AI for fintech + AI fintech development — orchestrate AI inside your compliance framework, not around it.

KYC AI workloads are the entry point most fintechs ask about first. KYC providers (Persona, Alloy, Onfido) return verdicts in ~30 seconds, but routing the 8–12% edge cases through manual review takes 3–7 business days. Drop-off at the KYC gate runs 18–35% at most fintechs, and the funnel math gets ugly fast. AI AML alert triage runs on the same pattern — a parallel signal layer that pre-classifies before the human looks.

An orchestration agent takes the KYC vendor verdict plus applicant context — employment signals, transaction history if you have it, document quality — and either auto-clears low-risk edge cases or routes to the right human reviewer with a pre-built decision packet. Sanctions and PEP screening run in parallel calls so the wall-clock doesn't grow. Nothing here replaces the KYC primitive; the agent's job is to make the edge-case routing legible and the audit trail clean enough for a BSA/AML examiner to read in one pass.

Fraud platforms (Sift, Feedzai, FICO Falcon, Stripe Radar) score in real-time, but the analyst team reviewing flagged transactions burns 4–8 minutes per case reading raw signals. The chargeback-vs-friction tradeoff comes out wrong because the analyst can't read fast enough to second-guess the score.

An LLM drafts case briefs from the fraud platform's score plus the raw signals plus the transaction graph. The analyst gets "here's why this scored 0.82, here are the 3 strongest signals, here's the cohort comparison" in 8–15 seconds instead of 4 minutes. We don't replace the fraud model — we make its output legible. Case-similarity retrieval over historical decisions teaches the brief format your team already trusts.

Bank model-risk teams need a written justification for every adverse credit decision. The data team's SHAP plots aren't a customer letter; the customer letter is hand-written by a senior analyst. 90 minutes per declined application is the median we see, and the queue grows the moment volume does.

An LLM takes the credit model's feature importances plus the applicant profile plus the bank's lending policy and drafts a regulator-readable adverse-action letter (ECOA and Regulation B compliant) plus an internal SR 11-7 model-output justification. A human reviews and signs; nothing auto-sends. The same engine produces the model-risk inventory entry the MRM team needs at quarter-end audit.

Regulators ship hundreds of pages of guidance per quarter — Fed, OCC, FDIC, CFPB, FCA, MAS, EBA — and the compliance team's 6–10 analysts can't read everything. New rules silently break old workflows, and the discovery lag runs 4–8 weeks at the fintechs we've audited.

RAG over your subscribed regulatory feeds plus internal policy docs. An agent surfaces "this new OCC guidance changes how Section 4.2.3 of your fair-lending policy reads — here are the 3 workflows it touches." Compliance officer reviews; nothing auto-implements. Retrieval quality is the load-bearing piece; we benchmark Cohere Rerank against your team's gold-standard answers before it touches a single policy document.

Daily and weekly reconciliation across payment processors, banking partners, FX desks, and the internal ledger eats 6–12 finance ops hours per day. Breaks surface 24–72 hours late, which means a payout problem on Monday gets noticed Wednesday and fixed Friday.

An agent ingests the four-to-six source feeds — Stripe, Adyen, Modern Treasury, your bank-partner API, your ledger — and surfaces breaks with proposed resolution paths ranked by historical pattern match. Finance ops approves or escalates. The agent never moves money; it just removes the manual-trace step that ate the team's morning.

Fintech support sees 60–80% of tickets about transaction status, dispute progress, KYC re-verification, payment delays, and statement explanations. Agent training is heavy because the system-of-record is genuinely multiple APIs, not a single CRM record.

A grounded chatbot with tool-calling against your payment processor, ledger, dispute system, and KYC vendor. It reads the actual transaction state from the systems, drafts the customer response, and escalates on uncertainty. The disputes path stays human-supervised by default — chargeback-reason-code language is too compliance-loaded to ship unsupervised on day one.

B2B fintech AEs and account managers carry 80–200 accounts; depth-of-context dies past about 30. The AM never has time to read the customer's last 90 days of transactions before the QBR, so the QBR runs on the customer's framing rather than yours.

An account-brief agent reads the customer's transaction volumes, product mix, expansion signals, support history, and recent regulatory exposure. It drafts the QBR pre-read 24–48 hours before the meeting. AE and AM read it, add the relationship layer, and walk into the room with the numbers already correlated. The brief lands inside Salesforce or HubSpot — the AM doesn't open a new tool.

AI compliance consulting that lives in the product. Engineers, product managers, and BD hit compliance questions weekly. "Can we offer this feature in California?" "Does this margin call hit Reg T?" "Does the new payment flow trigger FinCEN reporting?" They either wait days for a compliance officer's answer or guess and ship.

RAG over your internal compliance policy library plus relevant external regulatory text. The agent answers with citations from your policy doc and the regulator's language. Genuinely-novel questions route to the compliance officer's queue, not the customer-success queue. The routing logic is the unsexy load-bearing piece of AI compliance consulting at scale — get it wrong and the compliance team drowns; get it right and product velocity goes up.