AI-powered decision-making instruments are cutting-edge technology that has the potential to displace conventional banking procedures. This chapter emphasizes the critical role artificial intelligence (AI) has played in guiding the banking industry toward expansion. AI techniques including robotics, deep learning, facial recognition, natural language processing, and more are used to achieve this goal. This chapter provides an overview of the use of AI approaches in several banking functional domains, such as loan approval, customer lifecycle management, customer services, alarm systems, and so on. It also highlights the benefits and difficulties that AI-driven financial apps provide. In summary, artificial intelligence (AI) has enormous promise in banking, but it also confronts several obstacles that, if correctly recognized and overcome, might broaden its use. This chapter is an invaluable tool for researchers, lawmakers, and bank officials who want to learn more about the unrealized potential of artificial intelligence in banking.
TopIntroduction
The banking industry is vital to every individual's existence. A man can't even sleep without making a single transaction with the advent of digital banking. People rely on banking services to carry out all of their daily financial transactions. People are forced to use banking services through financial inclusion and technological transformation. Customers who are tech-savvy and regularly interact with sophisticated technologies anticipate frictionless banking services. To fulfill these demands, banks have branched out into the retail, Information Technology (IT), and telecom sectors to offer services like real-time money transfers, e-banking, and mobile banking (www.deloitte.com). AI techniques were applied in the banking sectors to identify various banking frauds, risk modeling, solve customer queries online, and so on.
The use of artificial intelligence in banking will be crucial in the future since it will enable sophisticated data analytics to prevent fraudulent transactions and enhance compliance. An AI program completes anti-money laundering tasks that would often take hours or days in a matter of seconds. Banks can now handle enormous amounts of data at lightning speed thanks to AI and gain insightful knowledge from it. Digital payment advisors, biometric fraud detection systems, and AI bots are features that improve service quality and reach a larger clientele. All of this results in higher sales, lower expenses, and higher profits (Kaya et.al 2019).
This chapter is structured into five parts. The initial section covers the objectives and methodology. The second part delves into AI techniques applied in the banking sector. The third part explores AI techniques as they relate to specific functional aspects of the banking sector. The fourth part addresses both the advantages and hurdles associated with AI in this field. Lastly, the fifth part encompasses case studies, success stories, future trends, and concluding remarks regarding the role of AI in the banking sector.
This chapter comes out with the following objectives. They are.
- 1.
The first objective of this study is to understand AI tools in banking such as machine learning, predictive analytics, voice recognition, natural language techniques, and chatbots
- 2.
The second objective covered the AI applications in various functional areas of banks such as customer lifecycle, loan approval, customer services, alarming systems, cyber security fraud detection, credit risk assessment/credit scoring, management of bad debts, and automation.
- 3.
Benefits, challenges, case studies, and success stories of AI in banking are presented in the third objective.
TopMethodology
This book chapter is purely based on secondary data from published articles, case studies, government reports, etc. Secondary data was covered with a focus on AI techniques such as Machine Learning, Chatbots, Deep Learning, Robotics, Voice recognition, Facial recognition, Predictive Analytics, and Natural Language Processing. Also, AI applied functional areas such as customer lifecycle, loan approval, customer services, alarming systems, cyber security fraud detection, credit risk assessment/credit scoring, management of bad debts, and automation. Finally, the benefits and challenges of AI in banking are presented in addition to case studies and success stories.