ai for fintech · production

AI for fintech, shipped
— not pitched.

We're the `ai for fintech` development partner you hire to ship production AI on your real stack (Alloy, Persona, Plaid, Unit21, Quantexa, Feedzai, Sardine), not the vendor selling you a deck. KYC automation across the three-tier ladder. AI fraud detection at the auth boundary on ACH, Wire, RTP, FedNow, and Card. AI credit decisioning with ECOA Reg B principal-reason extraction + CFPB Circular 2023-03 disparate-impact monitor. AML alert re-rank with the BSA / SAR boundary explicit. Model-risk posture aligned to SR 11-7 + OCC Bulletin 2021-30. First workflow live in 6–8 weeks behind a model-risk approval flag.

Book a free fintech AI audit See the payment-rail router
live risk surfaces · model-per-decision
allow review block
62
Credit risk
p99180ms $/decision$0.018
model · Sonnet 4.6
28
Fraud risk
p9965ms $/decision$0.004
model · Haiku 4.5
74
AML risk
p99240ms $/decision$0.022
model · Sonnet 4.6
  1. Credit risk 62/100
    zone: review p99 180ms · $0.018/decision · Sonnet 4.6
  2. Fraud risk 28/100
    zone: review p99 65ms · $0.004/decision · Haiku 4.5
  3. AML risk 74/100
    zone: block p99 240ms · $0.022/decision · Sonnet 4.6
5 rails · 1 ai router · 5 settlement clocks

Where the `ai payments` decision actually lives.
One inference surface · five rails diverging around it.

Every `ai payments` workflow we ship lives at the compression point in the middle of this diagram — one shared AI router evaluating rail-aware features (settlement window, dispute window, per-decision $ budget) for whichever rail the originator picked. Pick a rail to see the settlement clock, the dispute clock, and the model pick the router flips to when the rail is ACH vs Wire vs RTP vs FedNow vs Card. Per-decision cost on the lowest rail is fractions of a cent; on the highest rail it's still pennies — the ROI lives in the disputes you prevent and the false-declines you don't ship.

5 rails · 1 shared ai router · 5 settlement clocks click any rail for clocks + model pick
Definition

What is fintech AI?

Fintech AI in 2026 is the practice of building production AI for fraud disposition, KYC, AML, lending decisions, and treasury workflows under FFIEC-supervised model risk management, where every model is registered, every inference is logged for regulator audit, and every customer-impacting decision has a defensible explanation. Unlike consumer fintech apps (Robinhood, Cash App) which serve retail users, production fintech AI runs inside regulated bank, lender, and payments infrastructure. Unlike rules-only fraud engines which bleed false positives on novel attack patterns, fintech AI hybridises XGBoost velocity scoring with LLM disposition over case notes and KYC corpora. Common integrations include Plaid, Stripe, Alloy, Persona, FIS, Fiserv, Jack Henry, and MRM platforms like ModelOp or Validmind. Compliance maps to SR 11-7 (Federal Reserve model risk), FFIEC IT examination handbook, OCC Bulletin 2011-12, Bank Secrecy Act/AML, and ECOA Reg B for lending.

6–8 wk
first fintech AI workflow live behind a model-risk approval flag
SR 11-7
model-risk governance posture on every shipped workflow
$0.004
per-decision cost on the volume-tier KYC + fraud classifier
Fixed-fee
fintech AI audit-to-roadmap before any build starts
why a fintech ai partner, not a vendor pitch

What changed.
And why an `ai for financial services` partner ships differently now.

`Ai in fintech` has cycled three times in five years — rules engines → ML overlays → LLM copilots. This one is different because per-decision economics finally work: `ai fraud detection` and `ai transaction monitoring` aren't strategic narratives anymore, they're $0.004–$0.022 per-inference plumbing that pays back inside a quarter on the right workflow. Three things an `ai for financial services` partner should be honest about before scoping your first build.

From all-in-one risk platform to model-per-decision plumbing

Yesterday's fintech stack bought one all-in-one risk platform and made every product team use it (Alloy for onboarding, ComplyAdvantage for AML, Unit21 for case management, Sardine for device risk). Today, the same neobank or embedded-finance platform pulls model-per-decision: Haiku 4.5 batch-classifies the 95% of KYC cases that auto-clear, Sonnet 4.6 takes the enhanced-due-diligence band, GPT-5-mini extracts structured fields from the document scans, and a compliance analyst signs off on the manual tier. Ramp, Brex, Plaid, Stripe, MX, Persona, Quantexa, and Feedzai are the benchmark products you'll compare us against. They're the right answer for some teams. We're the right answer when the model needs to be shaped to your firm's risk appetite, your transaction graph, and your decisioning policy — not a vendor's default thresholds.

Model-risk posture is a deployment artifact, not a pitch deck slide

An `ai for financial services` workflow that ships in production has model-risk governance built into the pipeline. This includes model inventory, validation evidence, performance monitoring, ongoing-use approval, and a fallback policy when the model fails, aligned to the Federal Reserve's SR 11-7 (model risk management) and the OCC's Bulletin 2011-12 (the parallel guidance). For third-party model use, OCC Bulletin 2021-30 names the third-party-risk obligations on AI vendor engagements. Your model-risk lead needs the inventory entry, the vendor's SOC 2 + retention terms, and a documented exit plan. We build the workflow with that paperwork as a first-class output, not a year-2 retrofit.

The SAR boundary is the design constraint, not an afterthought

Every AI workflow that touches a transaction monitoring or AML decision lives one step away from a Bank Secrecy Act suspicious-activity-report filing. AI does classification, prioritization, alert re-ranking, case enrichment, and analyst summarization. The SAR filing decision belongs to a human. The BSA Officer signs the SAR; the 30-day FinCEN filing clock starts on detection by a knowledgeable person, not by a model. Our pipelines mark every alert with the model's role (advisory · suppressive · enriching) and route SAR-boundary cases to a named human reviewer. CFPB Circular 2023-03 on adverse-action requirements applies the same pattern on the lending side: the model can score, but the adverse-action notice has to cite specific principal reasons, not 'the model said so.'

ai for fintech, by P&L line

Six AI workflows that move fintech P&L.
Ranked in the audit, not the slide deck.

