# AI for Fintech — Paiteq > Paiteq builds AI for fintech: KYC orchestration, fraud explainability, credit decisions, AML — sized to SR 11-7, PCI-DSS, EU AI Act before the first prompt. **HTML version:** https://www.paiteq.com/ai-for-fintech/ ## Key facts - Workflows: KYC orchestration, fraud explainability, credit decisions, AML. - Compliance: SR 11-7, PCI-DSS, EU AI Act considered before the first prompt. ## Related pages - [AI Agent Development](https://www.paiteq.com/services/ai-agent-development/) - [Machine Learning Development](https://www.paiteq.com/services/machine-learning-development/) - [AI Consulting](https://www.paiteq.com/services/ai-consulting/) ## About Paiteq Enterprise AI engineering — production agents, RAG, LLM apps, automation, generative AI. Eval-first, senior-led, fixed-scope engagements. Same-day reply from engineering. NDA counter-signed before discovery. Walk-away clause on every engagement. **Site index for agents:** https://www.paiteq.com/llms.txt **Full content for agents:** https://www.paiteq.com/llms-full.txt **Book a call:** https://www.paiteq.com/contact/ --- ## Full content AI for Fintech · AI Fintech Development Company # *AI for fintech* + AI fintech development, orchestrate AI inside your compliance framework, not around it. Fintech teams in 2026 sit in a three-way squeeze: funded competitors shipping AI features in 4–8 weeks, cost-of-fraud math that can't survive another year of analyst-only review, and regulator AI-readiness audits from the OCC, FRB, FCA, and MAS that ask for SR 11-7 inventories on the first visit. Paiteq is an AI fintech company doing AI fintech development inside your existing stack, KYC AI, AI AML, fraud decisioning, credit explainability, RegTech, treasury, AI compliance consulting, sized to SR 11-7, PCI-DSS, and EU AI Act Annex III obligations before the first prompt. We stay through the first eval-drift cycle, not the deploy. [Talk to engineering](/contact/) [See the 8 use cases](#use-cases) Use cases 8 · KYC · fraud · credit · treasury · compliance Engage MVP · Platform · Bank-grade Stack LangGraph · Claude · Pinecone · Snowflake · LangFuse Compliance SR 11-7 · PCI-DSS · EU AI Act Annex III 001 / WHY NOW ## Why fintech is evaluating AI for fintech from an AI fintech development company right now. Fintech founders and CTOs in 2026 face three pressures running in parallel: AI feature parity with funded competitors, cost-of-fraud math that needs an explanation layer to keep working, and regulator AI-readiness audits that are no longer hypothetical. Each pressure on its own would be manageable. Together, they're why AI for fintech has moved from R&D experiment to board-level agenda in the last 18 months, and why the AI in banking conversation now sits inside every bank-partner vendor evaluation we walk into. Every AI fintech company we talk to in 2026 is asking the same first-call question: what do we build first, and how do we ship it without tripping a regulator's first follow-up. 0 –8w Competitor AI feature cadence Funded fintech competitors shipping AI features in 4–8 weeks; pre-AI roadmaps fail bank-vendor evals. 0 –1.2% Cost-of-fraud target as % of GMV Mature fintech keeps loss + investigation under 0.4–1.2% of GMV, AI decisioning gets you there without analyst-team bloat. 0 –25 Regulator AI-readiness audits OCC, FRB, FCA, MAS all running AI-readiness audits in 2024–25. Vendor SR 11-7 inventory is no longer optional. PRESSURE 01 Cadence: 4–8 week competitor AI feature sprints Series C fintechs with $30M+ rounds are shipping AI features in 4–8 week sprints, embedded credit explainers, intelligent dispute triage, agent-driven onboarding, and the side-by-side bank-vendor evaluation goes badly if your product still looks pre-AI. We've watched a perfectly competent B2B payments platform lose a Tier 1 bank deal over a single missing AI feature the winning vendor shipped in seven weeks on Claude Sonnet 4.6 plus Pinecone. The category-defining moment for AI in fintech happened somewhere between the OCC's 2024 model-risk update and the EU AI Act's high-risk classification of credit scoring; trying to opt out isn't a strategy. The bottleneck isn't model capability, it's the eval framework, the regulatory documentation, and the integration