurance, asset management and development finance institutions in Ghana, whose data were analysed through partial least squares structural equation modelling in SmartPLS 4.0. The results indicate that perceived usefulness and trust in AI are the most significant factors influencing adoption intention, with perceived ease of use having indirect rather than direct effects through perceived usefulness. The relationship between ease of use and adoption is significantly mediated by institutional readiness, whereas the relationship between usefulness and adoption is not, suggesting that ease of use must be combined with institutional readiness to generate adoption commitment. The most significant barriers are inadequate AI infrastructure, a lack of technical expertise, and regulatory uncertainty. The study recommends that to mitigate regulatory uncertainty, the financial regulators in Ghana should develop sector-specific AI governance frameworks for climate-related applications; the financial institutions should invest in targeted AI literacy programmes aimed at climate finance teams; and the policymakers should provide support for the development of the climate data infrastructure through public-private partnerships to address the identified institutional readiness gaps.