In the evolving landscape of financial technology, credit scoring systems must adapt to increasingly complex data environments to ensure accurate and fair lending decisions. Traditional credit models rely heavily on structured data such as income, credit history, and debt ratios, but often fall short in assessing borrowers with limited credit footprints or non-traditional financial behaviors. This study proposes a hybrid deep neural network (DNN) model that integrates structured financial indicators with unstructured textual data—including customer service interactions, financial news, and social media sentiment—to enhance credit risk prediction. We collected and preprocessed a multimodal dataset comprising over 100,000 loan profiles, developed a bi-directional LSTM architecture for text processing, and fused it with structured data via a deep learning framework. Our model was evaluated against benchmark algorithms including logistic regression, random forest, XGBoost, and single-input DNNs. Experimental results show that the hybrid DNN significantly outperforms traditional models, achieving an accuracy of 87% and an AUC-ROC of 0.91. These findings underscore the potential of multimodal deep learning in transforming credit scoring systems, improving model precision, and expanding financial inclusion. The proposed model offers a scalable and robust framework for future credit evaluation tools in data-rich financial ecosystems.

Credit scoring, deep neural networks, unstructured data, financial risk assessment, LSTM, multimodal learning, machine learning.

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