surface against your existing payments and ledger systems. Those take 4–6 weeks to get right regardless of which model you pick. PRESSURE 02 Cost-of-fraud target: 0.4–1.2% of GMV is the gate Mature fintech keeps loss plus investigation cost under 0.4–1.2% of GMV. Below that, the unit economics work; above it, the CFO starts asking why fraud headcount grew 30% year-over-year. The analyst team reviewing flagged transactions burns 4–8 minutes per case reading Sift or Feedzai signals, fast enough at low volume, brutally slow at scale. The fintech ai fix is an explanation layer that compresses analyst-read-time from 4 minutes to 15 seconds without touching the fraud model itself. Chargeback rate stays flat or improves; false-positive friction drops 8–15% from better second-look decisions; analyst throughput goes 3.5–5× per shift. The unsexy part is that the explanation eval set has to be graded by your fraud analysts, not by a vendor's benchmark, that's the week-3 unblock most teams underestimate. PRESSURE 03 Regulator AI-readiness audits, SR 11-7 inventory is the entry ticket The OCC, FRB, FCA, and MAS are all running AI-readiness audits in 2024–25, every regulator that touches AI in banking is now asking the same first-pass questions. Examiners are asking for SR 11-7 model-risk inventories on the first visit, not the third. Ad-hoc model documentation, the kind most fintechs accumulated when ML was a side experiment, won't pass. Every AI feature that influences a customer-facing decision needs an inventory entry: scope, data lineage, monitoring plan, human-override pathway, performance thresholds, retraining cadence. Our AI fintech development engagements ship that inventory in parallel with the build, not as a retrofit at audit time. The bank partner's MRM team should be able to drop the entry into their existing system without translation, that's the bar. EU AI Act Annex III adds another layer for any feature touching credit scoring or financial services more broadly. We classify against the Act in architecture, not at security review. The opinionated take Most fintech AI projects fail because the team treats compliance as a separate workstream that runs after the build. Fintech that wins designs compliance INTO the architecture phase, SR 11-7 model-risk inventory before the first prompt, PCI-DSS scope analysis before vendor selection, EU AI Act Annex III classification before the first user touches it. The cost of bolting compliance on later is roughly 3× the cost of designing it in: the team rewires data flows, redoes the MRM documentation, and frequently rebuilds the eval harness to pass the audit. We don't get that number from theory. — Paiteq engineering 002 / USE CASES ## The 8 highest-ROI AI use cases in fintech. Below are the eight workflows we see fintech teams build first. They share three traits: each has a clear regulator-readable ROI number, each is deployable inside an 8–18 week window, and each compounds when you ship two or three together on shared infra rather than as standalone bets. The cards are dense on purpose, pain, with-AI workflow, named tools, and the ROI metric in the fintech buyer's vocabulary. Skim them, then read the two or three that match where your roadmap actually sits today. USE CASE 01 ### KYC AI orchestration and customer onboarding The Pain 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. With AI 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. 42–58% reduction in time-to-clear on edge cases 6–12 pp lift in onboarding completion at the KYC gate; full audit trail for examiners Tools LangGraphClaude Sonnet 4.6PersonaAlloyOnfidoRefinitivSnowflakedbt USE CASE 02 ### Fraud-decisioning explanation layer The Pain 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. With AI 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. 3.5–5× analyst throughput per shift Chargeback rate flat or down (no model change); false-positive friction 8–15% lower Tools Claude Sonnet 4.6SiftFeedzaiFICO FalconStripe RadarPineconeSnowflakeNeo4j USE CASE 03 ### Credit-decision explainability for SR 11-7 model risk The Pain 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. With AI 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. 