AI in Finance? Accountability, Solved.

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"When AI gets it wrong, who's accountable?"

This question comes up in every discussion about AI adoption at financial institutions.

It’s a fair question, and a good one.

But the question already has a clear answer.

Once you know it, there are fewer reasons to hesitate about adopting AI, not more.

Regulators have already opened the door to AI

Source: Forbes

The answer is simple: AI supports the judgment; people make the final decision.

Korean regulation is built on exactly this structure, and actively encourages AI adoption on that basis.

The Financial Services Commission released its revised AI Guidelines for the Financial Sector

under the subtitle “Responsible Innovation: Accelerating AI Transformation in Finance.”

The guidelines’ “supplementary-tool” principle treats AI as a tool that supports work,

with final decisions left to employees. Because accountability is defined from the outset,

institutions know exactly what they need to uphold when using AI.

The AI Basic Act places additional obligations on “high-impact AI” in 10 sectors, including finance. However, AI is excluded from that category when a human is involved in the final decision.

In other words, when AI analyzes and people decide, the regulatory burden drops significantly. Institutions can simply add AI to the way they already work.

AI assists. People decide.

Good financial AI never says “buy” or “sell.”

It simply organizes the information needed for a decision, quickly and accurately.

In this structure, people keep their authority.

In fact, they gain more time and information to make their decisions.

With AI handling data collection and first-pass analysis,

the same team can review far more information without missing what matters.

Decisions get better, and fewer risks slip through.

A clear record of the basis for every decision

Source: Pinterest

What protects a financial institution is the process, not just the outcome.

LPs, auditors and regulators ultimately ask the same question:

“On what basis was this decision made, and by whom?”

Well-designed AI solves this naturally, because the way it works becomes the record itself.

1. Sources: Every answer records the materials it was based on, with links to the originals.
2. Evidence and uncertainty: Verified claims are clearly separated from uncertain ones.
3. Decision history: Who decided what, when, and based on which information is logged in

chronological order.

When an LP asks, “How was this decision made?”, you can pull up the record on the spot.

Whether the outcome is good or bad, a process you can explain protects your institution’s credibility. AI doesn’t blur accountability. It makes responsible decision-making easier to demonstrate.

How to choose a financial AI you can trust

Source: Vecteezy

That said, not every AI is fit for the job.

Check that a solution meets the criteria below, then roll it out step by step, starting with a single task.

• Does every answer show its sources, with links to the originals?
• Does it distinguish verified information from uncertain information?
• Does a person make the final decision, with no trades or actions executed by the AI itself?
• Does it log who asked what, when, and what answer they received?
• Can those records be used for audits and LP reporting?
• Is your internal data kept out of external AI training?
• Have you assessed whether your use of AI qualifies as “high-impact AI” under the AI Basic Act?
• Has leadership designated an owner and set internal policies for AI use?

Start responsible AI adoption with TREASURER Core

Source: Core by TREASURER website

Core, TREASURER’s AI transformation solution, is built on the same principles discussed here:

people decide, evidence is shown, and the process is recorded.

• People make the final call. AI output isn’t finalized until the responsible person approves it. When evidence is insufficient or signals conflict, the AI stops on its own and escalates for human review.
• No number without a source. Every result comes from a financial ontology linked to the source data, and you can verify the evidence in a single click.
• The decision process is preserved. Queries, referenced documents, reasoning paths and approval history are all recorded, so you can reconstruct any past decision during an audit.
• Your data stays inside your network. Core is deployed on-premises, including a local LLM within the client’s internal network, so there’s no risk of data leaving your environment.

Leading financial institutions including NH Investment & Securities, Mirae Asset Securities, Shinhan Venture Investment, Hanwha Investment & Securities and The Bell, already work with TREASURER.

The question is no longer “Can we use AI?” but “Where do we start?”.

Start your responsible AI adoption with Core.