AI Agents for Banking
FISTA Solutions builds banking AI agents that reduce contact-center and back-office load: answering customer questions from read-only data, processing documents, taking dispute details, triaging exceptions, and answering staff procedure questions. Every account-changing or money-moving action requires a human approver.
- 150+
- projects delivered
- 50+
- companies served
- 99.9%
- verified uptime
- 47%
- efficiency gains
- 12+
- countries reached
What we build
What can AI agents do in banking?
Banking agents answer customer questions from read-only account data, classify and extract documents, capture dispute details and assemble evidence, triage posting and reconciliation exceptions, and answer staff procedure questions from approved policy with citations.
- 01
Customer support agent
Answers balance, transaction, product, and status questions from read-only data, escalating anything transactional.
Support - 02
Document processing agent
Classifies and extracts statements, identity documents, and loan files with page-level citations per value.
Documents - 03
Dispute intake agent
Collects Regulation E dispute details, assembles supporting evidence, and prepares the case for an analyst.
Disputes - 04
Exception triage agent
Works posting, reconciliation, and file-transfer exceptions, proposing resolutions and escalating outside tolerance.
Operations - 05
Staff procedure agent
Answers internal policy and procedure questions from approved documents with citations to the current version.
Internal - 06
Onboarding document agent
Validates account opening documentation, flags missing items, and prepares files for a reviewer.
Onboarding
Requirements
What guardrails do banking agents need?
Bank agents must satisfy examiners as well as customers, so guardrails cover authority, evidence, and change control: read-only defaults, approval gates, complete traces, versioned prompts and models, and evaluation results retained as part of the change record.
| Guardrail | Why it matters here | How FISTA implements it |
|---|---|---|
| Action authority | Agents must not change accounts or move money. | Read-only scopes by default, approval gates enforced in the tool layer, and the approving employee recorded on every action. |
| Examination evidence | Examiners ask how the system behaves and how changes are controlled. | Versioned prompts, models, and tools with evaluation results retained per release, and full traces per conversation. |
| Customer data protection | Agents access GLBA-protected information. | Purpose-scoped retrieval, masking where full values are unnecessary, and audit logging on every data access. |
| Accuracy on money facts | A wrong balance or fee answer is a complaint and a risk event. | Retrieval from authoritative systems only, no generative recall of figures, and abstention when data is unavailable. |
| Complaint handling | Regulated complaint processes must not be bypassed. | Complaint intent detection routes to the formal process, with the interaction recorded as part of the complaint file. |
Where AI fits
Which banking workflow should you automate first?
Start with internal staff assistance or read-only customer questions. Both remove real load without touching account state, and they produce the traces and evaluation history that make a later, more capable agent straightforward to justify to risk and audit.
- 01
1. Start internal
A staff procedure agent has lower risk and immediate value, and it builds institutional confidence with real usage data.
- 02
2. Move to read-only customer answers
Balance, transaction, and status questions are high volume and require no account changes.
- 03
3. Add document work
Classification and extraction remove back-office effort with human review retained on material values.
- 04
4. Introduce approval-gated actions
Let agents prepare disputes, maintenance, and cases that a human approves in one click.
- 05
5. Review with risk quarterly
Bring traces, evaluation results, and escalation statistics to risk and audit on a schedule, not on request.
Cost and timeline
How much does an AI agent for banking cost, and how long does it take?
Cost is driven by core integration constraints, document variety, and evidence requirements; timeline by vendor access and internal risk review. FISTA does not quote blind: the scoping call returns an agent design, guardrails, and a phased estimate.
Core access shapes the work. Where the core exposes modern APIs, read-only agents are quick; where it exposes batch files, a middleware layer is needed before an agent can answer anything in real time. FISTA determines that in discovery and prices the middleware separately.
Risk review is a scheduled cost in time rather than money. FISTA produces the artifacts risk and audit will ask for — data flows, control descriptions, evaluation evidence — during delivery, which typically shortens the review from a research exercise to a check.
Send the scope you have, even if it is a paragraph. You get a written brief, an architecture sketch, and a phased estimate before any commitment.