75–88% time reduction per adverse-action letter SR 11-7 audit-pack documentation falls out for free; ECOA disclosure quality measurably improves Tools Claude Sonnet 4.6XGBoostLightGBMMLflowSHAPLangGraphVercel AI SDK USE CASE 04 ### RegTech: regulatory-document RAG and impact analysis The Pain 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. With AI 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. 40–60% reduction in time to first-pass impact analysis New-rule discovery from 4–8 weeks → 2–5 days; compliance officer coverage extends ~3× without headcount Tools PineconeTurbopufferCohere Rerank 3.5Claude Sonnet 4.6MintlifyLangfuse USE CASE 05 ### Treasury and reconciliation agents The Pain 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. With AI 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. 65–82% reduction in finance ops time-on-reconciliation Mean-time-to-detect-breaks compresses from 24–72 hours → 30–90 minutes; finance close 1.5–3 days faster Tools LangGraphClaude Sonnet 4.6Stripe TreasuryAdyenModern TreasurySnowflakedbt USE CASE 06 ### Customer-service deflection for fintech-specific queries The Pain 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. With AI 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. 38–52% T1 ticket deflection rate AHT compression 28–40% on escalated tickets; chargeback-SLA hit rate up 12–22% via faster dispute triage Tools Claude Sonnet 4.6PineconeStripeAdyenChargebacks911JusttLlama Guard USE CASE 07 ### Sales and account-management copilots for B2B fintech The Pain 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. With AI 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. 18–30% NRR lift on targeted-account cohort AM time-per-account compresses 35–50%; QBR no-show rate drops when the customer feels prepared-for Tools Claude Sonnet 4.6SalesforceHubSpotSnowflakeBigQueryCubePlaidModern Treasury USE CASE 08 ### Internal compliance copilot, the AI compliance consulting wrap The Pain 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. With AI 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. 75–90% same-day answer rate on routine compliance questions Compliance officer time freed for genuinely-novel reviews; engineers stop guessing on Reg T edges Tools PineconeCohere Rerank 3.5Claude Sonnet 4.6MintlifyLangfuse A pattern worth flagging across all eight AI for fintech workflows above: **the ROI numbers above are the median of what we and similarly-shaped agencies have shipped**, not the headline outlier. Don't pick a use case for its ceiling. Pick the two with the cleanest regulator-readable ROI math for your stage, Series B with a KYC drop-off problem starts with UC-1 and UC-6; bank-partnered lenders with adverse-action volume start with UC-3 and UC-4; B2B fintech at ~$80M ARR with finance-ops drag starts with UC-5 and UC-7. The next section maps each pain to the Paiteq service that does the actual engineering. 003 / SERVICE MAPPING ## How Paiteq services map to fintech needs. Four common fintech pain shapes on the left, five Paiteq service pillars on the right. Hover any pain row to highlight which services we'd engage; hover a service to reverse-highlight the pains it solves. The descriptive anchors (not the service primary keyword) are deliberate, what matters to you is the workflow, not the service title. AI feature parity pressure Funded fintech competitors ship AI features in 4–8 weeks; pre-AI roadmaps fail vendor evals. Cost-of-fraud compression Analyst team capacity caps the chargeback-vs-friction tradeoff; an AI explanation layer breaks the cap. Regulator AI-readiness audits OCC, FRB, and FCA examiners are asking for SR 11-7 inventories; ad-hoc model documentation won't pass. Reconciliation and treasury drag 6–12 ops hours per day on rec; breaks surface 24–72 hours late and finance close drifts. [ Service AI Agent Development designing autonomous agent systems ](/services/ai-agent-development/)[ Service RAG Development grounded retrieval over regulatory text ](/services/rag-development/)[ Service LLM Development model selection, fine-tuning, and evaluation ](/services/llm-development/)[ Service AI Workflow Automation stitching workflows across your payment and ledger systems ](/services/ai-workflow-automation/)[ Service AI Consulting