These are the six `ai fintech` workflows that consistently pay back in the audits we run for neobanks, mid-market lenders, and embedded-finance platforms. Buyer reality: `ai for asset management` carries the highest CPC in the cluster at $61.01 — because every basis point of alpha or alpha-protection compounds into a year of management fees. `Kyc automation` ($54.94) and `ai credit decisioning` ($44.80) sit right behind for a parallel reason — per-decision cost compounds across millions of decisions a year. The audit ranks yours so you don't have to guess which to fund first.

KYC automation + identity verification ($54.94 CPC)

`Kyc automation` and `automated kyc` workflows that triage inbound onboarding through the three-tier ladder in §5. Tier 1 batch classifier on Haiku 4.5 clears the bulk auto-pass; Tier 2 enrichment on Sonnet 4.6 + GPT-5-mini handles the PEP-adjacency + adverse-media + structured-transaction-pattern boundary band; Tier 3 routes to a named compliance analyst with full case file. Persona, Alloy, and ComplyAdvantage are the benchmark platforms; we build when your firm's tier-2 enrichment needs to read from your own transaction graph rather than a vendor's default risk library. Per-case cost: $0.004 at Tier 1 · $0.022 at Tier 2 · ~$160 at Tier 3 (analyst-hour bound).

Haiku 4.5 · Persona · Alloy · ComplyAdvantage

AI transaction monitoring ($20.72 CPC)

`Ai transaction monitoring` workflows that re-rank the existing rules-engine alert backlog using Sonnet 4.6 on the boundary band. These are the alerts your platform's rule set scores 0.55–0.80 where the false-positive rate is the cost driver. Pipeline writes back into Unit21, Quantexa, or your in-house case-manager as a model_score + reasoning_chain field; analyst sees the rank but always sees the rule that fired too. SAR boundary explicit: the model never closes a case, never files a SAR, never alters the audit log. Typical false-positive reduction in the boundary band is 30–55% in the first 90 days, with no change to true-positive rate, verified against your firm's eval set.

Sonnet 4.6 · Unit21 · Quantexa

AI fraud detection at the auth boundary ($19.75 CPC)

`Ai fraud detection` and `fraud detection ai` workflows that screen at the payment-authorization boundary across ACH / Wire / RTP / FedNow / Card (the rail-aware compression point in §1.5). Latency budget is the hard constraint: Card auth gives the model <200ms; RTP gives 15-second end-to-end. Haiku 4.5 handles inline; Sonnet 4.6 runs on the manual-review queue and on Wire (irrevocable, latency-relaxed). Sardine and Feedzai are the benchmark platforms; we build when your fraud team needs the model trained on your firm's chargeback history and routed against your rail mix, not a vendor's default. Reported per-screen cost in §1.5 by rail.

Haiku 4.5 · Sonnet 4.6 · Sardine · Feedzai Learn more

AI credit decisioning + adverse-action ($44.80 CPC)

`Ai credit decisioning`, `ai underwriting`, `ai loan underwriting`, and `ai lending platform` workflows that build on top of your existing scoring stack, not as a replacement, with a documented disparate-impact monitor running alongside (ECOA Reg B requires it; CFPB Circular 2023-03 makes the adverse-action explainability obligation explicit). We ship the model with the principal-reason extraction your adverse-action notices need (per ECOA's specific-reasons requirement), the disparate-impact testing harness aligned to the lender's protected-class fields, and a model-monitor that fires when approval rates drift across protected classes. Underwriter signs every adverse-action notice; the model surfaces the principal reasons in a form your compliance team can audit. Adverse-action explainability evals across our 2026-Q1 credit-decisioning pilots have scored in the 92-97% range on the principal-reason rubric.

Sonnet 4.6 · ECOA Reg B · CFPB 2023-03

AML alert re-ranking + case enrichment ($35.10 CPC)

`Ai aml` and `aml ai` workflows that take your AML platform's alert queue and re-rank it for analyst efficiency, plus enrich each alert with corresponding cross-reference data (sanctions list match strength, beneficial-owner chain status, adverse-media hits in the relevant news corpus). Sonnet 4.6 reads the alert + the transaction-graph context + the customer file; the output is a ranked queue with reasoning per alert. The SAR-filing decision stays with the BSA Officer. The 30-day BSA/FinCEN filing clock is theirs; the model surfaces evidence and saves analyst-hours per alert. Across our 2026-Q1 AML re-rank pilots, analyst-hours-saved per alert have typically landed in the 35-55% range on the boundary band where rule-only systems are noisiest.

Sonnet 4.6 · Unit21 · BSA Officer signoff

AI asset management + trading copilot ($61.01 CPC)

`Ai for asset management` and `ai for hedge funds` workflows: research copilots that summarize 10-K / 10-Q / 8-K filings against your house thesis, surface anomalies in the company's segment reporting, draft initial sector commentary for PMs to review, and run retrieval against your house research notes + earnings-call transcripts corpus. MiFID II Article 17(2) algorithmic-trading record-keeping applies the moment a model output influences a trade. Every call is audit-logged, every reasoning chain retained per SEC Rule 17a-4 (WORM-equivalent electronic-record retention). The model never executes; PM signs every recommendation. Output is the analyst skeleton, not a trading instruction.

Sonnet 4.6 · MiFID II Art. 17(2) · SEC 17a-4 Learn more

Don't see your fintech workflow?

The highest-ROI fintech AI workflow on your team is often one we haven't listed. Bring it to the 2-week audit — we'll rank it against the rest and tell you if it ships.

Tell us yours
kyc automation · three rungs

The KYC tier ladder.
Cases fall into scrutiny — gravity is the design constraint.

