Because of changing borrower habits, a lack of readily available credit histories, and inaccurate or nonexistent financial data, credit risk assessment is particularly difficult in developing economies. Such restrictions make it difficult for conventional statistical and machine learning models to make the kind of reliable, interpretable decisions that are required when lives are on the line. This research presents a new method for real-time credit risk assessment called Neuro-Symbolic AI. It combines the power of neural networks for pattern identification with symbolic logic's reasoning and transparency. For feature extraction from diverse datasets (such as mobile use, transaction histories, and behavioural indicators), our model incorporates deep learning. Additionally, symbolic reasoning modules are used to encapsulate domain knowledge, lending rules, and regulatory limitations. This hybrid system can reason with some degree of ambiguity, draw logical conclusions, and adjust to fresh data as it comes in. In addition, the symbolic component makes the model more explainable, which is an important quality for financial system confidence and compliance. In places where alternative sources, such as mobile money platforms and social financial behaviours, are numerous but formal banking data is sparse, our study focusses on applying this neuro-symbolic architecture to underserved financial ecosystems. The algorithm was trained and evaluated using data from South Asian and Sub-Saharan African fintech lenders and microfinance institutions. In terms of interpretability, robustness to data sparsity, and prediction accuracy, the experimental findings show that the neuro-symbolic AI model surpasses the baselines of traditional machine learning and deep learning. The symbolic layer also enables domain experts to step in and intervene with decision logic as required and efficiently manages exceptions based on rules. It indicates in a favorable direction for building more accessible real-time financial services that can fit the particular demands of developing countries.