Onboard and service more customers with the same ops team.
Banks, lenders and fintechs grow faster than their operations teams can hire. Onboarding files, alert reviews, disputes and servicing requests pile up, and every one needs evidence. We look for the workflows where agents assemble and check that evidence, so analysts spend their time on decisions. Credit decisions and risk judgments stay with people.
Six workflows where evidence gathering is the bottleneck.
These are the workflows we would examine first in a bank, lender, payments company or fintech. They run through a core or ledger system, a case management tool, screening providers, document uploads and shared inboxes.
| Workflow | Manual today | AI-native version | Stays human | Verification check |
|---|---|---|---|---|
| Business customer onboarding (KYB) | Analysts collect registry extracts, ownership charts and IDs, then run screening and write the file note by hand. | An agent collects and validates documents, maps beneficial owners, runs screening and drafts the file note with sources. | The risk rating, the onboarding decision and every screening hit. | Evidence complete for the risk tier. Ownership adds up to 100%. Screening hits are never cleared automatically. |
| Transaction monitoring alert review | Analysts open each alert, pull account history and counterparties, and write the disposition. | An agent gathers the history, related alerts and open-source context, and drafts a narrative for the analyst. | Every disposition and every decision to escalate or report. | Narrative facts must match the ledger. No alert is closed without a named analyst. |
| Loan application intake | Underwriting assistants chase payslips, statements and accounts, then re-key figures into the origination system. | An agent checks the application pack for completeness, extracts figures with source references and lists the gaps. | The credit decision, pricing and any exception to policy. | Extracted income and liabilities recomputed from source documents. No score or decision is produced by the model. |
| Card disputes and chargebacks | Agents read the customer's claim, pick a reason code and gather evidence before the network deadline. | An agent classifies the dispute, proposes the reason code and assembles the evidence pack. | Disputes above a set amount and any goodwill decision. | Reason code must fit the transaction type. Network deadlines tracked per case. |
| Servicing requests | Address changes, statement requests, payment holidays and limit changes are handled from email and chat. | An agent classifies the request, checks identity and eligibility, and prepares the change for approval. | Hardship cases, limit increases and anything touching vulnerable customers. | Identity verified to the required level before any change. Contact detail changes need a second factor. |
| Periodic KYC reviews | Review backlogs grow because each refresh means re-collecting documents and re-checking everything. | An agent pre-fills the review from existing data, finds what has changed and requests only what is missing. | The updated risk rating and any exit decision. | Changes since the last review are listed with sources. Overdue reviews are escalated. |
Six numbers that show whether it worked.
The Audit measures these before anything is built. The Pilot is measured against the same numbers.
| Metric | Definition |
|---|---|
| Analyst minutes per case | People time per onboarding file, alert or dispute, from opening to decision. |
| Time to onboard | Days from complete application to an account that can transact. |
| First-time-right rate | Share of files that pass quality assurance with no rework. |
| Review backlog | Alerts, disputes or periodic reviews past their internal due date. |
| Customer touches for documents | How many times a customer is asked for information before the file is complete. |
| QA finding rate | Quality assurance findings per 100 files, split by severity. |
What can go wrong in banking ops, and the control for each.
Credit decisions and risk judgments stay with people. The EU AI Act treats creditworthiness assessment of individuals as high-risk. Our agents gather and check evidence, they never hold payment credentials, and every step is written to an append-only audit trail.
| Failure mode | Control | How we'd detect it |
|---|---|---|
| Screening hit cleared without review | Hits always go to an analyst. Clearance needs a recorded reason. | Audit trail of every hit, decision and reviewer. |
| Credit decision made by a model | Agents prepare the application pack only. Scoring and decisions run in your approved credit process with a named decision-maker. | Check that every approval and decline has a human decision record. |
| Wrong figure in an application or file note | Every extracted figure carries its source. Ratios are recomputed by code, not by the model. | QA sample with field-level correction log. |
| Account takeover through servicing | Contact detail changes need step-up verification and a cooling-off period. Agents cannot change them alone. | Alert on every contact detail change followed by a payment or limit change. |
| Unfair or inconsistent treatment | Vulnerable-customer and hardship signals route to trained staff. Outcomes are reviewed by segment. | Periodic outcome review across customer groups. |
| Customer data exposed | Agents see only the fields a task needs. Self-hosted, EU-hosted deployment is possible. | Access logs per agent and per task. |
Business onboarding, from diagnostic to pilot.
