KYC prep, AML triage, fraud-signal clustering, and onboarding automation — human-in-the-loop where regulation demands it, at $3,999/mo.
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monthly retainer
Who this is for
You run ops or product at a fintech. KYC volume is growing faster than your review team, AML alerts are noisy, and support costs scale with every new customer. You want AI to take the repetitive work off human plates — but you cannot afford a compliance failure or an audit that traces back to an opaque model decision.
The pain today
- KYC review takes days per customer and the backlog grows every quarter
- AML alerts are so frequent that real signals get buried in noise
- Support tickets scale with customer count but hiring doesn't stop costing money
- Fraud decisions need a human, but the prep work before that decision is entirely manual
- Every new AI initiative stalls on 'what happens if the regulator asks about this'
The outcome you get
- KYC prep automated with structured extraction and human approval in the loop
- AML alert clustering so analysts review patterns instead of individual transactions
- Support triage cutting first-response time without removing human judgment on edge cases
- Full audit trail on every AI input, output, and override — queryable for compliance review
- Compliance-mapped onboarding that shows where AI is appropriate and where humans must decide
Where AI pays back fastest in fintech ops
Three workflows deliver clear, measurable ROI for most fintechs.
KYC prep: an LLM reads application documents — IDs, business registrations, utility bills — extracts structured data, flags inconsistencies, and prepares a review packet for the human analyst. The analyst makes the approval decision; the AI does the reading, extraction, and flagging. That step alone cuts human KYC time by 50 to 70 percent in the fintechs I've seen tackle it.
AML triage: instead of human analysts processing individual transaction alerts, a clustering layer groups related alerts by counterparty, timing, and behavioral patterns. Analysts review clusters and decide on SARs. The AI reduces the volume of individual decisions without making any of them.
Support automation: incoming tickets are categorised, prioritised, and routed with a suggested first response. Compliance-sensitive queries (account freezes, dispute escalations) route to humans automatically. The rest resolves faster.
KYC prep automated with structured extraction and human approval in the loop
Compliance-aware automation: the three rules
Fintech AI runs on three non-negotiable rules. Human-in-the-loop for every regulated decision — the model suggests, the human approves. Full audit trail of AI inputs, outputs, confidence levels, and human overrides. Explainability where required by regulation, including the EU AI Act and US state-level financial rules.
Violating any of these creates regulatory exposure that ends the business. I build to these constraints from the first architecture decision, not as a retrofit.
In practice: no fully-automated KYC approval, no autonomous fraud decision, no AI action that a regulator cannot reconstruct from logs. Within those guardrails, there is still a very large amount of work AI can own — reading, extracting, clustering, suggesting, routing. That is where the throughput gains come from.
At bolttech, the $1B+ unicorn backed by Tokio Marine and MetLife, I led the Payment Service integrating 40+ payment providers across Asia and Europe at 99.9% uptime. The reliability and audit discipline required at that scale transfers directly to fintech AI work. Every AI workflow needs the same properties: idempotent execution, observable state, auditable decisions.
40+: Payment providers integrated.
AML workflow automation without autonomous decisions
AML is where fintech teams feel the most pressure — and where AI is most useful as a triage layer rather than a decision-maker. The two most impactful AML automations are alert clustering and SAR preparation.
Alert clustering uses embeddings and similarity search over transaction patterns to group related alerts before a human analyst sees them. Instead of reviewing 300 individual alerts, an analyst reviews 40 clusters with clear patterns and suggested risk scores. The decision — file a SAR or not — remains with the human.
SAR preparation takes a different angle: once a human analyst decides to file, AI drafts the SAR narrative from the transaction record and case notes, saving 30 to 60 minutes per filing. The analyst reviews, edits, and submits. Audit trail covers both the AI draft and every human change.
I use vector databases (Pinecone, Qdrant, or Postgres pgvector) for the similarity layer and Anthropic Claude for the reasoning and drafting tasks. Stack is chosen per use case and per your existing data residency requirements.
Onboarding funnel: where AI and compliance meet
Customer onboarding is the highest-ROI area for most fintechs because the cost-per-onboarding is visible and the bottleneck is obvious. A typical onboarding automation covers four steps.
Document intake: AI extracts structured fields from uploaded identity documents using OCR and vision models. It flags missing fields, expired documents, or inconsistencies before a human ever opens the case.
Risk scoring: a model scores the application against internal risk rules and external watchlists (PEP, sanctions). High-risk applications route to senior review; standard applications route to a lighter review queue.
