--- title: "Fintech AI: fraud, KYC, audit-grade observability" description: "Production AI for neobanks, payment platforms, lending, and regulated fintech. Fraud detection, KYC document AI, audit-grade observability, and on-prem deployment." source: "https://www.kensink.com/industries/fintech-ai/" canonical: "https://www.kensink.com/industries/fintech-ai/" --- ★ Industry vertical · Fintech AI 06 service items Audit-grade engagements INDUSTRY · FINTECH AI · PAYMENTS + LENDING # AI that handles money. Carefully. Production AI for neobanks, payment platforms, lending, and regulated fintech. Fraud detection at p95 latency banks expect, KYC document pipelines that pass regulator review, and an audit trail your compliance officer can put their name on. [Start a project →](https://www.kensink.com/contact) [View case studies →](https://www.kensink.com/cases) Industry Fintech · payments · lending Compliance SOC 2 · KYC · AML Reliability Audit-grade by default Stack Direct LLM · auditable \[WHAT WE HEAR FROM CTOs AND COMPLIANCE LEADS\] ## Three pains every fintech-AI team hits. We have shipped against this shape with neobanks, lending platforms, payment processors, and remittance corridors. The constraints repeat. The fix is not a smarter model. The fix is the audit trail underneath it. Per-decision logging, regulator-defensible eval, and a deployment shape that fits the bank your platform is regulated as. PAIN · 01 01 / 03 ### AI that touches money needs a different reliability bar. A fraud-detection false negative costs real money on the next transaction. A KYC false positive blocks a real customer's payday. The reliability bar is not 'good enough,' it is 'defensible in audit.' ↓ How we fix it, below. PAIN · 02 02 / 03 ### The audit trail blows up most LLM stacks. Regulators ask: 'show me every input and every output for this customer for the last seven years.' Most LLM observability tools sample. That answer ends a license review. ↓ How we fix it, below. PAIN · 03 03 / 03 ### Compliance varies per market. KYC standards in Bangladesh, AML rules in the US, GDPR in the EU, MAS in Singapore. The architecture has to support country-level policy overrides, not a single global config. ↓ How we fix it, below. \[SIX SERVICE ITEMS · ONE TEAM\] ## Pick the fintech-AI problem. We'll bring the audit trail. Eight-week engagements, audit-grade by default, deployment shape your bank's security team can sign off on. Bundle two when the problem warrants. SERVICE · 01 / 06 Real-time decisioning Fraud detection engine Hybrid ML + LLM scoring on every transaction. Sub-100ms decisions, explainable to a compliance officer. - Per-feature contribution logs for every decision - LLM-on-graph for novel scheme detection - Configurable policy thresholds per country and per tier Python PostgreSQL Redis OpenTelemetry SERVICE · 02 / 06 OCR + verification KYC document AI Identity document parsing, liveness signal capture, and address verification on a pipeline that passes regulator review. - Multi-country ID parsing with structured field extraction - Stratified accuracy reporting per document type and country - Configurable refusal patterns for low-confidence parses Python Vision LLMs Anthropic Zod SERVICE · 03 / 06 Compliance-first Audit-grade observability Every input, every output, every decision logged with retention controls and per-customer redaction. Regulator-defensible by default. - Full prompt + completion archive with retention policy - Per-decision feature-importance log - Replay tooling: pull a customer journey for compliance review ClickHouse OpenTelemetry PostgreSQL Grafana SERVICE · 04 / 06 Hallucination guardrails LLM eval for financial advice AI advice that names what it does not know. Refusal patterns, source citation, and a regulator-readable eval suite. - Golden set of regulator-sensitive financial questions - Hallucination + advice-out-of-scope scoring - Refusal patterns gated on knowledge cutoff and jurisdiction LangSmith Promptfoo TypeScript Anthropic SERVICE · 05 / 06 Inside the data boundary On-prem deployment for banks Open-weights models inside the bank's VPC. Customer data never leaves the boundary, model selection still flexible. - Self-hosted Llama / Mistral / Qwen with vLLM throughput - Vendor-neutral abstraction so model swap is a config change - Air-gapped option where regulator requires vLLM Llama Kubernetes Cloudflare SERVICE · 06 / 06 Pre-build wedge Architecture review One-week audit of your fintech-AI stack. Vector store, model provider, deployment shape, audit posture, country-policy overrides — all named in writing. - Vector store and audit-log retention scored - Model selection: latency, accuracy, BAA / DPA, exit cost - Country-policy override patterns reviewed ADRs Postgres vLLM Cloudflare Most engagements bundle two: a build (01, 02) paired with the discipline that keeps it audit-defensible (04, 05). Bring the shape closest to your blocker. [Scope your engagement →](https://www.kensink.com/contact) Want to see the K-Framework discipline behind every item? [Read the K-Framework](https://www.kensink.com/k-framework). \[THE STACK · BY LAYER\] ## Audit-grade infrastructure. Bank-defensible results. Tooling that has already cleared SOC 2 and bank security reviews. Self-host where regulator requires, BAA / DPA-backed SaaS where acceptable. LAYER · DATA + RETRIEVAL ### Data + retrieval. The store, the index, the search PostgreSQL pgvector Redis ClickHouse BigQuery OpenSearch LAYER · MODEL LAYER ### Model layer. Embeddings, providers, fallbacks OpenAI Anthropic Cohere Embed Voyage Llama (self-hosted) vLLM LAYER · EVAL + OBSERVABILITY ### Eval + observability. The eval bar, the cost meter, the drift alarm LangSmith Promptfoo OpenTelemetry Datadog Grafana Sentry LAYER · BACKEND + TRANSPORT ### Backend + transport. Type-safe everything TypeScript Next.js Python FastAPI gRPC BullMQ tRPC Zod LAYER · MOBILE ### Mobile. iOS + Android, native or cross React Native Expo Swift Kotlin FCM APNs LAYER · CLOUD + DEPLOYMENT ### Cloud + deployment. Whatever your infra already runs Cloudflare Workers Cloudflare R2 AWS GCP Vercel Fly ✕ WHAT WE DO NOT SHIP ### Direct against the model API. Audit log on every call. - ✕ No LangChain - ✕ No LlamaIndex - ✕ No agent framework - ✕ No orchestration vendor - ✕ No black-box ML platform \[ PROOF · WHAT THE STACK DELIVERS \] ## Numbers that survive a regulator review. MEASURED · WEIGHTED · 2024–2026 LATENCY p95 / 80 ms Fraud decision round-trip target AUDIT 100 % Decisions logged with feature attribution COMPLIANCE SOC 2 Stack default for production engagements REACH Multi Country policy overrides supported FINTECH AI · APPLIED K-FRAMEWORK ## Bring the money problem. We'll bring the audit trail. Eight weeks, fixed scope, eval suite + audit log at handoff. Direct LLM engineering on top of the K-Framework. Two Q3 slots remain. [Start a project →](https://www.kensink.com/contact) [Read the K-Framework](https://www.kensink.com/k-framework) CYCLE 8 weeks · problem to live OUTPUT Code · evals · audit trail DEPLOYMENT On-prem or BAA-backed