One of the clearest applications of generative AI in financial services is the automation of complex operational processes. Tools like advanced chatbots and virtual assistants now go far beyond handling real-time queries — they can proactively anticipate customer needs. For example, a digital assistant can analyse spending patterns and deliver personalized financial recommendations, significantly enhancing the value banks provide to their clients.

In 2025, generative AI will continue to drive automation in high-impact areas such as fraud detection. In Chile alone, AI accounts for 25% of the total impact in the financial sector. Machine learning algorithms are capable of scanning massive datasets in seconds, detecting anomalies that would likely go unnoticed through traditional processes. This not only protects consumers but also strengthens trust in the financial system.

Another major trend is the use of machine learning to hyper-personalize financial services. By analysing customer behavior and preferences, banks can craft highly targeted offerings for each segment. Digital platforms can, for instance, recommend products tailored to a client’s risk profile, age, lifestyle, or financial goals.

In the credit arena, machine learning is transforming risk assessment models. Rather than relying solely on historical data, modern algorithms factor in hundreds of real-time variables. This is especially crucial in emerging markets, where millions remain excluded from formal credit systems due to legacy evaluation practices that fail to reflect their current financial realities.

Generative AI is also enabling the integration of multiple payment rails into broader financial ecosystems. Today’s banking platforms are no longer siloed; thanks to AI-powered interoperability and technologies like Frame Banking™, customers can manage accounts, make payments, and track investments — all in one place. This convergence empowers banks to offer smart, integrated collection and payment alternatives through partnerships with multiple rails. AI can then dynamically identify the best option based on criteria such as cost, speed, or service level.