AI coworker for banks and financial institutions
Employees delegate tasks in chat, work with agents configured for processes such as credit, sales and onboarding, and build their own. We set each agent up with your team, around the data and tools your institution approves and the review steps you define.
Illustrative interface, fictional data
How it works
Employees in chat, process owners with a configured agent and builders creating their own all work on one platform, with shared skills and the same administrative controls. Customer-facing assistants are scoped separately for each implementation.
Every agent action is logged and auditable. Role-based access, MFA that can be required per client and agent version history.
AIConsole works with leading LLM providers. Swap underlying models without rebuilding your agent workflows.
Who it is for
Choose your area to see an example of a task you could hand to an agent, what the agent would do and what stays with you.
Credit analyst, risk manager
The analyst receives a case ready to assess and spends the time on risk, not on assembling the file.
Corporate banking adviser, relationship manager
The adviser arrives prepared, and what was agreed does not get lost after the meeting.
Onboarding team, back office
The team focuses on exceptions while the agent handles the repetitive part of the process.
Digital banking, products, channels
Customers get an answer they can act on, within limits the bank has set.
CIO, CISO, architects
One AI platform under IT’s control, in place of tools chosen separately by each team.
Compliance, internal audit, data protection
A record of platform changes that your teams can review and explain.
Board, division directors, process owners
A clear view of where agents are working and who is responsible for them.
Internal processes
Each example below is a starting point that we adapt with your institution. This is how the work could look in four processes.
The agent prepares the case. People decide at the review steps, and above a limit the case also goes to a director.
Illustrative interface, fictional data
The adviser writes one sentence. The agent gathers context from the CRM and approved sources, picks up signals and suggests topics. After the meeting it drafts a follow-up for review.
Illustrative interface, fictional data
The agent checks the documents for a company account. Exceptions go to an employee with an explanation and a ready draft message.
Illustrative interface, fictional data
Application: 60%. Register: 45%. The register entry is newer than the application. I suggest confirming with the client.
A draft request to the client is ready for your review.
Two documents give different values for a leased asset. The agent works out where each version came from and applies the agreed rule: resolve or escalate.
Illustrative interface, fictional data
Build mode
Employees shape agents in conversation, without programming. Changes stay in a draft until someone with builder rights reviews them and deliberately publishes the new version.
In plain language, the way you would brief a colleague.
Choose the skills your organization has installed and attach the documents the agent should answer from.
The draft runs in your own preview, so nobody else gets a half-built agent.
A builder publishes the reviewed draft. Earlier versions stay available to restore.
Illustrative interface, fictional data
Every Monday, check changes in mortgage offers in the chosen sources and prepare a short note for the product team: what changed, at which lender and how we compare.
Customer assistants
A customer assistant could help people find and understand products, compare options and take actions the bank has allowed. What it may do, and with which consent, is agreed for each implementation.
Illustrative interface, fictional data
The bank sets the limits
Example configuration
Trust Layer
Trust Layer is a gateway between applications and AI models. It classifies content, applies the client’s policy, replaces personal data with tokens and restores them in the response.
Illustrative example, fictional data
Please compare 2025 revenue for Example Company Ltd. Contact: Anna Example, tax ID 000-000-00-00, account PL00 0000 0000 0000 0000 0000 0000.
Please compare 2025 revenue for Example Company Ltd. Contact: <PERSON_1>, tax ID <PL_NIP_1>, account <IBAN_CODE_1>.
Tokens are restored in the response to the application.
Masking happens before the model gateway, so the gateway and its logs handle tokens.
Each request is assessed against the client’s policy. Policy changes are proposed by one administrator and approved by another.
If the detector, policy or audit is unavailable, the request is refused.
Each decision is recorded in an audit log without its content.
Control and oversight
Administrators manage members and role-based access, decide who can build each agent and archive an agent when it should no longer run. Every agent action is logged and auditable, and data is encrypted at rest and in transit.
Illustrative interface, fictional data
| Agent | Origin | Owner | Version | Status |
|---|---|---|---|---|
| SME credit process | process agent | Credit department | v12 | Published |
| Sales | process agent | Corporate banking | v7 | Published |
| Company onboarding | process agent | Operations | v5 | Published |
| Customer assistant | mobile channel | Digital banking | v3 | Draft |
| Mortgage offer review | employee-built | m.example | v1 | Archived |
Sample data. Every agent action is logged and auditable.
Case studies
Two case studies where AIConsole was part of the technology.
An October 2024 scoped proof of concept for one part of a microenterprise credit workflow at a Polish commercial bank reduced application switches by 70–80%, made analysis over 50% faster, and indicated 100+ analyst hours saved monthly.
AI-supported research for factoring sales. In the April 2025 PoC, manual verification fell from 40 to 20 minutes, source or screen checks fell from 34 to 5, and task success reached 80%. Live since September 2025 with a limited sales group; expansion is underway.
Partnerships
We help financial institutions and digital commerce companies put Claude into production — custom agents built on the Claude API and the Claude Agent SDK, Claude Code and Claude Cowork enablement, and agentic workflows running on AIConsole.
Read the announcementStart Path is Mastercard's global startup engagement program. The Agentic Commerce & Services track works with companies building the tools and payment flows for AI-driven customer journeys, where payments, financing and insurance will increasingly be sold. That is the distribution environment we are building AIConsole for.
Read the Start Path announcementHow to start
We start with a process that matters to you and work with your team through setup. AIConsole is used where it fits your process and technology environment.
Together we identify where an agent would help and who owns the outcome.
Your own tenant, members and roles.
The sources and tools your IT team approves for this agent.
Steps, rules and review steps are agreed with the process owner and tried in preview.
The agent starts work once the process owner is satisfied with the configuration.
Questions
That depends on how each agent and its tools are configured, and we agree it with your team during implementation. Trust Layer replaces personal data with tokens before it reaches a model, and data is encrypted at rest and in transit.
The process owner, working with our team. Together you set the steps, the information required, the connected systems and the review steps. Changes stay in a draft until they are deliberately published.
Yes. Every agent action is logged and auditable. A run keeps the order of the steps an agent took, and agents have a version history.
Only what is agreed for the implementation and made possible by the connected services. You decide which actions need the customer’s confirmation.
Yes, within your organization’s setup. You describe the task in plain language, add skills and knowledge, try it in preview, and a builder publishes it for others.
No. AIConsole works with leading LLM providers, and you can swap underlying models without rebuilding your agent workflows. It can be deployed on Azure Cloud (default), your private cloud infrastructure, or fully on-premise.
Talk to us. We begin with one process, then set up your environment and first agent with your team.
Tell us which process you would like to improve, and we will show how an agent could work in it.
Talk to us about AIConsole