OPTIMIZING BANKING PERFORMANCE THROUGH ARTIFICIAL INTELLIGENCE IN SELECTED BANKS IN WESTERN BARANGAY, BICUTAN, TAGUIG CITY

Aims: The study aimed to assess the extent of artificial intelligence (AI) integration in banking operations and its impact on banking performance in selected banks in Western Barangay, Bicutan, Taguig City. Specifically, it sought to evaluate how AI influences operational efficiency, strategic decision-making, risk management, customer satisfaction, and financial growth. Study design: A quantitative descriptive-correlational research design was employed. The study utilized a survey-based approach to gather data from 100 banking personnel, including managers, tellers, and IT staff, from selected banks in Western Barangay, Bicutan. The design enabled the researcher to assess the extent of AI integration and examine the relationship between AI adoption and various performance metrics. Methodology: A structured survey questionnaire was developed, comprising three sections: demographic profile, dimensions of AI integration (such as financial analytics, risk management, process automation, and customer service), and banking performance indicators (e.g., operational efficiency, strategic decision-making, risk management, customer satisfaction, and financial outcomes). Descriptive statistics and Pearson correlation analysis were used to analyze the data and determine the relationship between AI integration and banking performance. Ethical considerations, including informed consent and confidentiality, were observed throughout the study. Results: The findings revealed a moderate level of AI integration in banking operations, with an overall mean of 3.06. The highest integration was observed in AI-driven process automation (mean = 3.19), followed closely by AI-based customer service and personalization (mean = 3.17). AI-powered financial analytics and fraud detection systems showed moderate integration. The banking performance, as influenced by AI adoption, had an overall mean of 3.05. The highest performance impacts were seen in risk management (mean = 3.23) and strategic decisionmaking (mean = 3.21). However, customer satisfaction (mean = 2.54) was identified as an area requiring further development. A Pearson correlation coefficient of 0.433, with a p-value of 0.000, confirmed a moderate positive relationship between AI integration and banking performance. Conclusion: The study concluded that AI integration has positively impacted banking performance, particularly in operational efficiency, risk management, and strategic decisionmaking. However, customer satisfaction remains an area needing improvement. The study highlights that while AI adoption leads to enhanced performance across several dimensions, a more balanced approach is necessary to ensure that technological advancements translate into superior customer experiences and long-term financial growth. The significant positive relationship between AI integration and banking performance emphasizes the importance of continuing AI investments to unlock its full potential for institutional success.