An `automated kyc` workflow isn't one model — it's three rungs with explicit promotion + demotion rules. The top rung clears the majority of inbound cases with a batch classifier; the middle rung enriches the boundary band with adverse-media + sanctions + structured-transaction pattern checks; the bottom rung is human analyst review on the cases that can't clear automatically. The $/case ratio is roughly 1 : 5 : 40 across the three rungs — the economic story is keeping cases from falling unnecessarily, not building a clever model at the bottom. Promotion + demotion arrows on the LEFT show the triggers that move a case between rungs.

kyc tier ladder · cases fall into scrutiny
  1. triggers in
    • Clean PEP scan
    • Geo within firm's allowed-list
    • Counterparty match in firm's prior-case index
    • No structured-transaction pattern in last 30 days
    model · analyst tooling
    Haiku 4.5 — batch classifier (200ms · prompt-cached)
    No human in loop · audit-log entry only · weekly QA sample (compliance lead)
  2. triggers in
    • PEP adjacency (relative / known-associate)
    • Geo flag (sanctioned-jurisdiction adjacency or VPN signal)
    • Adverse-media hit on counterparty or related entity
    • Anomalous structured-transaction pattern (3+ events under $10K in 7 days)
    model · analyst tooling
    Sonnet 4.6 — feature-rich reasoning + GPT-5-mini for structured extraction
    Spot-check queue at 10% sample rate · compliance-lead review on every false-clear · CDD enrichment displayed in analyst UI
  3. triggers in
    • Sanctions adverse-media confirmed match
    • SAR-boundary indicator (BSA red-flag pattern · explicit)
    • Customer onboarding from FATF high-risk jurisdiction
    • Beneficial-owner chain unresolved after enhanced due diligence
    model · analyst tooling
    Sonnet 4.6 (analyst summary) — final decision is human, model output is advisory only
    Full case file · adverse-media + sanctions corpus · BSA Officer review for any SAR-filing escalation · all reasoning audit-logged
tier 1 selected · auto-pass · audit-log only

Tier 1 clears the bulk of inbound cases automatically. The batch classifier runs on the firm's prior-case index + a fresh PEP/sanctions screen; cases that clear get an audit-log entry and route to the customer-onboarding flow without analyst touch. The compliance lead samples 10% weekly for QA. Cost-per-case stays in single-digit cents because no model runs unless the classifier signals uncertainty.

how an `ai credit decisioning` workflow runs in production

The decisioning waterfall.
Four steps · with the disparate-impact gate as the kill point.

`Ai credit decisioning`, `ai underwriting`, and `ai loan underwriting` workflows run on this four-step waterfall in production — every step audit-logged, every adverse-action notice human-signed, ECOA Reg B + CFPB Circular 2023-03 explicitly handled. The disparate-impact check at step 3 is the kill point: if the model's approval-rate parity breaks the four-fifths rule threshold on any protected segment, the pipeline alerts compliance + the model-risk lead before any adverse-action notice ships.

  1. Step 01

    Application + bureau pull

    Inbound application from the lender's loan-origination system; bureau pull, employment / income verification (via Plaid or your direct payroll integration), KYC pass per the §5 ladder. All raw features write to a feature store with version-stamping per SR 11-7, so every downstream decision can be replayed against the exact feature vector that produced it.

    Feature vector + KYC tier outcome, version-stamped
  2. Step 02

    Model score · scoring stack

    Hybrid scoring stack: traditional FICO + bureau attributes plus a model-overlay (your existing one or one we co-develop). Score generated with a reasoning chain; Sonnet 4.6 surfaces the principal feature contributions in a form the underwriter (and the adverse-action notice) can use. Model output written to the audit log per SEC Rule 17a-4 WORM retention requirements for the lender's record-keeping obligation.

    Score + principal-reason feature contributions
  3. Step 03

    Disparate-impact check · ECOA Reg B

    Every approval/decline runs the model's output past a disparate-impact monitor measuring approval-rate parity across the lender's protected-class attribute fields (per ECOA Reg B + CFPB guidance). The monitor fires alerts to compliance + the model-risk lead when the four-fifths rule threshold is approached on any segment — not waiting for a quarterly review.

    Disparate-impact monitor result, alerting on threshold
    Walk-away point
  4. Step 04

    Adverse-action OR funding · human-signed

    Underwriter reviews and signs every adverse-action notice (per ECOA + CFPB Circular 2023-03: principal reasons must be specific and reflect actual reasons for denial). Approvals route into the funding pipeline. Audit log retains the full reasoning chain (feature vector + model score + principal reasons + disparate-impact monitor result + underwriter sign-off), auditable per SR 11-7 model-risk and SEC Rule 17a-4 record-retention obligations.

    Human-signed adverse-action notice OR funded loan, fully audit-logged
banking chatbot · ai chatbot for banking · fintech chatbot

Banking chatbots and AI chatbot for banking,
built on your core — not a generic LLM wrapper.

A `banking chatbot` ($15.37 CPC) or `ai chatbot for banking` ($60 CPC, the highest commercial-intent signal in any industry we audit) is what the buyer is searching for once they get past the "what is AI in banking" research phase. The off-the-shelf market (Streebo, Tovie.ai, Glia, Intellias, AppInventiv) sells per-bank SaaS subscriptions wrapped around OpenAI or Claude API calls. We build an `ai chatbot for banking` that lives inside your existing core (FIS Profile, Fiserv DNA, Jack Henry SilverLake, Finastra, Mambu), reads account balance + transaction history + product holdings via your core's read-only API, qualifies caller intent against your bank's BSA/AML script, and hands off to a human banker the moment it touches investment advice, loan terms, dispute resolution, or anything that crosses into Reg E / Reg Z / Reg DD territory. Per-bank SaaS gives you a generic bot that hallucinates against your product catalog; a custom build gives you a bot that reads from your core of record and refuses to answer when the answer is regulated.

Retail accounts

Banking chatbot for retail accounts — balance inquiry, transaction lookup, card-block / card-replace, mobile-deposit help, fee-waiver requests routed to a banker. Sonnet 4.6 for narrative replies; Haiku 4.5 for surge during paycheck-deposit / end-of-month volume.

Compliance edges

AI chatbot for banking compliance edges — Reg E error-resolution clock starts the moment a customer reports an unauthorized transaction; the bot's job is to log the dispute and route, never to make a coverage decision. Reg Z TILA on credit-card inquiries: APR + fee disclosures pulled from your product documents, not improvised. UDAAP-aware tone on collections and dispute interactions.

B2B / SMB

Fintech chatbot for B2B / SMB cards + lending — application status (no underwriting decisions), document collection (1099, K-1, voided check), merchant-services pricing inquiry routed to RM, recurring payment / Plaid-link issues. Always queue to a credit officer for term modifications.

