AI in banking: use cases with security and regulation

What are the AI use cases in banking?

Onboarding and KYC. For example, an agent can read documents submitted by the customer, check data against authorized sources, identify gaps and prepare the file for approval. The analyst reviews cases with discrepancies instead of keying in every record.

Fraud and anti-money-laundering. Models and agents can prioritize alerts, gather transaction history and customer context and draft a first analysis. The decision to report, block or close stays with the specialist, who gains time for the cases that really matter.

Credit, service and operations. AI can organize the paperwork for a credit application, explain a request’s status to the customer, reconcile transactions and prepare recurring reports. These are high-volume tasks with known rules, where consistency makes a difference.

How can financial institutions use AI within regulation?

Financial institutions already operate under rules on risk management, cybersecurity, outsourcing of services, data protection and customer relations. The rules of the Central Bank and the CVM still apply when part of the work is done by AI. Using technology doesn’t shift the institution’s accountability.

The LGPD applies too. When a decision based solely on automated processing affects the data subject’s interests, they can request a review of that decision (art. 20). This is especially relevant in credit and onboarding, and it requires the institution to be able to explain how the decision was made.

Brazil’s AI legal framework is also under discussion. Bill PL 2.338/2023 was approved by the Senate on December 10, 2024 and, as of October 2026, remains under review in a special committee of the Chamber of Deputies, with the vote postponed until after the elections. It takes a risk-based approach, with extra obligations for high-risk systems such as impact assessments, human oversight, transparency and records. The text may change. Always confirm with the institution’s legal and compliance teams how each rule applies to your case.

Which security and governance safeguards are essential?

1 · Least-privilege access. Each agent accesses only the data and systems it needs for its task, with defined and reviewed permissions. Customer data stays inside the institution’s perimeter.

2 · Human oversight at the right points. Write down which decisions the agent can execute, which it only proposes and which always go to a person. Cases with high impact on the customer should have human review.

3 · Complete records. Every agent action and every human decision must be logged, with the data used, the rule applied and the person responsible. These records serve internal audit, external audit and any review requests.

4 · Continuous monitoring. Track decision quality, behavioral drift and incidents. Changes to rules or models should go through approval, like any other significant change.

5 · Third-party management. If the operation involves a partner, assess certifications, controls and data location, and align the contract with the regulatory requirements that apply to the institution.

Where should a bank start with AI?

The safest starting point is an internal, high-volume, rule-based process where AI prepares and a person decides. For example, alert triage, building onboarding files or reconciling transactions. These processes show quick gains and let you test governance before moving to decisions that directly affect customers.

Before the technology, map where the data lives, who decides what and where queues build up. There is no AI without context. An agent without access to customer history or internal policies produces generic answers and increases risk instead of reducing it.

It’s also important to involve risk, compliance, information security and legal from the start, not only at approval time. When these teams help define rules, approval limits and records, rollout is faster and the outcome is easier to defend before auditors and regulators.

How Semantix supports financial institutions

Semantix runs Risk and compliance, Finance and Customer service processes with AI agents and specialists, backed by more than 30 years of critical 24×7 operations and an operations center in Londrina with SOC and IOps. The company holds ISO 27001, ISO 27701, PCI-DSS and SOC 2 Type II certifications. Safetix enforces rules and permissions on every decision, with every execution traced and human approvals recorded, and what the operation learns stays with the institution.

It starts with a diagnostic that shows where data and processes hold back the operation and what that costs. In about three weeks, you get the map and the expected return. Then you choose how far Semantix goes: the platform, building with your team or running the operation, which starts as assisted operation and can move to an outcome-based contract in about 90 days.

That is how Semantix, The Enterprise AI Company, builds enterprises that learn.