How to use artificial intelligence in finance?

Google Cloud launches an artificial intelligence solution for finance. Here’s how to use it without exposing customer data or weakening compliance. Imagine a credit analyst at a bank in Libreville needing to prepare a report on a major corporate client in just a few hours. An artificial intelligence (AI) assistant can help them find figures, compare reports, and summarize a large file. The time gain is…

31 August 2026

Imagine a credit analyst at a bank in Libreville needing to prepare a report on a major corporate client in just a few hours. An artificial intelligence (AI) assistant can help them find figures, compare reports, and summarize a large file.

The time gain is real. But one question remains: who verifies the sources, assumptions, and confidential data used? In finance, a quick answer is not enough. It must be accurate, traceable, and compliant with regulations.

What is it, concretely?

Gemini Enterprise for Financial Services is an AI solution designed for financial professions, not just a general conversation tool. It can be connected to the documents, databases, and applications used by analysts, credit teams, compliance services, or market professionals. Specifically, software assistants can search for information, summarize a report, extract figures from a file, or prepare a first draft of a note. The main challenge is trust: in banking, insurance, or asset management, a mistake can have financial, legal, or reputational consequences. The good news is that AI can be useful if it remains supervised by humans and governed by precise rules.

Concrete case: what to do and what not to do

Let’s take Deutsche Bank, an international bank based in Frankfurt. In a pedagogical case inspired by current events, the institution indicated that it participated as a design partner for the Financial Research Agent, a financial research assistant integrated into Google Cloud's solution.

A team of analysts prepares notes for corporate clients or investors. They need to quickly cross-reference market data, sector reports, internal documents, and sometimes confidential information. The temptation would be to let AI directly produce a credit or investment note. What is lacking, if one moves too quickly: human validation, verifiable sources, access rights, data retention, and procedures in case of error.

What can be done

Limit AI access to only the necessary documents according to each user’s role.
Request a clear trace of the sources used for each important figure.
Maintain human validation before any credit, investment, or compliance decision.
Start with simple files before handling sensitive client cases.
Note any errors identified to improve internal control.

What not to do

Copy-paste sensitive client data into an unapproved tool.
Transform an AI response into a final decision without professional review.
Assume that a finance-specialized AI never makes mistakes.
Forget the cybersecurity risks reported by several financial regulators.
Deploy the tool without involving compliance, risk, legal, business, and IT management.

Questions to Ask Before Acting

Are the data entrusted to the AI authorized, accurate, and up to date?
Who can see what, and are access rights appropriate to each person's responsibilities?
Does the tool indicate the sources used to produce an analysis or summary?
Is an identified person validating the result before an important decision?
What procedure should be followed if the AI produces a false, biased, or non-compliant response?
Do the data leave the country or the relevant regulatory space?
Does the contract with the supplier provide for audit, security, confidentiality, and data deletion?

UNIVGA Viewpoint

For Gabon and Francophone Africa, financial AI should not be seen as just a productivity booster. It touches on banking secrecy, personal data, client consent, cybersecurity, and decision-making accountability. In a financial institution, a poorly prepared credit note or inadequately protected client data can cost much more than the time saved.

The Gabonese framework deserves special attention. The protection of personal data is governed by law n°001/2011, amended notably by law n°025/2023, under the oversight of the Authority for the Protection of Personal Data and Privacy (APDPVP). As soon as a bank uses cloud services, that is, remote-hosted computing, the issue of data transfer and storage becomes central.

UNIVGA's position is clear: train professionals, not just IT specialists. Students in banking, finance, insurance, auditing, or asset management must know how to use these tools, but also ask the right questions. An AI can accelerate an analysis. It does not replace professional judgment, client knowledge, or internal control.

Sources

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What is it, concretely?

Concrete case: what to do and what not to do

Questions to Ask Before Acting

UNIVGA Viewpoint

Sources

Can business software steal your sensitive data?

Can AI enhance the reliability of accident investigations?

Digital cadastre: how to avoid land errors?

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AI at work: how to succeed without disorganizing?

AI in business: who really keeps your data?

Excel with AI: how to maintain reliable results?

Master of Science (MSc) in Banking, Finance, and Insurance

What is it, concretely?

Concrete case: what to do and what not to do

Questions to Ask Before Acting

UNIVGA Viewpoint

Sources

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