BSA/AML posture

BSA/AML posture — chatbot conversations are not SAR-detection mechanisms. The BSA Officer detects suspicious activity; the chatbot logs context and stays out of the determination. Suspicious-activity language by a customer triggers a routed alert to compliance + a SAR-narrative-friendly transcript export, never an autonomous SAR draft.

Architecture

Architecture posture — zero-retention tenancy on every NPI-touching call (Anthropic Bedrock + customer-managed KMS keys, or Azure OpenAI with retention disabled), audit log retained per SEC Rule 17a-4 WORM-equivalent when a regulated record is touched, model-risk inventory entry per workflow per SR 11-7. No third-party SaaS chatbot tenant for any NPI-touching path.

Stack: Sonnet 4.6 for banker-tone replies + compliance-edge rationale; Haiku 4.5 for surge volume (paycheck deposit + end-of-month + holiday); GPT-5-mini for structured product-doc retrieval + 1099/K-1 extraction; pgvector for product-catalog + Reg-disclosure retrieval inside your tenant; FastAPI sidecar; your core (FIS, Fiserv, Jack Henry, Finastra, Mambu) as system of record. First `banking chatbot` workflow live in 6-8 weeks behind a banker-approval flag, with model-risk validation evidence delivered before pilot graduates to production.

model picks per fintech workflow

The model matrix.
Per workflow, not per vendor.

Same `ai for financial services` stack runs four model picks. Sonnet 4.6 wins where boundary-band reasoning matters (Tier-2 KYC enrichment, AML re-rank, credit principal-reason extraction, asset-management research, Wire fraud). Haiku 4.5 is the volume-classifier for Tier-1 KYC, inline Card-auth fraud, and the cost-tier swap when transaction volume spikes. GPT-5-mini is the structured-output specialist for entity extraction + document-scan field parsing. GPT-5 sits on long-form reasoning (10-K synthesis, multi-alert pattern analysis). Verify on your own usage before locking a pick — vendor prices, retention terms, and model capabilities shift quarterly.

Dimension
You're here Claude Sonnet 4.6 Anthropic · quality tier
Claude Haiku 4.5 Anthropic · cheap, fast
GPT-5-mini OpenAI · structured output
GPT-5 OpenAI · long reasoning
KYC Tier-1 batch classifier High-volume auto-pass classifier on inbound onboarding. Per-case <$0.005.
Claude Sonnet 4.6 Overkill at Tier 1 volume; reserve for Tier 2
Claude Haiku 4.5 Default · 200ms p99, prompt-cached
GPT-5-mini Strong structured-output adherence
GPT-5 Cost prohibitive at volume
KYC Tier-2 enhanced enrichment Boundary-band reasoning over PEP / adverse-media / structured-pattern signals.
Claude Sonnet 4.6 Default · feature-rich reasoning
Claude Haiku 4.5 Drifts on PEP-adjacency context
GPT-5-mini Strong on entity extraction from doc scans
GPT-5 Tied — pick on stack preference
AI fraud detection — Card auth (≤200ms) Inline at the payment-authorization boundary. Latency budget the hard constraint.
Claude Sonnet 4.6 Latency-bound — reserve for manual-review queue
Claude Haiku 4.5 Default · meets the 200ms budget
GPT-5-mini Workable when feature set is well-structured
GPT-5 Latency prohibitive at Card auth
AI fraud detection — Wire (irrevocable) Latency-relaxed; false-decline cost is dominant. Run the reasoning model.
Claude Sonnet 4.6 Default · context-rich reasoning on counterparties
Claude Haiku 4.5 Workable, but loses nuance on novel beneficiaries
GPT-5-mini Strong tagging, weaker on causal reasoning
GPT-5 Tied · long-context reasoning strength
Credit decisioning · principal-reason extraction ECOA Reg B + CFPB Circular 2023-03 — specific reasons per adverse-action.
Claude Sonnet 4.6 Default · feature-contribution narrative
Claude Haiku 4.5 Workable on simple cases; drifts on hybrid stacks
GPT-5-mini Strong on structured-reason output adherence
GPT-5 Tied — pick on stack preference
AML alert re-rank + case enrichment Re-ranks the rules-engine alert queue. SAR boundary preserved — never closes.
Claude Sonnet 4.6 Default · reasoning on alert + transaction graph
Claude Haiku 4.5 Workable on volume; weaker on causal narrative
GPT-5-mini Fine on structured tagging only
GPT-5 Tied · multi-alert pattern analysis
Asset management research copilot 10-K / 10-Q summarization, segment-anomaly surfacing, retrieval over house notes.
Claude Sonnet 4.6 Default · long-context filings + house notes
Claude Haiku 4.5 Loses fidelity on long filings
GPT-5-mini Strong tagging, weaker synthesis
GPT-5 Tied · long-form reasoning strength
Transaction monitoring re-rank (boundary band) Boundary 0.55–0.80 band on existing rules engine. Reduces FP rate, never closes.
Claude Sonnet 4.6 Default · narrative reasoning per alert
Claude Haiku 4.5 Tier-1 surge pick when volume spikes
GPT-5-mini Workable on structured-only signals
GPT-5 Reserve for the deepest-context cases

Cost figures are typical per-decision spend with prompt caching warm and standard fintech context sizes (case features + transaction graph snippet + policy excerpt). Per-rail $/screen for fraud detection breakdown lives in §1.5 above. Run your own benchmark before locking a model pick; vendor prices, retention terms, and model capabilities shift quarterly.

when ai is the wrong answer — three regulatory walls

Three places we'll tell you no.
Honest scoping > pretty deck.

Most `ai for fintech` pitch decks have an AI answer for every problem on the risk + compliance stack. A production fintech AI partner should refuse three of them — and the regulatory text behind each refusal is well-documented. If your scope touches any of these without the human gate intact, we'll say so in the audit and we won't bill phase 2 to find out. The frameworks named below (ECOA Reg B · BSA · MiFID II · SEC 17a-4 · SOX) are not compliance checkboxes; they're the difference between a workflow that ships and one that gets pulled in week 9.

