AI in fintech: how to implement it safely and with good governance

Implementing AI in fintech requires integrating language models directly into your legacy system via orchestration platforms (iPaaS), ensuring that the accessed data is governed, structured, and strictly complies with the LGPD (Brazilian General Data Protection Law), eliminating the risk of "Shadow AI".

The AI ​​dilemma: innovation vs. control

The race for artificial intelligence in finance is driven by the promise of agility and a better customer experience. However, the most common mistake is treating AI as an external "plug-and-play" component.

Fintechs operate with complex legacy systems and massive volumes of sensitive data. When an isolated team uses public AI tools (the "Shadow AI" phenomenon) to process PII (Personally Identifiable Information), the company is not only innovating: it is creating an imminent compliance vulnerability before the Central Bank and the LGPD (Brazilian General Data Protection Law).

True innovation doesn't happen in the implementation of AI itself, but in its ability to orchestrate reliable data. If AI isn't integrated through secure APIs, it will inevitably work with outdated or "misleading" information, compromising the integrity of your financial operations.

API integration will delay our launch

The biggest leadership mistake is confusing speed with haste. Trying to implement AI without data governance generates an immediate “technical debt.” You’ll spend three times as much time fixing hallucinations, remedying data leaks, or rebuilding flows that don’t scale. Orchestration through a platform like Skyone Studio isn’t a bottleneck; it’s the foundation that allows you to scale safely from day one.

Practical example: the financial chatbot

Before: A fintech company allows its team to use an open AI tool to analyze credit histories. Without integration, the data leaves the company's secure environment and goes to the public cloud. The chatbot, not connected to the main database, "hallucinates" non-existent trading terms, causing friction with customers.

Next: the fintech adopts an orchestration layer (iPaaS). AI interacts with the legacy system via secure APIs. Sensitive data never leaves the governed infrastructure (Cloud). The chatbot only queries validated information from the database in real time. Result: accurate service, without data exposure and total compliance.

Common questions about AI in Fintech

What is "Shadow AI" in financial environments?

This occurs when employees use public AI tools (such as free versions of chatbots) to process internal company data without authorization or control from IT, creating security breaches and leaking sensitive customer data.

Can I use AI without an orchestration platform?

Technically, yes. But in practice, you create "AI islands." Without centralized orchestration, each AI project consumes resources redundantly and in isolation, drastically increasing IT operational costs and making data governance impossible.

Read also: How does governance work for AI agents in companies?

How to ensure compliance (LGPD) when using AI?

The key is governance. AI should not have direct access to the "raw database." Use middleware that anonymizes sensitive data before sending it for processing and ensures that storage is done within the infrastructure guidelines (private or hybrid cloud).

Comparison: manual integration vs. orchestration platform (Skyone Studio)

FAQ

1. Is AI in the cloud secure for financial transactions? Yes, provided the infrastructure is robust, with encryption at rest and in transit, and the architecture follows secure cloud models (such as those offered by Autosky solutions).
2. What is the role of iPaaS in AI strategy? iPaaS (Integration Platform as a Service) acts as a bridge between legacy systems (Core Banking) and new AI tools, ensuring the exchange of structured data.
3. What are "hallucinations" in financial AI? They are responses generated by AI that have no factual basis in the data provided. They mainly occur when the AI ​​is isolated from real data.
4. How does Skyone help fintechs? Through Skyone Studio, we connect legacy systems to AI applications via APIs, ensuring governance, process automation, and reduced infrastructure costs.
5. Should I migrate everything to AI at once? No. The correct strategy is "layered governance." Start by integrating low-risk flows before scaling to critical operations.
6. Can AI replace the credit analyst? Not replace them, but enhance their role. AI automates the analysis, but the final decision and data curation remain with the human, supported by a secure infrastructure.

Metrics for success in implementation

To measure whether your AI strategy is working, don't just look at the hype. Monitor:

• Integration response time: how long it takes for raw data to be processed and delivered as a secure response by AI.
• Reducing infrastructure costs: orchestration should reduce unnecessary API call redundancy.
• Error rate (Hallucinations): the number of AI responses challenged due to data inconsistency.

Technical Glossary

• iPaaS: Integration Platform as a Service. A platform that facilitates the connection between different applications, essential for orchestrating AI and legacy systems.
• Shadow AI: unauthorized or ungoverned use of AI tools by employees.
• PII: Personally Identifiable Information . Information that can identify an individual.
• RAG: Retrieval-Augmented Generation. A technique that allows AI to consult reliable data sources (its internal systems) before generating a response, increasing accuracy.
• Cloud: on-demand computing infrastructure. In a financial context, it focuses on availability, redundancy, and security (e.g., Autosky).

Written by Skyone