Get a scoped quoteDelivery
How does FISTA deliver an AI agent into production?
FISTA delivers agents in four gated phases: a discovery sprint that picks the workflow and writes the agent specification, a design that names tools, permissions, and approval points, a build with an evaluation harness and shadow runs on real work, and a production release with traces, dashboards, and rollback.
- 1
Select and specify
Choose the workflow with a measurable outcome, map its systems and edge cases, and write the agent spec with success metrics.
OutputAgent specification, golden test set
- 2
Design the guardrails
Tool inventory with least-privilege scopes, approval gates, escalation paths, data handling, and the evaluation plan.
OutputTool and permission matrix
- 3
Build and shadow-run
Implement tools as MCP servers or connectors, iterate against the evaluation harness, and run in shadow mode on live inputs.
OutputShadow-mode results, eval scores
- 4
Release and observe
Graduated rollout, full traces, cost and quality dashboards, on-call runbook, and a change process that re-runs the evals.
OutputProduction agent with SLOs
Why FISTA
Why choose FISTA Solutions to build your banking agents?
FISTA builds bank agents that stay read-only unless a human approves, keep examination evidence as a by-product of delivery, and never generate figures from model memory. Work is contracted through a US entity with full IP assignment.
Banking specifics
- Figures are always retrieved from authoritative systems; agents abstain rather than recall a balance from memory.
- Approval gates are enforced in tool permissions, so an instruction cannot talk an agent into acting.
- Evaluation results and version history are retained per release, producing the change-control evidence examiners expect.
- Complaint intent is detected and routed into your formal process rather than absorbed by an agent conversation.
How FISTA engineers
- Spec-Driven Development: every deliverable starts as a written specification with acceptance criteria, so scope is testable before it is built.
- AI-native delivery: engineers direct coding agents under review gates and evaluation harnesses, compressing build time without loosening verification.
- Official Anthropic partner, with production experience across Claude, OpenAI, Google, and open-weight models, chosen per workload rather than by default.
- One accountable delivery lead, weekly demos on your environment, and code in your repositories from week one.
What you get as a client
- 150+ projects delivered for 50+ companies across 12+ countries since 2017, with 99.9% verified uptime on systems we operate.
- A US entity (FISTA Solutions Inc., Wilmington, Delaware) for contracting, invoicing, and IP assignment, with an engineering center in Faisalabad, Pakistan for cost-efficient senior capacity.
- US business-hours overlap for standups and reviews; written decision logs so nothing depends on a meeting you missed.
- Flexible engagement: fixed-scope build, embedded forward deployed engineers, or a dedicated team that you can scale month to month.
Clear answers
What teams ask before deploying agents.
Straightforward guidance for evaluating scope, fit, and the next step.
01Can an AI agent talk to our customers?
Yes, within read-only scope: balances, transactions, product information, and status, with strict escalation on anything that changes an account or moves money. Every conversation is traced, and complaint intent routes into your formal complaint process.
02How do we satisfy examiners about AI use?
With documentation produced during delivery: model and prompt version history, evaluation results per release, tool permission matrices, data flow diagrams, traces, and escalation statistics. The evidence exists because the process creates it, not because it was assembled for the exam.
03What stops an agent from giving a wrong balance?
Figures are retrieved from authoritative systems at request time, never generated, and the agent abstains and escalates when the system is unavailable. Evaluation includes adversarial cases specifically targeting numerical accuracy.
04Can agents work with our legacy core?
Yes, though a middleware layer is often needed first when the core is batch-oriented or rate-limited. That layer is scoped and priced separately, and it benefits your digital channels beyond the agent.
05Do you work with credit unions?
Yes. Member service, document processing, and internal procedure agents deliver the same relief at credit union scale, usually starting with a single high-volume workflow rather than a broad program.
06How long does deployment take?
An internal agent can be in supervised use within weeks; customer-facing deployment typically takes a quarter including risk review and shadow-mode evidence. Discovery dates the review gates specific to your institution.
Scoped in writing before you commit
Answer the routine questions; escalate the ones that matter.
Bring the contact-center volume or the back-office queue. The scoping call returns an agent design, a control map, and a phased estimate.