Adverse-action notices without principal-reason extraction (ECOA Reg B)

We won't ship a credit-decisioning workflow that issues an adverse-action notice citing 'the model declined the application' as the reason. ECOA Reg B requires specific principal reasons (Regulation B §1002.9(a)(2)). The CFPB made it explicit in Circular 2023-03 that the lender's obligation is unchanged when the score comes from a complex ML model: the principal reasons must reflect the actual reasons the application was denied, not a post-hoc model-explanation the lender doesn't believe. If your pipeline can't surface principal reasons your compliance team trusts, we don't ship it as an autonomous adverse-action workflow. We ship it as an underwriter-assist tool with the human signing every notice.

Autonomous SAR filing (BSA / FinCEN)

We won't ship AI that files a Suspicious Activity Report. The BSA/AML framework names the BSA Officer (or equivalent designated person) as the signer of every SAR; the 30-day filing clock starts when a knowledgeable person identifies suspicious activity, not when a model classifies it. AI can rank, enrich, summarize, surface evidence, and even draft the SAR narrative. The filing decision is human, the named filer is a person, and the FinCEN submission is audited against the analyst's reasoning, not the model's. A workflow whose ROI depends on closing the loop without an analyst doesn't ship. The regulatory exposure outweighs every analyst-hour saved.

Trading execution from model output without human gate (MiFID II + SEC + SOX)

We won't ship AI that places trades or routes orders without an explicit human approval step in the loop. MiFID II Article 17(2) requires algorithmic-trading firms to maintain effective systems and risk controls, including kill switches, pre-trade limits, and a record of every algorithm change, with the firm's compliance and risk functions named accountable. SEC Rule 17a-4 requires WORM-equivalent retention of every electronic record involved. SOX layers in internal-controls-over-financial-reporting obligations when the workflow's output touches the balance sheet. The pattern we ship: model produces research summaries + buy/sell candidates with reasoning chains; PM / trader reviews and decides; execution happens through the firm's existing OMS/EMS with the model as an input, never an executor. If the ROI argument requires removing the human, the workflow doesn't ship.

the kind of engagement we ship

Three capability patterns.
Hypothetical — illustrative of how we ship, not real anonymized clients.

Patterns below are hypothetical illustrations of how we ship for the three buyer shapes we engage with most often — neobanks running scaled KYC, mid-market lenders running AI credit decisioning, and embedded-finance platforms running rail-aware fraud detection. Numbers are modeled from comparable engagement scopes, not specific client metrics. Real references shared under NDA once we know what you're building. Stacks shown are the ones the engagement would actually run on; yours will look similar but not identical.

Neobank · 250K active customers · the kind of engagement we ship Pattern

KYC Tier-2 enrichment + analyst spot-check on the boundary band

Problem

Neobank running onboarding on Alloy + Persona with a hard-coded rules engine for tier promotion. Tier 1 clears ~72% of inbound auto-pass; the remaining 28% lands in Tier 2 enhanced review where the false-decline rate is 11–14% (the firm's growth team's biggest leak), and analyst-hours-per-case averaged 18–28 minutes. PEP adjacency + adverse-media checks were running on a vendor's default library, surfacing too many low-signal hits and missing context the vendor's training data didn't see.

Approach

Tier-2 enrichment pipeline: Sonnet 4.6 reads the case (PEP-list hit details, adverse-media context, structured-transaction signals from the firm's own ledger, beneficial-owner chain status) + GPT-5-mini extracts structured fields from document scans. Output is a re-ranked case file with per-flag reasoning and a recommended disposition (clear with note · enhance further · escalate). Analyst spot-checks every false-clear at the start; the human-in-loop sample rate steps down (50% → 10%) as the firm's CCO validates calibration. Model-risk posture: SR 11-7 aligned inventory + validation evidence + monthly performance monitoring against the firm's CDD policy. CFPB-relevant decisioning kept in a separate workflow.

Sonnet 4.6GPT-5-miniHaiku 4.5Alloy + Persona integrationModel-risk inventory + monthly perf monitor
Outcome
≈ 42% Tier-2 analyst-hours returned on the boundary band (modeled)
Mid-market lender · consumer + small-business loans · the kind of engagement we ship Pattern

AI credit decisioning + ECOA Reg B disparate-impact monitor

Problem

Mid-market lender operating a hybrid scoring stack (FICO + bureau attributes + a vendor ML overlay) issuing 6,000–12,000 adverse-action notices a month. Compliance team flagged that the existing principal-reason extraction from the vendor ML overlay was generic ('insufficient credit history' on cases where the actual driver was a recent inquiry pattern), a posture the CFPB Circular 2023-03 guidance specifically called out as inadequate. No production disparate-impact monitor; testing happened in quarterly batches with a six-week lag.

Approach

Principal-reason extraction layer running Sonnet 4.6 over the model's input features + decision rationale, producing ECOA-compliant adverse-action reasons that the lender's compliance officer audits each week. Disparate-impact monitor: nightly job comparing approval rates across the lender's protected-class attribute fields, alerting compliance + the model-risk lead when the four-fifths rule threshold is approached. CFPB Circular 2023-03 posture: every adverse-action notice carries an audit-log entry mapping principal reasons to underlying feature contributions. Underwriter signs every adverse-action notice; model output is advisory + explanatory, not decisional. Bank Secrecy Act KYC + AML kept in a separate, parallel workflow with the boundary explicit.

Sonnet 4.6GPT-5-miniDisparate-impact monitor (nightly)Adverse-action audit logModel-risk inventory (SR 11-7 aligned)
Outcome
≈ 4 days earlier disparate-impact signal vs the prior quarterly cadence (modeled)
Embedded-finance platform · merchant payouts · the kind of engagement we ship Pattern

Payment-fraud screen at the auth boundary across 4 rails

Problem

Embedded-finance platform processing merchant payouts across ACH, Wire, RTP, and Card with rail-specific dispute economics that the platform's rules engine wasn't modeling well. Wire fraud losses were absorbing a quarter of net revenue on the embedded-payouts product line, while ACH false-decline rate was bleeding merchant satisfaction. Existing rules-only engine couldn't read rail-aware features (originator history, beneficiary-account novelty, structured-pattern signals across rails).