Decision support: the reviewer sees a structured summary — extracted data, flags, risk score, source documents — rather than a raw document pile. Decisions are faster because the work is done.
Audit packaging: every step is logged. The regulator's question — why was this customer onboarded or rejected — has a clean, traceable answer.
Stack and tool choices for fintech AI
LLMs: Anthropic Claude for sensitive reasoning tasks — KYC document review, AML narrative drafting, compliance reasoning. OpenAI GPT-4o for structured output extraction and high-concurrency workloads where cost-per-call matters.
Vector databases: Pinecone, Qdrant, or Postgres pgvector for RAG over internal documents — compliance policy, case history, sanctions lists.
Workflow engines: Temporal or Inngest for multi-step automations that require retries, human approval steps, and durable state. BullMQ or AWS SQS for lighter queue work.
Data residency: for EU-only requirements, Azure OpenAI with EU region or self-hosted models on EU infrastructure. For Brazilian LGPD or Swiss FINMA, self-hosted open-source models on in-region cloud. Slightly higher infrastructure cost; required for compliance.
Monitoring: Datadog or Sentry for standard observability, plus custom dashboards tracking AI-specific metrics — latency per call, cost per document processed, model accuracy drift over time. Every fintech AI workflow needs this from day one.
What fintech AI workflows should stay fully manual
Not every workflow is an AI candidate. Final KYC approval in regulated markets. Policy cancellation or fraud freeze decisions affecting customer account access. Any regulatory decision where the regulator asks 'why was this customer rejected' and the answer must trace clearly to human judgment.
I map AI-appropriate sub-tasks versus human-only decision points before writing a line of code. Most fintech ops workflows have large AI-appropriate preparation steps and small human-only decision steps. Automating the preparation and keeping the decision human is the right split — it reduces cost, improves speed, and keeps the compliance risk where it belongs.
The common mistake is trying to automate the decision itself to save one more step. That one step is the entire regulatory exposure. The preparation steps before it are where the work, and the savings, actually live.
Recent proof
A comparable engagement, delivered and documented.
Unified payment orchestration across Asia and Europe
Delivered the payment orchestration platform at bolttech, a $1B+ unicorn, with 40+ integrations across multiple regions.
Read the case studyFrequently asked questions
The questions prospects ask before they book.
KYC document prep, AML alert clustering, SAR narrative drafting, support ticket triage, and onboarding risk scoring are the highest-ROI starting points. They share a pattern: high volume, repeatable structure, and a clear human decision at the end. The AI handles the reading and extraction; the human handles the regulated decision. That split is where compliance safety and throughput gains coexist.
Not in any regulated market I'd build for. Final KYC approval and fraud decisions affecting account access must trace to a human. What AI does well here is everything before that decision: document reading, data extraction, risk scoring, flagging inconsistencies, and preparing a structured review packet. That preparation work can be 70 to 80 percent of the total time, and it's the part AI owns cleanly.
Every AI call logs the input (with PII redacted where needed), output, model used, confidence score, and timestamp. Human overrides log the override reason and reviewer ID. For regulated workflows, the full trail is queryable so compliance teams can reconstruct any decision. I build this into the monitoring layer from the start — not bolted on after the fact — because a regulator asking for decision history is a certainty, not a possibility.
Enterprise tiers of Anthropic and OpenAI have data-processing agreements and no-training commitments for customer data. For highly sensitive PII — full card numbers, SSNs, biometric data — I use self-hosted open-source models (Llama via vLLM on your own infrastructure) or dedicated API services with stricter data handling. PII minimisation is a design principle from day one: only send the fields the task actually needs. What goes to the model, and why, is documented in your privacy posture.
For EU-only data requirements, Azure OpenAI with an EU region or self-hosted models on EU cloud infrastructure. For Brazilian LGPD or Swiss FINMA requirements, self-hosted models on in-region cloud are usually required because hosted API residency guarantees are insufficient. The infrastructure cost is slightly higher than US-only, but it's not optional. Data residency decisions get made during the compliance-mapping phase before any model is called.
Clustering is an AI task: group hundreds of individual transaction alerts into a smaller set of patterns by counterparty, timing, and behavioral similarity, so human analysts review 40 clusters instead of 300 individual alerts. Decisioning — whether to file a SAR, freeze an account, or escalate — is a human task. The value of clustering is that it makes the human decision faster and better-informed. AI never decides on the SAR; it prepares the case the analyst uses to decide.