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AI for Fintech

The applicant is gone before the credit decision clears. The books close two weeks after month-end.

We engineer AI for lenders, payment companies, embedded-finance platforms, and treasury teams: explainable credit decisioning, continuous reconciliation across every processor you touch, real-time risk and exposure monitoring, and dispute automation your auditors can follow.

Discuss your finance workflowSee how the system works

The operational reality

Credit takes hours. Reconciliation takes weeks. Risk is a quarterly snapshot.

The neobank next door clears in a minute. Your applicant is already gone when yours clears.

Speed is the product. The books prove you can afford it.

Felt by heads of finance operations, CFOs who answer to a board on close speed, VPs of engineering who own the ledger pipelines, and heads of credit who defend every denial under Reg B.

  1. 01

    Application and intake

    A borrower or merchant applies through a web form, a partner API, or an embedded flow. KYC, KYB, and bank-connectivity checks run against third-party data.

    Friction · The applicant waits while the review queue clears; third-party calls time out and get retried by hand.

  2. 02

    Risk and credit decisioning

    The credit model combines bureau pulls, cash-flow data from Plaid or MX, alternative data, and policy rules to return approve, decline, or refer. An adverse-action reason is produced for every decline.

    Friction · Decisions take hours; the applicant has already clicked through to a competitor that answers in under a minute.

  3. 03

    Booking and funds movement

    Approved loans or merchant accounts are booked into the core ledger. Disbursements, settlements, and card loads are instructed through the payment processors.

    Friction · Dual-control is a shared inbox and a spreadsheet; the booking is right most of the time, and audits catch the rest.

  4. 04

    Reconciliation and month-end close

    Daily settlement files from Stripe, Adyen, Plaid, bank-direct, and the card networks are matched against internal ledger entries. Breaks are triaged and booked before the month closes.

    Friction · The match work lives in a spreadsheet, breaks get investigated at month-end instead of the day they happen, and close drags into week two.

  5. 05

    Disputes, chargebacks, and exceptions

    Chargebacks, failed debits, refunds, and funding exceptions are investigated against transaction history and processor evidence. Response packets are assembled under network deadlines.

    Friction · Deadlines are missed because evidence lives in four systems; the win rate drops and the chargeback becomes real revenue loss.

Swipe the workflow

Where the work breaks

The friction is in the places nobody budgets for.

  • 01

    Credit decisions take hours while competitors answer in under a minute.

    How we fix it · A system we build catches this before it costs you.

    Impact · Applicants drop off before approval clears. Conversion falls, and the cost of acquisition pays for the applicant that never funded.

  • 02

    Reconciliation is a spreadsheet across Stripe, Adyen, Plaid, bank-direct, and the card networks.

    How we fix it · A system we build catches this before it costs you.

    Impact · Nobody can tell you cash position before noon. Finance owns the file; engineering owns the data; neither owns the match.

  • 03

    Mismatches hide real revenue inside tolerances.

    How we fix it · A system we build catches this before it costs you.

    Impact · Small, consistent breaks become a quarterly surprise. Fees that were never booked and timing differences that never resolved sit in a suspense account until someone finds them.

  • 04

    Month-end close drags into week two.

    How we fix it · A system we build catches this before it costs you.

    Impact · The board gets a late number; the FP&A cycle starts late; the finance team runs on evenings and weekends to produce a report the business already needs.

  • 05

    Audit trails are reconstructed after the fact.

    How we fix it · A system we build catches this before it costs you.

    Impact · When SOC 2, SOX, or a payment-network audit asks who approved a booking and why, the answer is pulled out of Slack threads and spreadsheet revision history, not out of a system.

What we would build

Systems, not features. Each with its own job.

  • 01

    Explainable credit decisioning

    Credit decisions take hours when the applicant is comparing you to someone who answers in under a minute.

    Who benefits · Heads of credit, risk engineers, and the growth team that watches funnel drop-off.

