Financial Services
AI for banks, deployed with the controls your risk teams expect to see
Kriv AI helps banks and financial services firms put AI assistants and agents to work on real banking tasks, with an owner, an inventory entry and a review step for every system, written to the policy your institution already answers to.
AI for banks means using AI assistants, document models and agents on banking work such as compliance reviews, customer correspondence, credit file preparation and research, under the bank's own risk management. The technology is the easy part. The work that decides success is choosing the right first tasks, keeping a person accountable for each output and recording every AI system so model risk, compliance and audit can review it.
what it means
What AI for banks looks like in practice
The practical starting points for AI in a bank are document-heavy, rule-bound tasks: reading, summarising, checking and drafting. An assistant drafts a response to a customer complaint for a person to approve. A document model pulls fields from a loan file into a checklist. An agent assembles the materials for a periodic review. In each case a named person signs off the result.
What separates a bank from other industries is the review around that work. Every AI system needs an owner, a place in the inventory and a decision about how closely its output is checked. Our pages on AI governance consulting for banks and on AI model risk management for banks cover that side in depth.
use cases
Where banks start with AI
Good first use cases are frequent, document-heavy and easy to check. Each row links to the page where we cover it in depth.
| Area | What AI does | Where a person stays accountable |
|---|---|---|
| KYC and AML | Gathers and summarises customer and transaction information, and drafts alert narratives for an analyst. | The analyst decides every alert and signs the narrative. |
| Compliance review | Compares documents, policies or communications against a rule set and flags gaps. | A compliance officer reviews every flag before action. |
| Credit file preparation | Extracts figures and terms from submitted documents into a structured file. | The credit officer checks the extracted data; AI does not make the decision. |
| Customer correspondence | Drafts replies to complaints and requests from templates and case details. | Staff approve each draft before it is sent. |
| Research and reporting | Summarises filings, research and internal data into a first draft. | The report owner verifies figures against the source. |
agents
AI agents for banks and financial services
AI agents for financial services go a step further than an assistant: they decide some of their own next steps, such as which system to query or which document to fetch. That suits tasks where the path varies, like assembling a review file. It also means an agent needs permissions limited to its task, a clear limit on what it may do without approval and a log of every action.
For AI agents for finance teams, we start with read-only agents that prepare work for a person, and only later consider agents that write to systems. Our page on agentic AI in investment banking compliance shows how this works in a regulated workflow, and our AI agent security risk page covers the controls.
ChatGPT for finance and Claude for financial services
One starting point is an enterprise assistant. Kriv AI is in the OpenAI Partner Network · Select tier and holds Claude Partner Network, Select partner · Services Track, so we deploy and govern both. Our ChatGPT for business page covers plan choice and rollout, and our Claude AI for finance page covers Claude in banks, asset managers and insurers.
governance
Governing AI in a bank: SR 26-2 and your own risk management
In April 2026 SR 26-2 superseded SR 11-7 as the model risk management guidance. SR 26-2 states that generative AI and agentic AI models are not within its scope, and that the guidance does not set forth enforceable standards. That does not leave those systems ungoverned: they still sit under the bank's own risk management and governance, and someone still has to answer for them.
In practice we help banks apply the same discipline to AI assistants and agents that they apply to models: an inventory entry, a named owner, a risk tier and ongoing checks. We write those rules to the policy and frameworks your institution already answers to. Your risk and compliance functions keep the final judgement. Our SR 26-2 page explains the change in detail.
not first
What we do not recommend as a first AI use case
We steer banks away from starting with any task where AI would make or appear to make a decision about a customer, such as approving credit or closing an alert. Those can come later, if at all, once the bank has run simpler systems through its own review and knows how its controls behave in practice.
We also advise against a first project that touches many systems at once. A single use case with one owner, a clear review step and data that is already approved for the tool gives risk and compliance something concrete to assess. A broad programme launched before any system has passed review can stall in that review.
why kriv
Why banks work with Kriv AI
Kriv AI sends a senior practitioner who both scopes and delivers: no handoffs, no bench. Our focus is regulated industries, so governance is part of every deployment rather than a separate project. Our partner standing: OpenAI Partner Network · Select tier; Claude Partner Network, Select partner · Services Track; Microsoft Solutions Partner for Cloud & AI Platforms · Partner ID 7036589; and Databricks partner.
The honest gap: we do not publish client names without written consent, so this page describes the work rather than naming banks.
how an engagement runs
How a first AI deployment at a bank runs
1. Discovery call
We learn which teams want AI, which tools are approved and how your model risk and compliance functions review new systems. Work is scoped and priced after this short discovery call.
2. Use case and rules
We pick one use case with the business owner and write its rules: data in scope, the review step and what gets logged.
3. Pilot with review
We deploy on real but limited work, with a person reviewing every output and the system recorded in your inventory from day one.
4. Handover
Your team receives the configuration, the written rules, the review records and training, so the system can pass your own review and be maintained internally.
Related resources
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Straight answers
Frequently asked questions about AI for banks, deployed with the controls your risk teams expect to see
How are banks using AI?
The practical uses are in document-heavy, rule-bound work: summarising and checking information for KYC and AML, reviewing documents against policy, preparing credit files, drafting customer correspondence and summarising research. In each case a person reviews the output and remains accountable. Decisions such as approving credit or closing an alert stay with staff.
Does SR 26-2 apply to generative AI?
No. SR 26-2, which replaced SR 11-7 in April 2026, states that generative AI and agentic AI models are not within its scope, and it does not set forth enforceable standards. Those systems still fall under the bank's own risk management and governance, so they still need an owner, an inventory entry and appropriate checks.
Can banks use ChatGPT for finance work?
Yes, through an enterprise plan configured to the bank's data rules, with staff trained on what may and may not go into it. Kriv AI is in the OpenAI Partner Network · Select tier and deploys ChatGPT with a written usage policy and review steps. Our ChatGPT for business page covers plan choice and rollout.
Are AI agents safe for banks?
They can be, when their permissions are limited to the task, they cannot act on a system without approval, and every action is logged. We start banks with read-only agents that prepare work for a person, and widen what an agent may do only after it has run under review.
Where should a bank start with AI?
Start with one use case that runs often, is document-heavy and is easy to check, owned by a business leader who wants it. Record it in the inventory from day one and agree how its output is reviewed. A first deployment that passes your own review makes the second one faster.
How long does a first AI deployment take?
It depends on the use case, the tools already approved and how your review functions work. A pilot on a single, well-scoped use case is the fastest route, because it gives risk and compliance something concrete to review. We agree a timeline after the discovery call.
What does AI deployment at a bank cost?
Our work is scoped and priced after a short discovery call, once we know the use case, the tools and the review requirements. Tool licensing is separate and set by each vendor.
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