---
title: "AI for Financial Services | Auditable, Permission-controlled Enterprise AI - AI GO"
description: "Adopt AI under heavy regulation: complete access and change logs, field-level permission control and traceable grounds for AI replies. AI GO provides financial management, smart accounting, NewebPay and service ticketing modules."
keywords: "financial services AI, compliance, audit trail, access control, fintech, AI GO"
canonical: "https://www.ai-go.app/en-US/solutions/financial-services"
locale: "en-US"
source: "AI GO（https://www.ai-go.app）"
---

# AI for Financial Services

It is not that you do not want AI — it is that you cannot use it without a record. AI GO logs every access, every change and every AI reply, so adopting AI and satisfying audit stop being competing goals.

- **Full trail** access and changes are traceable
- **Field-level** layered control over data
- **Verifiable** AI replies carry checkable grounds

## 現況痛點

### AI projects stall at compliance

The business wants it; compliance asks where the data goes, who can see it and how you investigate an incident. No answer means the project goes back, and it sits in evaluation for a year.

### Producing audit evidence takes two weeks

Records live across different systems, so every audit becomes a company-wide effort — and what comes out still has to be checked by hand for completeness.

### Service cannot be automated safely

Nobody will let AI answer anything touching amounts, contracts or entitlements. But human replies are slow, and satisfaction never improves.

### Skilled people doing repetitive work

Reconciliation, issuance and case entry consume specialist time, squeezing out the work that actually requires judgement.

## 導入前後對照

**Before**

- AI projects parked in evaluation over compliance concerns
- Audit records spread across multiple systems
- Every customer question answered by a person
- Reconciliation and case entry consume specialist time
- Access control lives in policy rather than the system

**With AI GO**

- Data flow and access scoped at the system layer
- Audit records retrievable directly by period
- AI handles factual questions; sensitive ones route to people
- Repetitive work automated, specialists left to judge
- Permissions written into the system, not remembered

## AI GO 對應能力

Finance and accounting form the core; service ticketing and judgment search can be added depending on your business.

### Permissions and audit are platform capabilities

Who can see which field, who changed what and who exported what are built into the platform rather than reimplemented per system. Auditors retrieve rather than reconstruct.

### You draw the AI’s boundary

Mark topics AI must never answer and topics that must route to a person. AI answers from the knowledge base you build, with grounds that can be traced — not an unbounded black box.

### Data stays where you can account for it

AI features use your own OpenAI key with the knowledge base in your account. Deployment shape can be discussed against your security requirements — there is no single mandated model.

### Reconciliation and payments automated

Financial management, smart accounting and NewebPay modules automate routine reconciliation and collection, returning specialist time to work that needs judgement.

## ROI 試算

For a mid-sized financial services firm with a five-person back office at an average loaded rate of NT$550/hour:

| 作業項目 | 每月人工工時 | 時薪 | 每月可省 |
| --- | --- | --- | --- |
| Routine reconciliation and variance checks | 50 | NT$550 | NT$27,500 |
| Retrieving and assembling audit evidence | 24 | NT$650 | NT$15,600 | <!-- Requires senior staff -->
| Answering factual customer enquiries | 40 | NT$450 | NT$18,000 |
| Case entry and document filing | 32 | NT$450 | NT$14,400 |

## 常見問題

### Can the system meet our regulator’s requirements?

We cannot judge compliance on your behalf — that is a determination for your compliance function. What the system provides is verifiable fact: complete access and change records, field-level permission control, and AI replies whose grounds can be traced. In practice those are exactly what compliance teams ask for during evaluation. Involve compliance from the start.

### Can customer data stay inside our environment?

Deployment shape is open to discussion rather than fixed. AI features run on your own OpenAI key with the knowledge base in your account, so ownership of that data is unambiguous. Raise stricter isolation requirements during consultation and we will propose against them.

### Is it really safe to let AI reply to customers?

Start with factual questions — opening hours, required documents, status enquiries — where a standard answer exists. Anything touching amounts, entitlements or contract interpretation should be set to route to a person. You draw the boundary; the system does not default to fully open.

### How long should the verification period be?

For this sector we recommend a longer parallel run: keep the existing process while the new system runs alongside for comparison, and only switch when both figures and records agree. The length follows your internal control requirements — the Forward Deployed Engineer works to that rather than pushing a schedule.