Payments
April 1, 2024
AI in Retail Banking and Payments - Opportunities and Practical Use Cases
Artificial Intelligence (AI) provides enormous potential to the banking and payments industry, as evidenced by numerous studies. Notably, NVIDIA’s research indicates that more than 80% of financial services professionals report that AI contributes to cost reductions and revenue increases. A study by the European Investment Bank anticipates that the adoption of AI could lead to a 30% increase in revenue and a 25% decrease in costs. The financial services value chain presents a wide array of potential AI applications that can enhance both the back and front office operations, aiming to achieve growth and efficiency goals. Key areas of impact include customer engagement (such as improved customer service), forecasting and predictions (for example, predicting loan defaults), and advanced data and risk management. However, many companies are still in the early stages of AI adoption, facing challenges in navigating its possibilities and translating them into actionable strategies. A systematic approach is essential for evaluating, prioritizing, planning, and executing AI initiatives. Establishing an “AI playground” for iterative testing of selected use cases represents a crucial initial step towards leveraging AI. This should encompass both technical infrastructure and a robust compliance framework. This paper presents a robust framework and hands-on use cases to capture the value of AI, developed from extensive technical and managerial experience and offering a practical starting point for exploring AI’s potential.
Artificial Intelligence (AI) provides enormous potential to the banking and payments industry, as evidenced by numerous studies. Notably, NVIDIA’s research indicates that more than 80% of financial services professionals report that AI contributes to cost reductions and revenue increases. A study by the European Investment Bank anticipates that the adoption of AI could lead to a 30% increase in revenue and a 25% decrease in costs. The financial services value chain presents a wide array of potential AI applications that can enhance both the back and front office operations, aiming to achieve growth and efficiency goals. Key areas of impact include customer engagement (such as improved customer service), forecasting and predictions (for example, predicting loan defaults), and advanced data and risk management. However, many companies are still in the early stages of AI adoption, facing challenges in navigating its possibilities and translating them into actionable strategies. A systematic approach is essential for evaluating, prioritizing, planning, and executing AI initiatives. Establishing an “AI playground” for iterative testing of selected use cases represents a crucial initial step towards leveraging AI. This should encompass both technical infrastructure and a robust compliance framework. This paper presents a robust framework and hands-on use cases to capture the value of AI, developed from extensive technical and managerial experience and offering a practical starting point for exploring AI’s potential.
%401x.svg){kind=icon}
