Finance
Bank 5.0: Agent-Based AI in Financial Transactions
Financial institutions have been under the same pressure to adopt AI as other sectors, and as a result, they are rushing to invest in advanced solutions without first reorganizing their own infrastructure.
The problem is that, when the database is not properly prepared, implementing sophisticated algorithms in disorganized repositories or fragmented systems drastically reduces the return on investment. In other words, the lack of a solid data foundation makes it difficult—if not impossible—to realize the technology’s full potential.
The lack of a robust architecture capable of supporting new AI-driven acceleration models and high request volumes—a basic requirement in the financial sector—exposes operations to serious vulnerabilities. Even a single minute of transaction downtime compromises financial margins and has a direct impact on the customer experience.
Particularly in the financial sector, there is zero tolerance for downtime. In addition to revenue losses, these operational failures and security breaches result in severe regulatory penalties and undermine the business’s long-term sustainability.
The path to overcoming the technology gap involves an organized transition from traditional infrastructure, moving from code written in legacy languages such as COBOL to the inclusion of autonomous agents in some cases.
Equifax Boavista, for example, enlisted Inmetrics’ help to migrate its consolidated databases to cloud environments without interrupting ongoing transactions. This modernization project demonstrates that artificial intelligence can serve as a strategic enabler in the restructuring of core banking systems, combining advanced software engineering with well-defined business objectives.
In the webinar “Finance 5.0: From COBOL to AI Agents,” we analyzed the key indicators that signal the need to modernize systems:
Many industry leaders overestimate the immediate pace of adoption of automated tools and end up giving in to pressure to implement AI without first organizing their data or establishing a well-defined goal regarding expected results. At the same time, the ongoing effort required for the preventive maintenance of complex legacy systems and the human resources needed to understand the key aspects of migrating to new language models is underestimated.
The introduction of autonomous agents requires a range of technical skills and calls for professionals who are focused on understanding the business and mastering integrated methodologies.
A successful transition to the current financial ecosystem—in which participants can drive efficiency gains at various stages and across various sectors of the operation—depends on clear criteria for measuring the actual results achieved through automation.
Changing the operating model of traditional banks requires secure environments that ensure compliance with current regulations and safeguard privacy. The ROI is positive when algorithms are able to reduce operational errors and take over repetitive workflows, allowing the human team to focus on strategic areas such as improving the user experience.
There are also other criteria for determining whether AI has been successful in generating results for the operation:
Although intelligent agents are capable of making decisions based on rich contexts, they should not have absolute autonomy.
People play a central role in active governance and are indispensable for the initial configuration and ongoing monitoring of workflows. It is this expert oversight that prevents unintended deviations from established functions and ensures that corporate boundaries are upheld.
As we mentioned earlier, the financial sector depends on stability and trust. Therefore, a proactive approach to compliance is essential to maintaining a sustainable operation.
Waiting for a problem to arise before seeking a solution can undermine the entire business structure and destroy the company’s market value. Customer trust is lost, and profit margins are eroded in the process.
An AI-Engineered approach provides real-time monitoring and end-to-end observability, enabling the identification of anomalies long before any noticeable impact on the end-user experience.
If you’re interested in learning more about this topic, the webinar “Finance 5.0: From COBOL to AI Agents” is now available. In it, we discuss Equifax Boavista’s success story in modernizing its operational systems and how AI played a key role in that process. It’s definitely worth checking out!
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