Bank J, a regional financial institution, ran a PoC building an AI chatbot with RAG and a bank-specific sLLM, reached a high accuracy rate, and demonstrated it has the capability to build finance-specific AI. The point was never a plausible answer — it was an answer that can show its basis.
In finance, the barrier for a chatbot was trust
The problems Bank J faced will be familiar to anyone in banking.
- There was no AI service wired into internal systems
- The customer-facing chatbot stayed at the level of a simple FAQ, reflecting neither real services nor operational information
- What was needed went past conversation: an LLM-based AI agent with security built in
In finance, a wrong answer is not a simple error — it is a trust problem. So the first principle we set was an AI that says it does not know when it does not, and shows its basis when it does.
Finance-specific AI, built on data sovereignty
Live financial data — embedded in the banking system inside the enterprise ERP, so it works from live financial data rather than a static FAQ.
RAG and a specialized sLLM — retrieval-augmented generation over Bank J's own data, together with a specialized sLLM, raised answer accuracy. It also reads financial visuals such as tables and charts, which makes it well suited to data analysis.
Sources on every answer — each answer carries the source it rests on, which raises confidence in the response and keeps compliance intact. Even for regulation-sensitive questions — limits by collateral type, for instance — it shows which material the answer came from.
Multi-agent orchestration — customer response, internal lookup, and risk management are split across several agents and automated. All of it runs on a custom on-premise MLOps environment, inside the internal network, so data never leaves.
What the PoC proved
- RAG, cited sources, and multimodal support raised answer quality
- Building inside the internal network strengthened security and compliance
- ERP and banking integration simplified the work process
- The customer-facing chatbot automated repetitive work and made real-time response possible
Above all, the PoC showed quantitatively that finance-specific AI can be built without giving up data sovereignty. Evidence-based answers and closed-network operation are design principles we have held to consistently in our Enterprise AI architecture.
If you are weighing AI in a regulated industry
In an industry where trust and security come first — finance, the public sector — an AI that cites its sources is the starting point. The approach is in Applied AI · certification and quality, and related cases are below.
- Actionable AI handling public procurement → Public agency K's SRM AI chatbot
- RAG over multimodal data → Company S
If you want to test it against your own regulations and data, get in touch through PoC and technical consultation.