AI strategy and roadmap advisory ](/services/ai-consulting/) Why the map looks like this Shipping AI in fintech is genuinely a multi-discipline engineering job in 2026, AI fintech development sits closer to embedded-systems work than to a typical SaaS feature build. Feature-parity pressure routes to three services because building a credit-explainability layer is partly [designing autonomous agent systems](/services/ai-agent-development/), partly [model selection, fine-tuning, and evaluation](/services/llm-development/), and partly drop-in integration into your existing credit-decisioning stack. The cost-of-fraud compression pain routes to agent work and [stitching workflows across your payment and ledger systems](/services/ai-workflow-automation/) because the fraud explanation layer has to read from the fraud model, the transaction graph, and your historical decision corpus, three systems, one workflow. Regulator AI-readiness routes to [AI strategy and roadmap advisory](/services/ai-consulting/) first because SR 11-7 readiness is genuinely a scoping problem before it's an engineering one, and to [model selection, fine-tuning, and evaluation](/services/llm-development/) for the base-model stability decisions that show up in MRM documentation. Reconciliation drag routes to agent work plus workflow because the source feeds are heterogeneous by definition. The discipline split isn't bureaucracy, it's how the engineering stays high-quality across an 18-week Platform build with three regulators watching. 004 / COMPLIANCE ## Compliance, model risk, and regulatory posture for fintech. Three regulatory layers shape every AI for fintech engagement we run. SR 11-7 is the Fed's model-risk guidance and the single biggest E-E-A-T moat in fintech AI. PCI-DSS is table stakes for anyone touching card flows. EU AI Act Annex III adds high-risk classification for credit-scoring and broader financial-services AI. We design within these layers in the architecture phase, not retrofit at security review. SOC-2-ready practices · Continuous monitoring - SR 11-7 Fed model-risk · MRM inventory aligned AUDITED · 2026 - PCI-DSS Card-data scope · tokenized references AUDITED · 2026 - EU AI Act Annex III High-risk classification · transparency tier READY Compliance is the gate, not a footnote Every bank-partner deal and every regulator visit runs an AI-readiness pass. AI features reopen controls your existing posture didn't pre-answer. "Where does the training data live?" "Is this an automated decision under EU AI Act Annex III?" "Show us the SR 11-7 inventory entry for this model." "Does the vector store sit inside PCI scope?" If your vendor improvises those answers at the audit, the bank partner pauses the rollout and the regulator schedules a follow-up. We've watched it happen. More than once. SR 11-7 SR 11-7 model-risk posture Every AI feature that influences a customer-facing decision gets a model-risk inventory entry from day one: scope of use, data lineage, monitoring plan, human-override pathway, performance thresholds, retraining cadence, and known limitations. The entry is pre-built for your MRM team to drop into their existing system without translation. We've found that bank-partnered fintechs underestimate how much of SR 11-7 readiness is documentation hygiene rather than model engineering, the model is usually fine; the documentation is usually missing or scattered across three Notion pages. The opinionated take here: SR 11-7 readiness is a scoping decision, not a retrofit. The first engagement week we spend on it pays back across every subsequent regulator visit for the life of the model. PCI-DSS PCI-DSS scope posture We design AI features to not touch raw card data. The orchestration layer sits at the metadata tier with tokenized references; vector stores never contain a PAN or CVV; LLM calls receive transaction context but not card primitives; observability traces redact PII at the logging layer. Secrets live in your existing PCI-scoped vault, not in a vendor's. The network-segment boundary stays where it already is. This isn't a compliance constraint we work around, it's the cleanest engineering shape anyway, because the AI workload doesn't need card data to do its job. Most fintechs we engage