    Inputs
    Bureau pulls, bank-connectivity data from Plaid or MX, your own prior-performance data, policy rules, and the alternative data your credit team has decided is in scope.
    What the system does
    A champion model scores the application; a challenger runs alongside; a policy layer applies hard rules and generates an adverse-action reason for every decline, grounded in the actual features that drove the decision.
    Output
    An approve, decline, or refer decision in under a second, with a reason code, a feature-level explanation, and a packet the compliance team can show a regulator.
    Benefit
    Approvals keep pace with the applicant. Declines are defensible under Regulation B, and model performance is monitored instead of trusted.
    Human stays here
    Policy rules override the model. Refers go to a human underwriter. Model updates ship through a model-risk workflow with a documented challenger period before promotion.
  • 02

    Continuous reconciliation across processors and ledgers

    Reconciliation lives in a spreadsheet that only closes once a month. Breaks are discovered too late to fix cheaply.

    Who benefits · Finance operations, controllers, and the engineering team that owns the ledger.

    Inputs
    Settlement files and webhooks from every processor you use (Stripe, Adyen, Checkout, Plaid, bank-direct ACH, card-network reports), your core ledger, and historical break patterns.
    What the system does
    A canonical ledger model ingests events as they arrive. A matching engine runs deterministic rules first; an ML tie-break proposes a match for the leftovers with a confidence score and a reason. Anything below threshold stays open as a break with a suggested root cause.
    Output
    A live reconciliation state at any point in the day, open breaks sorted by dollar impact and age, and a queue of exceptions ready for a human to clear.
    Benefit
    Cash position is known before lunch. Month-end becomes a review of a book that is already closed, not an investigation that starts on day one.
    Human stays here
    A finance operator approves auto-match proposals above a tolerance. Bookings against the ledger require a maker-checker approval. Nothing writes to the general ledger without a human in the loop above a configurable threshold.
  • 03

    Real-time risk and exposure monitoring

    Risk is reported as a quarterly snapshot. By the time a trend shows up in a dashboard, it is already a loss.

    Who benefits · Chief risk officers, portfolio managers, and the credit committee.

    Inputs
    Live decisioning events, performance data from the loan book or merchant portfolio, bureau refreshes, settlement activity, and macro signals the risk team has endorsed.
    What the system does
    A streaming pipeline recomputes exposure, cohort-level loss curves, concentration limits, and early-delinquency indicators as events land. Alerts fire against policy thresholds, not against intuitions.
    Output
    A continuously updated view of exposure by cohort, product, geography, and merchant category, with alerts routed to the right owner.
    Benefit
    Risk is managed in the week it moves, not after a quarterly review. The credit committee sees the same numbers the dashboards use.
    Human stays here
    Thresholds and policies are owned by the risk team and version-controlled. Alerts never auto-adjust limits; they route to the policy owner for action.
  • 04

    Dispute and chargeback automation with evidence packs

    Chargebacks are missed on deadline because the evidence lives in four systems. The win rate drops and the loss becomes real.

    Who benefits · Dispute analysts, finance operations, and the merchants served by an embedded-finance platform.

    Inputs
    Chargeback notifications from the networks, your transaction history, device and session data, delivery confirmations, communication logs, and prior dispute outcomes.
    What the system does
    The system matches the chargeback to the originating transaction, pulls evidence from each connected system, and assembles a response packet in the network's format. A model predicts win likelihood so analysts work the cases that pay back.
    Output
    A drafted response packet with every piece of evidence linked to its source, a predicted win probability, and a deadline-aware queue.
    Benefit
    No deadlines missed on packets that could win. Analysts spend their time on the cases where their judgement matters, not on assembling files.
    Human stays here
    An analyst reviews and approves every submission. The model never submits a response on its own; it drafts.
  • 05

    Finance operations copilot for exception investigation

    A finance operator spends their day asking the same questions across the ledger, the processor dashboards, and the data warehouse.

