E.SUN Bank is working with IBM to create clearer AI governance rules for how artificial intelligence can be used within the bank. This initiative reflects broader changes in finance. Many companies are already using AI for fraud checking and credit scoring, and some are even using it to handle customer service inquiries. The new challenge is how to manage these systems in a way that meets legal and risk rules.
Banks are facing a growing number of questions when implementing AI. How should I test my model before publishing it? Who will take responsibility if I receive a wrong call? And how can companies prove to regulators that their systems are fair and secure?
To address these issues, E.SUN Bank and IBM Consulting created an AI governance framework for banks. The project also includes an AI governance whitepaper that explains how financial companies can build internal controls around AI systems. According to a press release from both companies, the initiative aligns with global standards such as EU AI legislation and ISO/IEC 42001 for financial services.
The framework sets out how banks will review AI models before they are deployed. We also discuss how these models should be monitored once they are in production. This includes rules on how data is used and how risk reviews are conducted.
E.SUN Bank said the framework aims to help financial institutions implement AI systems while maintaining governance and regulatory oversight. Many companies already run limited AI tools. The next step is to extend these systems across core operations such as lending and payments, while staying within regulatory boundaries.
Banks seek to manage AI risks
Financial companies have strong reasons to put guardrails around AI systems. Banking relies on trust, and regulators require companies to track how decisions are made. AI models often function as “black boxes.” This means it can be difficult to explain how an AI model arrives at its results. This can lead to problems in areas such as credit decisions and fraud checks. Regulators in many regions are beginning to focus on these risks.
The European Union’s AI Law, adopted in 2024, sets out strict rules for AI systems used in high-risk sectors such as finance. The law requires companies to assess risk and document training data. You also need to monitor how your AI model behaves after deployment.
Global standards are also taking shape. ISO/IEC 42001, published in 2023, sets out how organizations should build management systems for AI. This standard focuses on monitoring and model monitoring. We also address how organizations can manage their AI data. The goal is to give companies a structured way to manage AI across the enterprise, rather than treating each model as a separate tool.
E.SUN Bank and IBM projects utilize both frameworks. This is intended to show how these rules work in everyday banking.
From AI pilots to enterprise systems
Banks have been using machine learning for years, primarily for risk analysis and fraud detection. New AI models are expanding how banks use technology. Many people are now applying it to customer service and document reviews. Some people use it in their internal knowledge systems.
This expansion brings new governance needs. A system that suggests answers to customer questions may seem low risk. But models that help approve loans or detect fraud can have a direct financial impact.
A governance framework created by E.SUN Bank and IBM establishes a process for tracking these risks. The model is reviewed before going live, and the team monitors the output after deployment. This framework assigns responsibilities across teams, from developers to compliance officers. This project also produced a white paper that explains the steps in more detail. It outlines how banks can categorize AI systems by risk level and apply different levels of oversight.
AI governance expands across financial services
E.SUN Bank’s efforts reflect trends across global finance. Many banks are now considering governance as a key step before extending AI across their operations.
Industry research shows that the adoption of AI in financial services is already widespread. NVIDIA’s 2024 report found that approximately 91% of financial services companies are evaluating or already using AI. Common uses include fraud detection and risk modeling. Some banks are using AI to automate customer service operations.
According to Deloitte research, more than 70% of financial institutions plan to increase their investments in AI. Much of that spending is aimed at compliance monitoring and risk analysis. Some banks hope that AI will improve internal operations.
At the same time, regulators are also paying close attention. Authorities in several regions have warned banks to track how automated systems impact decisions such as credit approval and fraud detection. This pressure has led banks to increase investment in internal monitoring systems. Instead of focusing only on model accuracy, companies now also track data sources and decision logic. We often monitor how a model behaves over time.
Why governance shapes AI adoption
The push for AI governance could impact the speed at which banks adopt new tools. Without clear rules, many companies are hesitant to move beyond small-scale experiments. A structured framework can help you scale your AI projects while meeting regulatory demands.
That’s the idea behind the E.SUN Bank project. By combining global standards and banking workflows, the framework sets out how AI can be deployed with clear oversight. In a statement from the companies, IBM said the framework was developed to help financial institutions manage AI risks as they expand the use of AI in banking.
This effort also reflects the growing role of governance in enterprise AI. Early AI projects focused on building models and improving performance. The focus has now shifted to how to manage these systems over the long term. As more banks implement AI into their core operations, this issue could become as important as the technology itself.
(Photo courtesy of Marcus Spike)
See also: Manulife moves AI agents into core financial workflows
Want to learn more about AI and big data from industry leaders? Check out the AI & Big Data Expos in Amsterdam, California, and London. This comprehensive event is part of TechEx and co-located with other major technology events. Click here for more information.
AI News is brought to you by TechForge Media. Learn about other upcoming enterprise technology events and webinars.