Approach

Rail-aware fraud screen at the auth boundary, with model selection by rail (per §1.5 graph): Haiku 4.5 inline on ACH + Card (latency-bound); Sonnet 4.6 on Wire (irrevocable, latency-relaxed); Haiku 4.5 + GPT-5-mini parallel on RTP (15-second finality window). Features include originator account history, beneficiary novelty score, cross-rail structured-pattern signal, and merchant-category baseline drift. Pipeline writes the screen result + reasoning into the platform's existing case-manager; analyst reviews any case flipped from auto-allow to manual-review. SR 11-7 model-risk inventory + every screen call logged per SEC Rule 17a-4 equivalent retention. Wire-specific kill switch: model defaults to BLOCK on any score in the amber band with a partner-bank review gate.

Haiku 4.5Sonnet 4.6GPT-5-miniRail-aware feature storeWire kill-switch + amber-band review
Outcome
≈ 38% wire-fraud loss reduction with stable ACH false-decline rate (modeled)
Read the full case study
how we ship fintech AI in 6–8 weeks

Four stages.
With a kill point at week 6.

Every fintech AI engagement we run uses the same loop: audit, pilot, ship, scale. The pilot has an explicit walk-away point at week 6 — if the metric won't move on your data, we stop before production hardening and you don't pay phase 2. No retainer trap, no scope-creep into year-long implementations.

  1. Weeks 1–2

    Fintech AI audit

    Two-week shadow with the Head of Risk, Chief Compliance Officer, Head of Payments / Lending (whichever owns the candidate workflow), the model-risk lead (or fractional equivalent if you don't yet have one), and the platform engineering lead. We rank candidate workflows by per-decision $ × decisions-per-month × regulatory exposure, list which ones won't pay back so you don't fund them, and map each candidate to the SR 11-7 / OCC 2021-30 / ECOA / BSA / GLBA / MiFID II / SEC 17a-4 / SOX framework boundary that governs it. A model-risk inventory entry is drafted before any build commits.

    90-day fintech AI roadmap with per-workflow $/decision band + model-risk inventory draft
  2. Weeks 3–6

    Pilot — one workflow, model-risk reviewed

    We build the single highest-ROI candidate against your stack (Alloy / Persona / Plaid / Unit21 / Quantexa / Feedzai / Sardine, integrating rather than replacing). Live behind a model-risk approval flag; SR 11-7 validation evidence (in-time + holdout backtest, performance monitoring config, fallback policy, model-monitoring alerting thresholds) compiled and reviewed with your compliance + risk leads. CFPB / ECOA / BSA / MiFID-II boundary explicit in the pipeline routing per workflow. Walk-away point at week 6 — if the metric won't move on your eval set, no phase 2.

    One workflow live behind a model-risk flag with SR 11-7 validation evidence + framework-boundary routing
    Walk-away point
  3. Weeks 7–8

    Ship to production

    Production hardening: Langfuse traces, retry + fallback policies, model-monitoring alerting (drift + performance + fairness metrics), record-retention pipeline aligned to SEC Rule 17a-4 + your firm's BSA log retention obligation, kill-switch wired in for trading + payment-rail workflows. Walk-through with the model-risk lead, CCO, and the operational owner. Vendor third-party-risk paperwork (OCC Bulletin 2021-30) filed before go-live.

    Production workflow + model-monitoring + record-retention + third-party-risk paperwork
  4. Ongoing

    Scale to next workflow

    Most `ai for fintech` engagements run 3–5 workflows by month 6. Same model-risk inventory, same eval harness, same record-retention pipeline. Compounding learning across KYC, fraud, transaction monitoring, credit decisioning, and AML re-rank. Quarterly model-risk review with the firm's risk committee, with the model-risk lead briefing each shipped model's performance + fairness + drift posture against the SR 11-7 expectation.

    3–5 fintech AI workflows live by month 6, all under one model-risk governance posture
engagement models

Three ways to engage.
Hire us at the tier that fits where you are.

Most `ai for fintech` clients start with the 2-week audit, hire us to ship one workflow on a pilot, then move to monthly for the next three to five. Per-decision cost reported monthly on every shipped workflow — no $/decision number, no engagement.

1–2 weeks

Fintech AI audit

Find which AI workflows pay back on your stack — before you commit a budget.

Fixed-fee fixed
  • Operator shadow with Head of Risk / CCO / Head of Payments / model-risk lead
  • Workflow scoring: per-decision $ × decisions/month × regulatory exposure
  • Per-workflow cost band ($0.004–$0.05/decision typical)
  • 90-day fintech AI roadmap with framework-boundary mapping per workflow
  • Model-risk inventory entry drafted per candidate (SR 11-7 + OCC 2021-30 aligned)
  • Honest list of workflows that won't pay back yet
Book the fintech AI audit
Most teams start here
4–8 weeks

Pilot to production

Hire us to ship one fintech AI workflow end-to-end, model-risk reviewed.

Fixed-bid fixed price
  • Build, integrate, deploy on Alloy / Persona / Plaid / Unit21 / Quantexa / Feedzai / Sardine
  • SR 11-7 model-risk validation evidence (in-time + holdout backtest, monitoring config)
  • ECOA Reg B + CFPB Circular 2023-03 alignment on lending workflows
  • BSA/AML SAR-boundary routing where transaction-monitoring is in scope
  • MiFID II + SEC Rule 17a-4 record-retention pipeline where trading touches
  • Walk-away point at week 6 — no phase 2 if the eval metric won't move
Hire us for the pilot
Monthly

Continuous fintech AI team

Embedded fintech AI engineers shipping the next workflow on your roadmap.

monthly per month
  • PM + AI engineer + model-risk analyst, embedded
  • Per-workflow monthly performance + fairness + drift report
  • Quarterly model-risk committee briefing prep
  • Cancel any time — no annual contract
Talk to a fintech AI engineer
Model-risk inventory on every workflow (SR 11-7 aligned) ECOA + CFPB + BSA / SAR boundary explicit per workflow Record retention aligned to SEC Rule 17a-4 No annual contract
frequently asked — fintech ai

Questions risk + compliance leads ask first.
Real answers, no hedging.

What does "AI for fintech" actually mean — what do you build?