    Who benefits · Finance operations analysts, controllers, and the on-call engineer who answers their questions.

    Inputs
    The canonical ledger, processor settlement data, the data warehouse, internal documentation on fee schedules and processor quirks, and the history of how similar breaks were resolved.
    What the system does
    A copilot grounded in your own data and documentation answers questions about a specific break, surfaces the matching historical pattern, and drafts the journal entry that would clear it. It cites every source and refuses to answer when it cannot.
    Output
    A specific answer, a cited source for every claim, and a drafted correction ready for maker-checker review.
    Benefit
    Investigation time per break drops from hours to minutes. Institutional knowledge about processor quirks stops living in one analyst's head.
    Human stays here
    The copilot drafts; the operator decides. Every entry still goes through dual-control before it books.

Show the system

What it could look like in your hands.

A design concept, not a shipped product. Interfaces like this get drawn out before any code lands, so the system reads back the way the people using it work.

finance ops / reconciliation

Day summary

As of 2026-10-09, 11:42 UTC
Matched today
4,218 entries
Matched value
$12.84M
Open breaks
7 breaks
Break impact
$18,406
Oldest break
3 days
Auto-match rate
99.1%

Live ledger entries

DateSourceCategoryExpectedActualDeltaStatus
  • 2026-10-09StripeCard settlement$482,310.44$482,310.44$0.00Matched
  • 2026-10-09AdyenCard settlement$214,980.11$214,862.74-$117.37Break
  • 2026-10-09PlaidACH debit$96,420.00$96,420.00$0.00Matched
  • 2026-10-09Bank-directWire inbound$1,250,000.00$1,249,962.50-$37.50Break
  • 2026-10-09Visa DPSCard-network fee$8,431.09$8,431.09$0.00Matched
  • 2026-10-08StripeDispute reversal-$420.00$0.00+$420.00Break
  • 2026-10-08AdyenFX settlement$74,118.22$73,964.08-$154.14Pending
  • 2026-10-07Mastercard IPMInterchange reclass$3,206.80$3,206.80$0.00Matched
Synthetic data. This is a design concept of how a live reconciliation state, open breaks, and the investigation of a single break could look for a finance operator.

End-to-end workflow

From the trigger to the outcome.

A representative reconciliation path, from an inbound settlement event to a signed journal entry. Automated stages run the moment the data lands; human stages are explicit and never skipped.

  1. 01

    Event lands

    A settlement file, webhook, or network report arrives from a processor, a bank, or a card network. It is received idempotently and timestamped against event-time, not clock time.

    Automated
  2. 02

    Ingest into canonical ledger

    The event is normalised against a canonical ledger model that spans every processor you use. Each entry carries its source, its reference IDs, and the raw payload for replay.

    Integration
  3. 03

    Deterministic match

    Rules match on reference IDs, amounts, dates, and known fee schedules. Matches above tolerance are closed. Everything else moves to the tie-break step.

    Automated
  4. 04

    ML tie-break with explanation

    A model proposes a match for the leftovers with a confidence score, a reason, and the historical pattern it looks like. Below-threshold items stay open as breaks.

    Automated
  5. 05

    Low-confidence breaks flagged

    Breaks above a dollar threshold are flagged to a human operator with the suggested root cause, the matching historical pattern, and a drafted correction. Nothing auto-books here.

    Guardrail
  6. 06

    Investigate with the copilot

    A finance operator asks the copilot about the break. The copilot cites processor rules, prior resolutions, and the exact lines in the ledger and settlement file.

    Human in loop
  7. 07

    Decide and draft entry

    The operator decides the resolution. A journal entry is drafted and routed through maker-checker. The decision, the reasoning, and the inputs are logged.

    Human in loop
  8. 08

    Book under dual control

    The second approver signs the entry and the system books it to the general ledger. The audit trail captures both approvers, the inputs, and the model version that proposed the match.

