AI-Driven Optimization of Banking Operations Using ML, NLP, and Advanced Techniques for Secure Data Management

AI-Driven Optimization of Banking Operations Using ML, NLP, and Advanced Techniques for Secure Data Management

Jatin Mittal1, Diksha Joshi2, Movin Naurd3 , Rashmi Sharma4, Angraj5

1 Computer Science and Engineering, Chandigarh University

2 Computer Science and Engineering, Chandigarh University

3 Computer Science and Engineering, Chandigarh University

4 Computer Science and Engineering, Chandigarh University

5 Computer Science and Engineering, Chandigarh University

Abstract - The banking sector faces challenges in operational efficiency, decision-making, and data security. Integrating advanced technologies can address these issues effectively. This study aims to enhance operational efficiency in banking by integrating artificial intelligence (AI), machine learning (ML), natural language processing (NLP), and blockchain technologies. We analyzed a comprehensive dataset of 25,341 documents using various predictive models. A segmentation model was employed to forecast deposit periods. The performance of logistic regression was evaluated and compared to that of a Naive Bayes model. The logistic regression model achieved an accuracy rate of 91.39%, outperforming the Naive Bayes model, which had an accuracy rate of 91.17%. This demonstrates the effectiveness of our approach in providing accurate predictions and insights. This research demonstrates significant advancements in banking operations through the integration of AI, NLP, and blockchain technology. The approach enhances decision-making, improves data processing efficiency, and ensures robust data security. It sets a new benchmark for future innovations in fintech, showcasing substantial performance improvements and practical applications.

Key Words: artificial intelligence, machine learning, NLP, blockchain technology, predictive modeling, operational efficiency, data security, banking innovation