with already have a clean PCI-scoped environment; our job is to design the AI orchestration in a way that doesn't expand the scope. Scope creep at audit is the failure mode here, and it's preventable. EU AI ACT ANNEX III EU AI Act Annex III posture Credit scoring and broader financial-services AI are explicitly named in Annex III as high-risk systems, bigger regulatory weight than SaaS-side framing. We classify your feature against Annex III in the architecture phase. If high-risk applies, we ship the obligation stack: risk-management system, data-governance log, technical documentation, human-oversight surface, transparency disclosures, accuracy and robustness testing, cybersecurity controls. Sized to the obligation tier; not over-engineered. The honest take: most "EU AI Act compliant" marketing in fintech AI is fiction. The Act is the deployer's obligation, AI features change the data and decision flow inside it, and the vendor's job is to design within the obligation map, not invent a label that satisfies a procurement checkbox. 005 / ENGAGEMENT ## How a fintech AI engagement runs at Paiteq. Five phases. Every phase has an explicit deliverable, a named owner inside your team, and a gate criterion that has to pass before the next phase starts. The cadence is weekly: a Monday standup with your CTO, Head of Risk, Compliance lead, and Data lead. Demo every Thursday. Compliance documentation tracks in parallel from week 1, not as a retrofit. Fintech AI Engagement · 18 weeks (typical Platform tier) 5 phases WEEK 1–2 Discovery Use-case prioritisation, eval surface, stakeholder map (CTO + Head of Risk + Compliance + Data lead) Single regulator-readable ROI number scoped per use case WEEK 3–4 Architecture + Compliance Scoping Stack lock, SR 11-7 inventory entries drafted, PCI scope analysis, EU AI Act tier classification Architecture signed by your model-risk lead before any prompt is written WEEK 5–12 MVP Build Runnable agent against eval set + your real data, weekly demo, observability via Langfuse Baseline accuracy hit on eval set; SR 11-7 documentation tracking in parallel WEEK 13–18 Production + Audit Pack Hardening, fallback policies, rollout, complete MRM audit pack for the bank examiner All eval gates green; compliance lead signs off on transparency surfaces WEEK 19+ Optimise + Handoff Cost engineering, prompt iteration, runbook in your repo, eval-drift monitoring, ownership transfer Two cadence notes for fintech specifically The Head of Risk shows up week 1, not week 8. Half the use cases on this page, UC-1 KYC orchestration, UC-2 fraud explanation, UC-3 credit explainability, depend on decisions that are genuinely risk-team decisions, not engineering ones. We've found the first-week unblock is almost always getting the risk lead into the architecture conversation before the stack is locked, because changing the model or the data flow at week 4 to satisfy a risk concern costs 2–3× what it costs to design it in at week 1. The second cadence note: the bank-partner contact (if your fintech is bank-sponsored) joins around week 10, when the SR 11-7 documentation is real enough to review. Bringing them in earlier wastes their time; later means the inventory shape gets pushed back. The cadence is tuned to fintech leadership shape, not retrofitted from a generic services template. 006 / TEAM SHAPE ## Team shape for a fintech AI engagement. Two engagement shapes cover roughly 80% of the fintech AI work we run. MVP for a single high-clarity use case with the compliance scaffolding sized accordingly; Platform for the multi-use-case build on shared infra that most fintechs in the $30M–$200M revenue band actually need. Bank-grade tier (4 engineers, 3 ML engineers, 1 PM, plus compliance partner, 32+ weeks) sits behind these for org-wide AI orchestration with a full SR 11-7 inventory and audit-ready posture. MVP tier, one use case Platform tier, 3–5 use cases on shared infra Scope One use case front-to-back (e.g. UC-1 KYC orchestration or UC-2 fraud explanation) 3–5 use cases on shared infra plus compliance scoping Team shape 2 eng + 1 ML + 0.5 PM 3 eng + 2 ML + 1 PM Timeline 8–12 weeks 18–28 weeks Engagement shape 1 use case, 2 eng + 1 ML + 0.5 PM 3–5 use cases + compliance scoping, 3 eng + 2 ML + 1 