An `ai for fintech` engagement with us ships production AI workflows on your stack, not slide decks or vendor pitches. The day-to-day:

  • Scope which workflow moves a P&L line (most often KYC tier-2 enrichment, AI fraud detection at the auth boundary, transaction monitoring re-rank, AI credit decisioning + ECOA Reg B adverse-action handling, AML alert re-rank, or an asset-management research copilot).
  • Assign each workflow to its governing regulatory framework (SR 11-7 for model risk · ECOA Reg B + CFPB Circular 2023-03 for lending · BSA/AML for transaction monitoring · MiFID II + SEC 17a-4 for trading · GLBA Safeguards for NPI · OCC Bulletin 2021-30 for third-party vendor risk · SOX for ICFR if outputs reach financial reporting).
  • Pick the right model per workflow (Sonnet 4.6 for boundary-band reasoning + ECOA principal-reason extraction, Haiku 4.5 for high-volume KYC classification + inline Card-auth fraud screening, GPT-5-mini for structured field extraction).
  • Build it against your existing platforms (Alloy / Persona / Plaid / Unit21 / Quantexa / Feedzai / Sardine, integrating rather than replacing), ship behind a model-risk approval flag with the validation evidence compiled, then operate the workflow long enough to prove per-decision cost-of-ownership before scaling.

We're a fintech AI development partner, not a vendor selling a packaged risk product. Model governance for regulated finance is wired in from day one — SR 11-7 paperwork, audit logs, validation evidence.

How do you handle BSA / AML and the SAR-filing boundary?

Every workflow that touches transaction monitoring or AML sits one step away from a Bank Secrecy Act SAR filing. The rule we apply:

  • AI does classification, prioritization, alert re-ranking, case enrichment, and analyst summarization. The SAR-filing decision itself stays with a human.
  • Human approver named in the pipeline: the BSA Officer (or your firm's designated SAR signer); the 30-day FinCEN filing clock starts when a knowledgeable person identifies suspicious activity, not when a model classifies it.
  • Model role marked on every alert (advisory · suppressive · enriching), with any case in the SAR-boundary band routed to a named analyst for review before any case closure.
  • Audit log separates reasoning: the model's reasoning + the analyst's reasoning + the filing decision are captured separately, with the filing decision attributed to the human filer.

ComplyAdvantage, Unit21, and Quantexa are the benchmark platforms we ship into; we don't replace them, we add the re-ranking + enrichment layer. Adjacent build: fintech ops automation for KYC enrichment, reconciliation, and case-closure workflows on the same audit-log spine.

What about ECOA Reg B and CFPB Circular 2023-03 on AI credit decisioning?

Credit decisioning under our pipeline is structured around three obligations CFPB Circular 2023-03 makes explicit:

  • Specific principal reasons: the adverse-action notice cites specific principal reasons reflecting the actual reasons for denial, not a generic 'insufficient credit history' or 'the model declined the application'.
  • Derived from actual decision logic: the principal reasons are derived from the model's actual decision logic, not a post-hoc explanation the lender doesn't trust.
  • Auditable extraction: the lender's compliance team can audit the principal-reason extraction.
  • Sonnet 4.6 over the model's input features + score rationale: producing ECOA-compliant adverse-action language the underwriter reviews and signs.
  • Disparate-impact monitor running nightly against the lender's protected-class fields, alerting compliance + model-risk lead when the four-fifths rule threshold is approached rather than waiting for a quarterly review.

The model is advisory + explanatory; the underwriter signs every adverse-action notice. Reg B's specific-reasons requirement (§1002.9(a)(2)) is the design constraint, not a compliance checkbox added at the end.

How does SR 11-7 / OCC Bulletin 2021-30 model-risk management fit into your engagements?

Model-risk governance is a first-class output of every pilot we ship, not a year-2 retrofit. Per workflow, we deliver:

  • Model inventory entry per the firm's SR 11-7-aligned inventory format, or we'll draft the inventory format if the firm doesn't yet have one.
  • Validation evidence: in-time + holdout backtest, performance metrics, fairness-monitoring config.
  • Ongoing-use monitoring: drift detection, performance alerts, fairness alerts.
  • Fallback policy (what happens when the model fails or degrades), and an exit plan.
  • Third-party-model paperwork: for the vendor LLM tier (Anthropic / OpenAI), OCC Bulletin 2021-30 applies, so the model-risk lead gets the vendor's SOC 2, retention terms, version-tracking policy, and a documented exit plan.

We've found the firms that get this paperwork right at the pilot stage ship workflow #2 in 4 weeks instead of 12. The model-risk lead and the CCO have already aligned on the framework, so workflow #2 inherits the posture rather than re-litigating it.

Are you a Ramp / Alloy / ComplyAdvantage reseller? Do you replace them?

Neither. We're a development partner, not a reseller, and we integrate with the fintech platforms you already run rather than replace them.

  • Alloy is strong for unified onboarding + risk; Persona is strong for identity verification at scale; Plaid is the bureau-equivalent for bank data.
  • ComplyAdvantage is strong on adverse-media + sanctions; Unit21 and Quantexa are strong case-management + investigation platforms.
  • Sardine and Feedzai are strong on real-time fraud at the auth boundary; Ramp and Brex are the corporate-card stacks we sometimes build adjacent to.
  • Each is the right answer for some firms. We build when the workflow needs to read from your firm's own transaction graph, chargeback history, or policy rather than a vendor's default thresholds.

We'll say in the audit if a packaged product is the better answer for the workflow; we've recommended Alloy + ComplyAdvantage to firms whose scope wasn't worth a custom build.

What about MiFID II and SEC Rule 17a-4 — where do they touch your work?

Anywhere a model output influences a trading decision, the obligations stack:

  • MiFID II Article 17(2) applies (effective systems + risk controls + kill switches + pre-trade limits + algorithm-change recording, with compliance + risk functions named accountable).
  • SEC Rule 17a-4 layers in electronic-record retention obligations, WORM-equivalent, with retention periods aligned to record type (typically 3–6 years, with specific record types longer).
  • Trading-adjacent pattern: model produces research summaries + buy/sell candidates with reasoning chains; PM / trader reviews and decides; execution goes through your existing OMS/EMS with the model recorded as an input, never an executor.
  • Recording at the pipeline layer: every model call audit-logged, every reasoning chain retained WORM-style, every algorithm change versioned and recorded.

We work alongside your compliance function to make sure the workflow ships with the recording obligation handled at the pipeline layer, not bolted on later.

How does AI fit into payments — what does an `ai payments` workflow actually cost per decision?