Illustrative example — not a client result. The numbers below are assumptions chosen to show the method and the arithmetic. Your Audit replaces them with measured values.
| 01 | Diagnostic | Suppose a business banking fintech picks "business customer onboarding" on the General track. The Snapshot might show a High leverage signal with Medium confidence, friction at the Processing and Verification stages, and Level 2 (workflow augmentation) as the indicated AI depth. |
| 02 | Audit | Suppose the Audit measures 600 business applications a month at 45 minutes of analyst time each. That is 600 × 45 = 27,000 minutes, or 450 hours a month, about 2.8 FTE at 160 hours. It also finds that about 40% of files have screening hits, complex ownership or missing documents. |
| 03 | Pilot | Suppose the 60% clean files arrive pre-assembled, and an analyst still reviews and decides every one in 15 minutes. The 40% complex files take 50 minutes each, because they are the harder cases. The Pilot measures whether that holds. |
| Line | Assumption | Hours per month |
|---|---|---|
| Baseline | 600 applications × 45 min | 450.0 |
| Clean files, after | 360 files, all reviewed × 15 min | 90.0 |
| Complex files, after | 240 files × 50 min | 200.0 |
| After, total | 90.0 + 200.0 | 290.0 |
| Difference | 450.0 − 290.0 (about 1.0 FTE) | 160.0 |
At an assumed loaded cost of €55 an hour, 160 hours is about €8,800 a month. An analyst still decides every file. The Audit would also measure time to onboard and customer touches, because asking once for the right documents loses fewer applicants. It would count the new costs too: compute, screening provider calls, integration and QA time. If the numbers don't hold, the recommendation is "don't proceed".
Where our proof comes from.
We'll be plain about this. Our production proof is in AdTech, not banking. We have not yet run these banking or lending workflows for a client.
The closest relatives are invalid-traffic detection and partner reconciliation, which we run in AdTech production: score every event, check before anything commits, escalate what the rules can't settle, and keep a record of why. The case studies show that work, and four of our AI plugins run live on reviveadservermod.com. Our governance working paper maps runtime controls to the EU AI Act.
Our engineering team and delivery network have built related systems: multilingual agentic workflows for financial operations, in production with a large active user base, a multi-agent financial copilot for analysis and transaction routing, and hybrid search over complex financial documents. See Selected builds.
That is why banking and fintech engagements start with the free diagnostic and the Audit. The Audit measures your own baseline and records the controls your compliance team relies on today.
Questions banking and fintech operators ask first.
Will an agent make credit or onboarding decisions?
No. Agents assemble and check the evidence. Credit decisions, risk ratings and onboarding approvals stay with named people in your existing process.
How does this fit with our compliance and model risk teams?
We recommend involving them from the Audit onward. The Audit records the controls they rely on, and the design gives them a full log of every proposal, check and human decision.
We already use a screening provider. Does this replace it?
No. Your screening provider keeps doing the screening. Agents call it, gather the context around each hit and prepare the case, so analysts spend less time collecting and more time deciding.
Find out which banking workflow is worth redesigning.
Start with the free diagnostic on the Other / General Business track. Move to the Audit only if the Snapshot points to something worth measuring.
| 01 | Free AI Operational Diagnostic | 10 steps, 8–12 minutes, General track. Snapshot on screen and as a PDF. |
| 02 | Operational Leverage Audit, from $1,500 | Fixed fee, one workflow, 10–15 working days after data access. Measured baseline, verification architecture, benefit model with stated assumptions, go / no-go. |
| 03 | Pilot implementation, from $6,000 | Fixed scope. The workflow built and run in your environment, with an audit trail, measured against the Audit baseline. Typically 4–8 weeks. |