    Integration

How exceptions are handled

  • If a processor file is late or malformed, the system holds its slot in the reconciliation state as a known gap instead of silently matching to zero.
  • If a proposed match sits below the configured confidence threshold or above the dollar tolerance for auto-book, the system refuses to auto-book and routes the break to a human.
  • If the general-ledger service is unavailable, bookings queue durably with idempotency keys so no entry books twice when it comes back.
  • If a credit decisioning model crosses a drift threshold, the system falls back to the policy layer and alerts the model-risk owner.
  • If an adverse-action reason cannot be generated with sufficient confidence, the decision becomes a refer instead of a decline.

How it is engineered

Built on your stack, not ours.

Design choices that let this run in a production fintech, not a demo. We build to your ledger, your processors, your identity provider, and your compliance posture, not to our stack.

01

Event-time ingestion with replay

Processor webhooks, settlement files, and network reports land in an idempotent intake. Everything is stored raw first and processed by event-time, so late or re-delivered events settle to the right day without double-counting.

02

Canonical ledger model

One normalised shape that every processor maps into, with reference IDs, fees, FX, and value dates preserved. The ledger becomes the single system of record for cash movement and the thing auditors read against.

03

Matching engine, deterministic plus ML tie-break

Rules first, model second. Deterministic matches close silently; the model only proposes on leftovers, with a confidence score and a reason, and it never books on its own above a configurable dollar tolerance.

04

Model risk management for credit

A champion/challenger workflow, documented features, monitored drift, and a promotion gate. Adverse-action reasons map to the features that drove the decline, auditable one case at a time.

05

Dual-control for financial actions

Every booking, limit change, and policy update runs through a maker-checker gate. Approvals are cryptographically attributable to a human identity; agents never book under a service account.

06

Observability and SOX-friendly audit trail

Every event, decision, model version, and approval lands in an append-only audit log. Reconstructing how any entry in the ledger came to be is a query, not an investigation.

What the business will measure

Numbers the operator already watches.

We agree on metrics and baselines before building. These are the numbers a fintech operator usually watches when deciding whether a system is working.

  • 01

    Credit decision latency, p50 and p95

    Baseline today

    Measured today from decisioning logs: time from application submit to decision returned, split by product and channel.

    How it moves

    A policy-plus-model pipeline returns a decision in under a second for the clean path; refers queue to an underwriter with the model's reasoning pre-staged.

  • 02

    Approval rate at a fixed loss band

    Baseline today

    Current approval rate holding target loss rate constant, calculated on vintages that have seasoned enough to score.

    How it moves

    A better-calibrated model and continuous monitoring let you approve more applicants at the same loss band, instead of trading approval for safety.

  • 03

    Days to close the month

    Baseline today

    Business days from month-end to the controller's close signoff, measured across the last six months.

    How it moves

    Reconciliation runs continuously; most breaks are investigated the day they happen; month-end is a review, not an investigation.

  • 04

    Open break backlog by age

    Baseline today

    Count and dollar impact of unreconciled breaks older than one, three, and seven days.

    How it moves

    The copilot assigns a suggested cause and historical pattern on day one; the queue is sorted by impact and age so the oldest money is cleared first.

  • 05

    Dispute win rate

    Baseline today

    Won responses as a share of represented chargebacks over a rolling 90-day window, by reason code.

    How it moves

    Evidence packs are assembled on time with every supporting document linked; analysts spend their time on the cases a model predicts are winnable.

Human in the loop

Oversight, safety, and the controls

Where the humans stay in control.

The system is designed around what finance, risk, and audit teams already expect. Nothing is autonomous that should not be, and every control is one your examiner can walk.

  • 01

    SOC 2 Type II posture

    We build to SOC 2 Type II controls from day one: logical access, change management, incident response, vendor review, and continuous monitoring, with evidence collected automatically rather than scraped at audit time.

  • 02

    PCI DSS scope kept small

    Primary account numbers never land in your application tier. We tokenise at the processor edge and keep PCI scope inside the processor's vault, so your audit surface stays narrow.

