ARTIFICIAL INTELLIGENCE IN BANKING: A COMPREHENSIVE MAPPING OF APPLICATIONS, CHALLENGES, AND STRATEGIC IMPLICATIONS

Artificial intelligence is rapidly reshaping the banking sector, creating opportunities for efficiency, innovation, and sustainability while introducing new regulations and financial stability risks. This research aims to systematically examine and categorise AI applications across the full spectrum of banking functions to assess their transformative potential and implications. The study employs a structured review methodology, combining academic literature, industry case studies, and regulatory reports, and applies thematic classification to map AI use into nine core domains of banking activity. The findings demonstrate that AI permeates all levels of banking operations. In customer-facing services, it supports personalisation, chatbots, biometric onboarding, and voice-gesture banking to enhance user experience. In risk management and compliance, AI improves fraud detection, AML/CFT monitoring, credit scoring, and regulatory reporting. Operational efficiency is advanced through robotic process automation, document processing, workflow optimisation, and predictive maintenance. AI also drives innovation in lending, enabling automated loan origination, dynamic pricing, and personalised debt collection. Cybersecurity and infrastructure benefit from AI intrusion detection, adaptive authentication, phishing prevention, and blockchain integration. Furthermore, human resources, marketing, ESG finance, and strategic management functions increasingly rely on AI for decision support, customer analytics, sustainability assessments, and scenario forecasting. Collectively, these results highlight AI’s holistic role in transforming banking while underscoring the importance of explainability, governance, and regulatory alignment to ensure trust, inclusivity, and resilience.