Abstract:
This research proposes a novel approach to software effort estimation in the Fintech domain by
leveraging deep learning and hybrid machine learning techniques. Traditional estimation methods
such as COCOMO and Function Point Analysis struggle with the complexity and dynamic nature
of modern software projects. To address these limitations, this study develops a deep learning -
based model that compares traditional machine learning algorithms (e.g. Random Forest,
XGBoost) with deep learning architectures (e.g. LSTM, MLP) using a proprietary dataset.
The methodology involves feature-level fusion and model stacking techniques to enhance
predictive accuracy. The dataset undergoes data augmentation, normalization, and encoding. The
models are trained and optimized through hyperparameter tuning and evaluated based on key
metrics such as RMSE, R-squared, and MAPE. The system is designed to improve estimation
accuracy, scalability, and reliability while providing a user-friendly interface for project managers
and pre-sales teams.
The results demonstrate that the hybrid model significantly outperforms standalone models. The
feature-level fusion approach achieves the highest accuracy, reducing the MSE and MAPE
compared to traditional models. The findings validate the effectiveness of combining deep learning
with machine learning for effort estimation, making it a promising tool for project planning,
resource allocation, and decision-making in FinTech software development.
