Explainable FinTech Models for Credit Scoring, Personalized Lending, and Trustworthy Financial Decision-Making

Artificial Intelligence (AI) are transforming modern financial technologies by enabling intelligent, adaptive, and transparent credit risk assessment systems. Traditional credit scoring approaches often suffer from limited interpretability, static risk evaluation, demographic bias, and inadequate utilization of alternative financial data. This study proposes an Explainable FinTech framework for credit scoring, personalized lending, and trustworthy financial decision-making using advanced deep learning and XAI methods. The proposed framework integrates attention-based, Bidirectional Long Short-Term Memory (Bi-LSTM), residual learning, probabilistic credit risk estimation, fairness-aware optimization, and explainability mechanisms including SHAP, LIME, integrated gradients, and attention explainability analysis. The framework also puts in place the recommendations for adaptive lending and the dynamic estimation of credit risk, which continuously adjusts borrower profiles as per the changing financial behaviour. A large-scale FinTech credit risk dataset has been used to undertake experimental evaluation, with a number of demographic, behavioural, transactional, and alternative financial attributes available. The proposed framework showed remarkable prediction performance with AUC-ROC score 99.12%, F1 score 92.15% and MCC score 0.9024, exceeding the baseline and ablation variants. Explainability analysis revealed that the most influential borrower features that influence risk prediction are: debt-to-income ratio, credit score, repayment history, and credit utilization ratio. The fairness-aware optimization module also enhanced the fairness of the decisions made by the classifiers across demographic groups. The findings align with the proposed framework's ability to deliver precise, adaptive, interpretable, and trustworthy AI-driven financial decision-making, ideal for next-generation FinTech applications.