Authors: Dr. Manisha Kaushal Arora
Abstract: Financial services have changed as a result of the quick growth of digital banking, which has increased client convenience, efficiency, and accessibility. Financial frauds have become more common and sophisticated as a result of this digital revolution, which presents serious difficulties for regulators and financial institutions. By evaluating massive amounts of transactional data in real time, spotting odd trends, and more accurately forecasting possible threats than conventional rule-based systems, artificial intelligence (AI) has become a valuable tool for identifying and stopping fraudulent activity. With an emphasis on important technologies including machine learning, deep learning, natural language processing, and anomaly detection, this study investigates the significance of AI in identifying financial frauds in digital banking. Additionally, it looks at how AI-driven fraud detection systems improve customer trust, boost cybersecurity, decrease false positives, and increase operational efficiency. The report emphasises the difficulties in implementing AI despite its benefits, such as algorithmic bias, explainability, data privacy issues, regulatory compliance, and the dynamic nature of cyberthreats. The study comes to the conclusion that although AI cannot totally eradicate financial fraud, it can greatly improve fraud detection capabilities and aid in the creation of safe and resilient digital banking ecosystems when combined with strong governance frameworks, human expertise, and ongoing model improvement. For researchers, banking experts, legislators, and tech developers looking to improve fraud risk management in the digital age, the results offer insightful information.
