Abstract

Accurate cost estimation and forecasting are critical for effective decision-making in the banking sector. This study evaluates the performance of machine learning algorithms, including Linear Regression, Ridge Regression, Random Forest, Gradient Boosting Machine (GBM), and Long Short-Term Memory (LSTM) networks, for cost prediction using a robust dataset comprising operational, transactional, and macroeconomic features. Our results demonstrate that while simpler models like Linear and Ridge Regression offer computational efficiency, their predictive accuracy is limited in handling complex data. Tree-based methods, particularly Random Forest and GBM, significantly enhance performance by capturing intricate patterns, albeit at a higher computational cost. The LSTM network outperformed all models, achieving the highest R² score and the lowest MAE and MSE values, highlighting its superiority in capturing temporal dependencies. This research provides actionable insights for banking institutions, emphasizing the trade-offs between accuracy, efficiency, and model complexity. The findings pave the way for optimized ML adoption in financial forecasting, enhancing operational resilience and strategic planning.

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