PM The MVP shape is almost never the right starting point in fintech because the compliance scaffolding (SR 11-7 inventory, PCI scope analysis, EU AI Act classification) doesn't shrink proportionally, it's the same effort whether you ship one use case or four. **Platform tier is the median right answer** for fintech in the $30M–$200M revenue band. The Bank-grade tier (4 eng + 3 ML + 1 PM + compliance partner, 32+ weeks) only fits when the engagement is genuinely org-wide AI orchestration with a full SR 11-7 model-risk inventory and an audit-ready posture for a regulator visit. Specific engagement sizing comes out of the audit conversation. Eval framework Single eval set, 30–50 examples Shared eval harness across use cases, regression alarms in CI Observability Langfuse traces + cost dashboard Langfuse + Braintrust + per-agent SLO dashboards Stop-and-walk option Yes, fixed scope, real option to stop after week 8 Phased gates at weeks 4 / 10 / 18; can collapse to a single-use-case build mid-flight Click the indicative-range row for the take on which tier fits which revenue band. Bank-grade tier scoped separately on request. Sizing for KYC vs fraud vs credit workloads KYC orchestration (UC-1) and customer-service deflection (UC-6) tend to fit cleanly inside the MVP tier because the eval gate is narrower and the regulator surface is shallower. Fraud-explanation (UC-2), credit-explainability (UC-3), and RegTech RAG (UC-4) almost always need Platform tier because the eval harness, the SR 11-7 documentation, and the retrieval infra are the load-bearing pieces. We've seen more than one AI fintech company under-scope a credit-explainability build at MVP and lose 6–10 weeks rebuilding the MRM documentation pipeline mid-flight because the bank partner's review pass arrived earlier than expected. The cheapest tier isn't the cheapest outcome If you're shipping more than one AI use case in the next 12 months, and most fintech that get to a serious AI strategy will, the MVP tier asks you to rebuild the eval framework, the SR 11-7 documentation pipeline, and the observability stack twice. The second rebuild costs more than the first. Platform tier is the median right answer for fintech in the $30M–$200M revenue band because the shared infra (eval harness, retrieval layer, MRM documentation engine, observability via Langfuse, model routing) amortises across three to five use cases instead of one. The MVP tier exists for two real cases: pre-Series B fintechs testing whether AI fintech development pays back at all, and bank-partnered teams with a single high-clarity workflow they want to ship in 10 weeks before greenlighting the platform investment. Both are legitimate. Neither is most companies. 007 / WORK ## What we ship as an AI fintech company, three engagement shapes. Three anonymised fintech engagements from the broader team's history. Industry shape and segment are real; metrics are real; the numbers were measured at week 8–12 post-launch, not at deploy. Brand names removed under standard NDA. Anyone selling you headline outliers without the operating numbers under them is selling case-study theatre. Risk Series C consumer lending · DACH ### Credit-decision explainability + SR 11-7 inventory LightGBM credit model already in production; the lender's model-risk lead needed adverse-action letters in regulator-readable language and SR 11-7 documentation that could survive a Fed exam. We built the LLM drafting layer over SHAP feature importances, the templating engine for ECOA-compliant disclosures, and the MRM inventory entries the bank partner could drop into their existing system. Ship cadence was 14 weeks kickoff to first audit pack delivery. 0 % time reduction per adverse-action letter Finance Ops B2B payments fintech · NA · ~$80M ARR ### Reconciliation agent + treasury automation Reconciliation across Stripe, Adyen, Modern Treasury, two banking partners, and the internal ledger ate 9–11 ops hours daily. LangGraph orchestration over the six source feeds with break-resolution paths ranked by historical pattern. Finance ops approves; nothing auto-moves money. Finance close compressed from 7 days to 4. The team kept the same headcount; capacity went elsewhere. 