7 steps to implement AI in the financial sector

Artificial intelligence is already among the main forces driving business transformation globally. According to the Future of Jobs Report 2025 According to the World Economic Forum, the 86% survey of employers predicts that advances in AI and data processing will transform their organizations by 2030. One of the areas where this is having the greatest impact is the financial sector. For this reason Excelia, a Spanish consulting, technology and professional services firm, identifies in its A practical guide to implementing AI in the financial sector What are the 7 key steps that companies should follow before investing in the implementation of AI elements in the financial area in a secure, scalable way and with a real impact on the business?

1. Start with low-risk internal cases: The first step should be to identify internal processes where AI can add value with controlled risk. Automating reporting, analyzing variances, generating financial summaries, classifying documents, or preparing committee presentations can be good starting points.
2. Define clear limits of autonomy: Not all financial tasks require the same level of automation. AI can generate an initial draft of a financial report or detect a significant discrepancy, but final validations and decisions with economic impact should remain in the hands of the responsible team.
3. Use role-based access control and full traceability: Finance works with particularly sensitive information, so any AI solution must respect permissions, roles, and access levels. Furthermore, every query, recommendation, modification, or automation must be logged to facilitate audits and prevent AI from becoming a black box.
4. Maintain human oversight in sensitive decisions: AI can help analyze, prioritize, summarize, detect errors, or recommend actions, but relevant financial decisions must remain under human oversight. This applies to payment approvals, accounting adjustments, official forecasts, external reporting, financing decisions, credit risk, and regulatory compliance.
5. Select the most appropriate technology for each use case: Before implementing a tool, it's advisable to assess whether the need requires automation, predictive analytics, generative AI, assistants, agents, or capabilities already integrated into existing platforms. The decision should be based on criteria such as integration, security, traceability, scalability, and the actual ability to solve a specific problem.
6. Prepare data, APIs, architecture, and organizational culture before scaling: Financial AI depends directly on data quality. If the information is incomplete, duplicated, misclassified, or scattered across different systems, the results will be unreliable. Before scaling, it's essential to review data sources, integrations, permissions, governance rules, and prepare teams to correctly interpret and use the technology.
7. Prevent informal automation from growing unchecked: One of the most common risks is that different teams start creating automations, macros, assistants, or AI models separately, without a common framework. To avoid duplication, errors, unauthorized access, or lack of traceability, Finance must work with clear responsibilities, validation criteria, security policies, and impact monitoring.

“Applying AI in Finance is not about automating for the sake of automation, but about identifying where it can generate a real impact: improving forecasting, accelerating closings, detecting deviations, avoiding human errors, strengthening control, and freeing up the finance team's time for higher-value tasks.”, points out Antonio Cerdán, Hyperautomation Managing Director at Excelia, which adds: “The financial sector works with particularly sensitive information, so any project must proceed with clear criteria for security, traceability, human oversight, and data governance. The key is to start with specific cases, measure results, and scale gradually.”.

Excelia supports organizations in the implementation of Artificial Intelligence applied to the financial area through the identification of use cases, data strategy and governance, intelligent process automation, advanced analytics, predictive models, generative AI, intelligent assistants, AI security, training and development of customized solutions.