AI Efficiency, Automation, Modernization to Revolutionize Banking

The banking industry is adopting AI at an accelerating pace to improve operational efficiency, optimize decision making, and personalize the customer experience. In 2025, three key trends are expected to drive AI in the financial industry: increased adoption in financial services, improved operational efficiency, and modernization of data infrastructure, says Julio Campoy, Regional Vice President, Appian.

AI adoption in the financial sector has evolved from process automation to the integration of advanced models that enable greater autonomy in decision making. According to data from the Boston Consulting Group, 77% of financial institutions have already implemented AI in at least one area of their business. By 2025, according to the study, the combination of Gen AI, process automation and predictive analytics will enable banks to optimize operations and reduce operating costs.

Large banks have adopted Gen AI in recent years, says Campoy, but in 2025 its use will expand to smaller institutions thanks to the accessibility of large language models (LLM). "This will enable improved security, accuracy, and data leverage in the sector," he adds. Financial institutions are expected to integrate AI agents for autonomous decision making in critical processes. While Gen AI will facilitate the interpretation of unstructured information, robotic process automation (RPA) and intelligent document processing (IDP) will remain key for data mining and document management.

The combination of traditional AI, Gen AI, and Agentic AI, which allows machines to analyze situations and act independently, is expected to transform operational efficiency in areas such as compliance reporting and internal communications. This will automate documentation, improve process traceability, and reduce the operational burden on repetitive tasks, says Campoy.

For example, investment bank State Street Global Advisors found that the use of AI led to a 20% increase in efficiency in the client onboarding process. The company plans to integrate intelligent document processing and process mining to further optimize its operations, with a documented 30% impact on efficiency and a 50% reduction in error losses.

Finally, digital transformation in banking requires more integrated and scalable data infrastructures. The implementation of Data Fabric architectures, which facilitate the end-to-end integration of various data pipelines and cloud environments, will enable financial institutions to consolidate disparate data sources into a unified system. For example, S&P Global adopted this technology to integrate hundreds of data sources, automating more than 1,000 processes with the participation of 7,000 employees, says Campoy. This strategy enabled over 100 million transactional tasks, optimizing the generation of financial insights.

"The advancement of AI in the financial industry in 2025 will enable the automation of processes such as credit approvals, fraud detection, and compliance monitoring, reducing costs and human errors," says Campoy. “Risk management will improve with predictive models that anticipate market changes and credit risks with greater accuracy."