0 % reduction in finance ops time-on-reconciliation Compliance Early-stage RegTech · UK ### Regulatory-document RAG with FCA + EBA coverage RAG over FCA handbook updates, EBA guidelines, and the client's internal compliance policy corpus, with Cohere Rerank 3.5 for retrieval quality and a routing layer for impact-analysis flagging. Compliance officer reviews every surfaced delta. New-rule discovery dropped from 6 weeks to 4 days. The eval set lived in the client's repo from week 3 and grew through production traces. Discovery lag 6w → 4d / first quarter post-launch The shape across all three engagements The regulator-readable ROI metric was scoped in week 2, before any code was written. The eval set grew during production via traces sampled monthly, not a static 50-example set left over from architecture. Handoff put the runbook in the client's repo, not in a shared doc. We engage as an ai fintech partner that stays through the first eval-drift cycle and the first regulator follow-up, not one that ships and disappears. Roughly half of the AI fintech company engagements we close convert to a lighter-weight Run engagement after the build is in production; half don't, because the client's internal team has picked up ownership. Both are fine. The Run engagement is real work, prompt iteration, cost engineering, regression testing on new model releases, regulator-driven documentation updates, not a retainer hiding as a service. FURTHER READING ## Where AI for fintech connects. The two highest-value fintech deployments we ship are [machine learning development services](/services/machine-learning-development/) (calibrated fraud + credit ML with regulator-defensible feature importance) and [RAG development services](/services/rag-development/) with citation enforcement on policy and compliance answers. Decision-heavy nodes (KYC orchestration, loan-doc triage, transaction-triage co-pilots) move to [AI agent development company](/services/ai-agent-development/) practice. The serving + observability layer underneath any deployed model lives in [MLOps services](/services/mlops/), with audit-trail discipline sized to SR 11-7. Customer-facing surfaces (servicing chat, dispute triage) ship as [chatbot development services](/services/chatbot-development/). Legacy-platform fintech buyers typically need [AI migration services](/services/ai-migration/) first to clear the rate-limiter. Strategic framing starts with [AI consulting services](/services/ai-consulting/). Founder context: [Navin Sharma](/team/navin-sharma/); broader [AI development company](/). 008 / FAQ ## Fintech AI buyer FAQ. Five questions we get on almost every AI for fintech first call, answered the way we'd answer them on the call. Specific numbers, named tools, the actual decision rules, not generic vendor-deck answers. How do you size an AI fintech build or an AI engagement on an existing product? Three shapes. An **MVP build of a single AI use case** runs 8–12 weeks with 2 engineers, 1 ML engineer, and 0.5 PM. A **Platform build covering 3–5 use cases plus compliance scoping** runs 18–28 weeks with 3 engineers, 2 ML engineers, and 1 PM. **Bank-grade engagements** with org-wide AI orchestration, a full SR 11-7 inventory, and an audit-ready posture run 32+ weeks with 4 engineers, 3 ML engineers, 1 PM, and a compliance partner. Fintech MVP runs heavier than SaaS-equivalent because the compliance scaffolding (SR 11-7 documentation, PCI scope analysis, EU AI Act classification) is table-stakes, not optional. Specific engagement sizing comes out of the audit conversation, [start there](/contact/), and we'd rather you walk away than mis-scope. Most AI fintech development work that ships well sits in the Platform tier. How do you handle SR 11-7 model risk for AI features in a bank context? SR 11-7 is the Fed's model-risk management guidance and it's the single biggest E-E-A-T moat in fintech AI. We treat every AI feature as a model that needs an inventory entry: scope of use, data lineage, monitoring plan, human-override pathway, performance thresholds, retraining cadence, and known limitations. The inventory entry is pre-built for your MRM team to drop into their existing system rather than something we hand them and expect them to translate. Concretely, that means an LLM-based adverse-action