Compliance-aware AI automation for fintech ops teams
KYC prep, support triage, fraud-signal clustering, and onboarding automation. Human-in-the-loop where regulation demands, $3,000/mo retainer.
Who this is for
Fintech ops or product lead, compliance-aware, already dealing with KYC volume, support cost, and fraud-signal noise.
The pain today
- KYC review takes days per customer
- Support is expensive and scales with customer count
- Fraud alerts are noisy and drown real signals
- Regulatory review would catch anything that lacks audit trails
- Cannot move fast on AI features because of compliance fear
The outcome you get
- AI automations for fintech ops on $3,000/mo retainer
- KYC prep automation with human review in the loop
- Support triage cutting response time 30 to 50 percent
- Fraud signal clustering reducing false-positive rate
- Audit trails on every AI decision for regulatory review
Where AI pays back fastest in fintech ops
Three places deliver clear ROI. KYC prep — LLM reads application documents (IDs, business registration, utility bills), extracts structured data, flags inconsistencies for human review. Cuts human KYC time 50 to 70 percent. Support triage — incoming tickets categorised, prioritised, and routed with suggested first responses. Fraud signal clustering — group related fraud alerts so human analysts review patterns instead of individual alerts. Each preserves human judgement while eliminating the work humans should not be doing.
Compliance-aware automation
Fintech AI runs on three rules. Human-in-the-loop for every regulatory decision — LLM suggests, human approves. Full audit trail of AI inputs, outputs, and human overrides. Explainability where required by regulation (EU AI Act, US state-level rules). Violations of any of these create regulatory exposure that kills the business. I build to these rules from day one. No fully-automated KYC approval, no fully-automated fraud decision, no AI action that regulators cannot review. Within those rules, AI delivers real throughput.
Typical tools (OpenAI, Anthropic, vector DBs, workflow engines)
LLMs: Anthropic Claude for sensitive reasoning tasks (KYC document review, complex fraud analysis), OpenAI for structured output. Vector DBs: Pinecone, Qdrant, or Postgres pgvector for RAG over internal knowledge (compliance docs, case history). Workflow engines: Temporal, Inngest, or simpler queue systems (BullMQ, AWS SQS) for multi-step automations with retries and human approval steps. Monitoring: Datadog, Sentry, custom dashboards for AI-specific metrics (latency, cost, output quality). Stack choice per use case, within compliance baseline.
Pricing and engagement model
$3,000/mo retainer. Covers AI integration work, compliance-aware architecture, prompt engineering, monitoring, and tuning. 14-day money-back guarantee. Cancel anytime. 100 percent code ownership under Work Made for Hire. NDA and BAA (where PHI touches fintech work) standard. US LLC invoicing — IRS/IR35 safe. LLM API costs pass through. For fintechs needing deeper integration with core banking or card-issuing systems, scope extends — often paired with Applications subscription for those integrations.
Case: bolttech — integration-heavy fintech playbook
At bolttech, the $1B+ unicorn backed by Tokio Marine and MetLife, I led the Payment Service integrating 40+ payment providers across Asia and Europe with 99.9 percent uptime. Stack: NestJS, React, MongoDB, Redis, TypeScript. The lesson for fintech AI: reliability and audit discipline at unicorn scale transfer directly. Every AI workflow in a fintech must hit similar reliability bars — idempotent, observable, auditable. The integration patterns that worked for payment providers work for LLM APIs with fintech-appropriate governance on top.
When regulated workflows must stay fully manual
Some fintech workflows should not use AI at all. Final KYC approval decisions in regulated markets. Policy cancellation or fraud freeze decisions affecting customer access. Anything where regulator would ask 'why was this customer rejected' and the answer cannot trace cleanly back to human judgement. I help you map which workflows are AI-appropriate (suggesting, triaging, extracting) vs human-only (deciding, approving, explaining). Most ops workflows have AI-appropriate sub-tasks that deliver value without touching regulatory decisions.
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.
Frequently asked questions
The questions prospects ask before they book.
- What about PII in prompts?
- PII can be sent to enterprise tiers of OpenAI or Anthropic that have data-processing agreements with no-training commitments. For highly sensitive PII (full card numbers, SSNs, biometric data), self-hosted open-source models (Llama via vLLM) or dedicated services. PII minimisation is a design principle — only send what the task needs. Prompts logged for audit but masked in shared logs. Documented in your privacy posture.
- Where can we host the AI infrastructure?
- For hosted LLM APIs, the region is controlled by the provider (OpenAI US, Anthropic US, Azure OpenAI in multiple regions). For EU-only data residency, Azure OpenAI with EU region or self-hosted open-source models on EU infrastructure. For financial data with strict residency (Brazilian LGPD, Swiss FINMA), self-hosted is usually required. Host choice made during the compliance-mapping phase.
- How do you choose between Claude, GPT, or Llama?
- Claude for complex reasoning, long-context analysis, sensitive work (customer communication, compliance review). GPT for structured output, function calling, many-concurrent workloads. Llama (open source, self-hosted via vLLM or through Replicate/Groq) for high-volume or data-sensitive workloads. Most fintechs use a mix — Claude for customer-adjacent tasks, GPT for structured extraction, Llama for high-volume triage. I choose per task based on quality, cost, and data sensitivity.
- Do AI actions leave audit trails?
- Yes. Every AI call logs input (with PII redaction where needed), output, model used, timestamp, and downstream action taken. Human overrides of AI suggestions log the override reason. For regulated workflows, the full trail is queryable for compliance review. This is not optional for fintech — it is baseline from day one. I build it into the monitoring layer, not bolt on later.
- Can we keep data in-region for specific markets?
- Yes. For clients with Brazilian LGPD, Swiss FINMA, or similar in-region requirements, AI infrastructure runs on in-region hosting. Hosted APIs with regional presence (Azure OpenAI EU, AWS Bedrock regions) or self-hosted on in-region cloud. Slightly higher infrastructure cost compared to US-only, but required for compliance. Decision made per market during the onboarding audit.
Ready to start?
Tell me what you need in 60 seconds. Tailored proposal in your inbox within 6 hours.