  • 03

    Model risk management and challenger models

    Every credit model has a documented feature set, a champion/challenger workflow, monitored drift, and a promotion gate. Model inventories, validation reports, and incident records map to SR 11-7 expectations even when you are not a bank.

  • 04

    Adverse-action notices under Regulation B

    Every decline carries up to four principal reason codes grounded in the actual features that drove the decision. Notices are generated with a defensible mapping, not with freeform model prose.

  • 05

    Data residency and tenant isolation

    Customer data lives in the region your contract and your regulator require, in a dedicated tenant where the ledger already sits. Residency is a configuration, not a promise.

  • 06

    Dual-control for financial actions

    Every booking, every limit change, every policy update runs through maker-checker with cryptographically attributable approvals. No service-account bookings and no shared credentials.

Proof

Work we have shipped, and the shape of what we build.

Real engagements where we can speak to them, and representative use cases where we cannot.

Case studies

Shipped in Fintech.

How the build runs

Scoped tightly, shipped in phases.

Fixed price per phase, approved before the phase starts. You own the code, the models, the ledger integrations, and the deployment.

  1. 01

    Discovery

    Two weeks. We shadow a close and sit with the credit and finance-ops teams, map the real workflow against your processors and your ledger, and agree on the metrics we will move and how we will baseline them.

  2. 02

    Prototype on real data

    Four to six weeks. One slice of the problem on real settlement and ledger data: one processor, one product, one business day of close. The output is a measured result against your baseline, not a demo.

  3. 03

    Parallel run

    Four to eight weeks. The system runs alongside your current reconciliation or decisioning process, shadowing every decision and every match. Nothing books, nothing declines; we compare against the live system and prove the delta before any action moves.

  4. 04

    Production rollout

    Eight to twelve weeks per product line. Processor integrations, ledger writes under dual-control, SOC 2 evidence collection, model-risk documentation, and the oversight workflow your audit and compliance teams signed off on.

  5. 05

    Handover

    You get the full source, the runbooks, the evaluation suite, the model documentation, and a two-week walkthrough with your engineering, finance, and risk teams. No vendor lock-in and no ongoing licence fee for what we built.

Common questions

Answers before the first call.

01Which processors and ledgers do you integrate with?
We build against Stripe, Adyen, Checkout, Plaid, Modern Treasury, bank-direct ACH and wires, and the card networks' settlement files. On the ledger side we work with core-banking systems, custom ledgers on Postgres, and products like Modern Treasury Ledger. The canonical model is the point: adding a processor is a mapping, not a rewrite.
02Where does our data live, and can we pick a region?
In your tenant where your ledger already sits, or in a dedicated environment we operate for you. Data residency is a configuration you choose and we honour, including US-only, EU-only, and single-region deployments for regulators that require it.
03How do you handle model risk management for credit decisions?
A documented feature set, a champion/challenger workflow, monitored drift, a promotion gate, and a model inventory with validation reports. We build to SR 11-7 and OCC 2011-12 expectations because the banks you sell into will ask about them even when you are not a bank.
04How are adverse-action notices generated?
Every decline carries up to four principal reason codes derived from the features that drove the decision, with a defensible mapping between model features and ECOA-permissible reasons. The notice is a system output a compliance officer can show a regulator, not freeform model prose.
05Who signs off on a booking?
A human. Every booking, every limit change, and every policy update runs through maker-checker with two attributable approvals. Agents never book under a service account, and the system refuses to auto-book above a configurable dollar tolerance even on high-confidence matches.
See the other details on the FAQ page

Next step

A thirty-minute conversation. Bring one workflow that costs your team real time today, and we will walk through what the first phase would look like, where the measurements come from, and what has to be true for your risk and audit teams to approve it.

Discuss your finance workflow

Helpful to have on the call

Helpful to have on the call: your processor stack, your current reconciliation cadence and open-break backlog, and the person who owns model risk or audit on your side.

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