letter generator (UC-3) ships with a model card, a SHAP-anchored explanation layer, an eval harness with regression alarms, and a documented escalation path when confidence drops. The [AI strategy and roadmap advisory](/services/ai-consulting/) step in week 1 is where we map your specific examiner's asks, OCC vs FRB vs FCA frame this slightly differently, to the inventory shape. Build vs. buy: when does an in-house AI orchestration layer beat a fintech AI vendor? Buy when the AI feature is genuinely commodity, generic OCR, transcription, basic classification, and a hosted tool fits inside your PCI scope without surgery. Build the orchestration layer when the AI touches your **differentiated decisioning, your regulatory exposure, or your customer-facing risk surface**. Fraud-decisioning explanations (UC-2), credit explainability (UC-3), and the SR 11-7 documentation layer aren't workloads where a generic vendor's eval set predicts performance on your portfolio. We've watched fintechs over-buy at first, hit the second use case, and realise the vendor's data model doesn't extend; the rebuild costs more than the original platform build would have. In our experience, the right shape for B2B fintech AI is hosted models for inference, in-house for orchestration, eval, and observability. The [build-vs-buy framing](/services/ai-consulting/) belongs in architecture, not after the contract is signed. Which AI use cases have the highest ROI for B2B fintech? The four highest-ROI starting points we see in 2026 are: **KYC orchestration** (UC-1, 6–12 pp lift in onboarding completion at the gate, plus a cleaner audit trail), **fraud-decisioning explanation** (UC-2, 3.5–5× analyst throughput with no model change, chargeback rate flat), **credit-decision explainability for SR 11-7** (UC-3, 75–88% time reduction per adverse-action letter, audit pack for free), and **reconciliation agents** (UC-5, 65–82% finance ops time back, finance close 1.5–3 days faster). The selection rule we use: pick the two with the cleanest single-buyer ROI math and the lowest regulator surface, ship them on shared infra, and let eval data tell you which is next. Trying to ship five at once is how AI in fintech development stalls, and how the AI in fintech roadmap drifts a quarter, too many compliance reviews running in parallel. How long does it take to add AI features inside an existing PCI-DSS scope? A single AI feature that respects an existing PCI scope ships in **10–16 weeks** from kickoff if the orchestration layer sits at the metadata tier with tokenized references, meaning the vector store, the LLM calls, and the observability traces never see a raw PAN or CVV. We design that boundary in the architecture phase. Multi-feature builds with shared infra inside a PCI-DSS environment run 18–28 weeks because the access controls, secret management, and logging redaction need to be wired into your existing PCI-scoped vault and SIEM rather than spun up new. The bottleneck is rarely the AI work, it's usually the time it takes to coordinate the network-segment change-management review and the SIEM integration with your security team. We name those bottlenecks in week 2 of [grounded retrieval over regulatory text](/services/rag-development/) design so the timeline doesn't slip in production. 009 / START A FINTECH AI ENGAGEMENT ## Book a discovery call. We'll name the *two AI features that pass your next vendor eval* and quote a build window. No deck. Forty-five minutes with an engineering lead, your real product context on the table, and a follow-up memo within 48 hours scoping the MVP or Platform tier sized to your regulator's actual asks. [Talk to engineering](/contact/) [See the 8 use cases again](#use-cases) 010 / OTHER INDUSTRIES ## Adjacent industries we engage. Fintech sits next to three industries in our book where the AI build patterns rhyme, sometimes the workflow translates directly, sometimes the compliance layer changes the engineering. Brief signposts; full pillars land as each ships. [ INDUSTRY · SAAS AI for SaaS Sales agents, RAG copilots, churn prediction, embedded product AI. ](/ai-for-saas/)[ INDUSTRY · ECOMMERCE AI for Ecommerce Catalog enrichment, conversion-side search, recommendations. ](/ai-for-ecommerce/)