Per-decision cost varies by rail (see §1.5 for the per-rail breakdown).

  • Card auth: typically runs $0.006/screen on Haiku 4.5 because of the 200ms latency budget.
  • ACH: typically runs $0.004/screen (same model, more relaxed batch tolerance).
  • RTP: runs $0.012/screen (Haiku 4.5 + GPT-5-mini parallel for the 15-second finality window).
  • Wire: runs $0.021/screen on Sonnet 4.6 because the rail is irrevocable and false-decline cost dominates. Running the reasoning model with full counterparty + beneficiary-history context is the right answer.
  • FedNow: lands at $0.014/screen with model selection by transaction size (Haiku 4.5 for volume; Sonnet 4.6 for any transaction over a firm-set high-value threshold).

The economic story isn't the per-decision dollar. It's the chargeback dispute economics on Card, the irrevocability premium on Wire, and the merchant-satisfaction cost of a false decline on ACH. We model all of that in the audit, per your rail mix, before scoping the pilot.

How do you handle GLBA Safeguards Rule and customer NPI when training or fine-tuning?

Customer NPI (nonpublic personal information) under GLBA Safeguards Rule is the design constraint for any fintech AI workflow touching account-level data.

  • Default deployment for NPI-touching workflows: vendor BYOK encryption with retention=0 (Anthropic Claude on AWS Bedrock with customer-managed KMS keys, or Azure OpenAI with retention disabled), matching the posture of legal ring-2 and healthcare PHI tier-2.
  • No fine-tuning on raw NPI: instead, we build retrieval indexes over de-identified or tokenized customer data with the re-identification key held inside your tenant.
  • Audit-log storage: any audit-log entry that includes NPI is stored under your firm's encryption-at-rest + access-control posture, not the vendor's.
  • Information-security program inheritance: GLBA's required program elements (designate qualified person, written program, risk assessment, monitoring, training) are the operating posture the workflow inherits from your existing safeguards program.

If your firm hasn't yet named a qualified individual for the program, we'll flag that in the audit before any data touches a model.

What does an end-to-end pilot timeline look like — and what's the walk-away point?

Four-to-eight weeks. The phases:

  • Weeks 1–2 — audit (overlapping with the pilot scope): operator shadow, workflow scoring, regulatory-framework mapping, model-risk inventory draft.
  • Weeks 3–6 — build: integrate with your platform stack, build the pipeline, generate the SR 11-7 validation evidence (in-time + holdout backtest, monitoring config, fallback policy), align the regulatory boundary (CFPB / ECOA / BSA / MiFID II / SEC 17a-4) per the workflow scope, ship behind a model-risk approval flag, eval against your own data.
  • Walk-away point at week 6: if the eval metric won't move on your data, we stop before production hardening. No phase 2, no scope-creep into a year-long implementation.
  • Weeks 7–8 — production hardening: Langfuse traces, model-monitoring alerting, record-retention pipeline aligned to SEC Rule 17a-4, third-party-risk paperwork (OCC Bulletin 2021-30) filed, kill switches wired in for trading + payment-rail workflows.

Most engagements that pass the week-6 gate ship workflow #2 in 4 weeks against the same infrastructure.

How much does a fintech AI project cost — and how do you price the audit, pilot, and continuous tier?

Three tiers, pricing-locked across our service cluster.

  • Fintech AI audit: Fixed-fee, 1–2 weeks. Operator shadow with Head of Risk / CCO / Head of Payments / model-risk lead; workflow scoring by per-decision $ × decisions/month × regulatory exposure; framework-boundary mapping per candidate (SR 11-7 / OCC 2021-30 / ECOA / CFPB / BSA / GLBA / MiFID II / SEC 17a-4 / SOX); model-risk inventory entries drafted; an honest list of workflows that won't pay back yet.
  • Pilot to production: fixed-bid fixed, 4–8 weeks. One workflow shipped end-to-end on your stack, model-risk reviewed, framework-boundary explicit, with the walk-away point at week 6.
  • Continuous fintech AI team: monthlynth, no annual contract. Embedded PM + AI engineer + model-risk analyst shipping the next workflow on your roadmap, with per-workflow monthly performance + fairness + drift reporting and quarterly model-risk committee briefing prep.

Most `ai for fintech` engagements we run start with the audit, ship the first workflow on the pilot, then move to monthly for workflows two through five. Per-decision cost reported monthly on every shipped workflow. No $/decision number means no engagement.

Ready to ship

Stop running another vendor PoC that dies at month 4.
Hire a fintech AI development partner that ships.

Book a free 30-minute fintech AI audit. We'll identify two or three high-ROI candidates from your stack, map each to its governing regulatory framework, give you a per-workflow $/decision band, and tell you which ones won't pay back yet. No deck, no obligation to build.

Book the fintech AI audit Read capability patterns
30 min, async or live Regulatory-framework mapping included You leave with a written roadmap
keep exploring

Related pages.
Pick where you are.

Not sure if you need a fintech-specific build, a payment-rail integration scope, or a model-risk-only engagement? These sibling pages go deeper.

01 Industry

AI for Law Firms

Sibling vertical · same canonical pillar shape, different regulatory rings (privilege + ECOA-equivalent UPL constraints).

Read more
02 Industry

AI in Healthcare

Sibling vertical · BAA + PHI posture parallels GLBA + NPI posture.

Read more
03 Industry

AI in E-commerce

Cross-vertical · fraud + payments overlap, same auth-boundary plumbing.

Read more
04 Service

AI Automation

Cluster sibling · the agent-loop framing that powers KYC + AML workflows.

Read more
05 Service

AI Agent Development

The sibling service — the fraud-agent pattern lives here as a service capability.

Read more
06 Service

Claude Development

Cluster sibling · Sonnet 4.6 + Haiku 4.5 are the fintech model picks across the §7 matrix.

Read more
07 Service

AI Development Services

When fraud detection grows into a multi-model platform — ML classifiers + LLM agents + transaction scoring + monitoring. The umbrella AI development services pillar.

Read more
08 Service

AI Consulting for fintech

Fintech AI engagements start with a discovery audit — workflow scoping under SR 11-7 and FFIEC, model-risk-management mapping, and the eval design + kill point that gates the pilot.

Read more
Updated May 20, 2026 · By Navin Sharma