Payments, fraud detection, and real-time decisioning — built correct under load.
We build the software fintech companies move money on: payment and transaction infrastructure, fraud-detection and risk models, real-time decisioning, and the regulatory-reporting pipelines that keep it auditable.
Built on your data, reconciled against your ledger, correct under load — because when software moves money, a wrong answer is a loss, a chargeback, or a compliance failure. Fixed scope, one accountable lead, production in 4–8 weeks.
Because in fintech the software is the product, and the product handles money in real time. A bug in a content site is an embarrassment; a bug in a payment flow is a double charge, a stuck settlement, or a fraud window someone is already exploiting.
The interesting work is never the happy path — it’s the reconciliation that has to balance to the cent, the idempotency that survives a retried request, the decisioning that must answer in milliseconds, and the audit trail an examiner can follow a year later.
Static fraud rules can’t keep up: tighten them and you decline good customers; loosen them and losses climb. The gap that sinks most fintech builds isn’t the feature list — it’s the engineering discipline that keeps a money-moving system correct, fast, and auditable while it scales. That is what fintech software development, done properly, delivers.
This isn’t one product. It’s a set of high-leverage systems that sit on top of your payment rails and ledger — for each, what it does, the benefit it produces, and a one-line illustration. (Illustrations are examples of how the technology helps, not Silicon Prime client results.)
Builds the rails that take, hold, route, split, and settle money — wired to processors and banking partners, with idempotent transaction handling and reconciliation that balances to the ledger. Benefit — money moves correctly and settlement reconciles, even under retries and partial failures.
For example, a payment that times out and gets retried by the client lands as a single charge instead of a double-debit dispute — so the transaction settles clean and never becomes a chargeback and a support ticket.
Learns the normal pattern of an account or transaction stream and scores activity for fraud in real time, flagging the genuine anomaly while letting legitimate volume through — instead of a static rule that blocks good customers and misses new schemes. Benefit — more fraud caught earlier, with fewer false declines costing real revenue.
For example, a card-not-present charge that breaks an account’s established pattern is held for step-up verification in milliseconds, while the customer’s routine purchase clears untouched — catching the fraud without the false decline that drives them to a competitor.
Runs the approve / decline / step-up / review decision the moment a transaction or application arrives — combining policy, risk score, and signals into a tiered response with the reason captured. Benefit — instant, consistent decisions that don’t trade speed for safety, with an explanation attached.
For example, an onboarding or transaction decision resolves in under a second with its rationale logged, so the customer isn’t left waiting and the team has a defensible record of why the call was made.
Assembles the data behind KYC/AML, transaction monitoring, and regulatory filings into auditable, repeatable pipelines — replacing manual spreadsheet assembly that eats analyst time and invites error. Benefit — lower compliance cost, faster filings, and an audit trail a regulator or partner bank can follow.
For example, a suspicious-activity review that took an analyst hours of pulling records from five systems is pre-assembled into one reviewable case with its evidence attached — so the team reviews and decides instead of hunting for data.
Exposes payments, accounts, payouts, or lending as clean, documented, well-versioned APIs other products build on — the layer that turns a financial capability into something a partner can integrate. Benefit — faster partner integrations and new revenue from distribution, without a brittle integration surface.
For example, a platform adds payouts to its product against a documented API in days instead of a multi-month custom build — so the capability ships while the contract is still warm.
Builds the data and decisioning layer behind origination and underwriting — pulling the right signals, applying the policy, and surfacing the explanation — with a human owning every adverse decision. Benefit — faster, more consistent decisions with the documentation a fair-lending review requires.
For example, an application’s decisioning inputs and the reasons behind the outcome are captured automatically, so a declined applicant gets a defensible explanation and the lender keeps an examinable record — rather than reconstructing the rationale later.
The scope below is the application and intelligence layer — the rails, models, decisions, and pipelines that run on top of your processors, banking partners, and ledger. We build software for fintech companies; we do not sell a chartered bank or a payment-processor license as a product, and we integrate with your partners and rails rather than becoming them.
Payment acceptance, payouts, ledgering, and reconciliation built against your processors and banking partners — idempotent, balanced to the cent, and resilient to the retries and partial failures real payment traffic produces.
We build the machine-learning models that score transactions, accounts, and applications for fraud and risk, validated against your historical data so alerts are trustworthy — not a false-positive generator the team learns to ignore — with human-in-the-loop review on the decisions that carry real consequence.
The low-latency layer that turns policy and risk signals into an approve / decline / step-up / review decision in milliseconds, with the reason captured for every call — built on trusted data through our data engineering work.
Auditable, repeatable pipelines behind KYC/AML, transaction monitoring, and regulatory filings — so the numbers reconcile and the audit trail holds up to a regulator or a partner bank’s diligence.
Clean, documented, versioned APIs that expose your financial capabilities to partners and internal teams — the integration surface that turns a capability into distribution, without becoming a brittle liability.
Fintech software lives or dies on security. We build under scoped permissions, encryption, and audit logging, folding in our secure software and DevSecOps practices and engineering toward standards like PCI-DSS and SOC 2 rather than bolting controls on at the end.
What you get when you hire us — all assigned to you under full work-for-hire IP transfer
The same delivery model behind all our work, tuned for a money-moving system — one accountable lead, fixed scope, no handoffs to account managers.
Scope the use case and the outcome it targets — fraud loss, false-decline rate, decision latency, reconciliation accuracy, filing time — and confirm the data, rails, partners, and regulatory constraints it has to live within. Run as our AI readiness assessment, with the honest “this one isn’t ready yet” call included.
Output: a ranked plan & the metric we’ll be judged on
Connect to your processors, banking partners, ledger, and systems of record through governed, scoped integrations, with reconciliation and audit logging built in from the start.
Output: a trusted, auditable transaction foundation
Develop the application and, where it applies, train and validate the model against your historical data, in your own cloud environment, with a security review and idempotent, examinable transaction handling built in.
Output: a working system tested on your real data & traffic
Ship behind a staged rollout — shadow mode, then a pilot, then wide — prove the metric moves and the controls hold under load, and train your team to operate, retrain, and examine it.
Output: a production system & a team that owns it
Most engagements reach production in 4–8 weeks, payment is tied to the ROI we agreed at kickoff, and full work-for-hire IP assignment is signed before we start.
Fintech is one of the few domains where our deepest proof is the domain itself — here is the in-domain record, with the founder accountability behind it.
We built YardClub — a contractor-to-contractor marketplace for heavy equipment — end to end, including its payments and transaction infrastructure: the listings, the money movement, and the reconciliation behind it.
That system processed more than $120 million in transactions before YardClub was acquired by Caterpillar in 2017. It’s the same payments and transaction engineering this page describes, shipped and proven in production — though to be precise, a marketplace’s payment rails are not a chartered bank or a licensed processor, and we don’t claim to be one.
The reliability bar comes from the same place every system we ship is held to. Over four years we moved BJ’s Restaurants, a 200+ location operation whose software is critical to daily operations, from biweekly releases to twice-a-week shipping with zero critical defects — through evals before release, staged rollout, and continuous production monitoring. Different industry, but exactly the “move fast, never break the thing money depends on” standard a fraud engine or a settlement pipeline has to meet.
The reliability bar — a stat we hold every system to
Silicon Prime is a Stanford-rooted Responsible AI lab, founded in 2011, run by founder Kelvin Tran — 20+ years of production engineering, including multimillion-dollar systems for one of the world’s largest manufacturers, and personally accountable for every engagement. If your problem is a genuine stretch for what we’ve shipped, we’ll tell you, scope a contained pilot to prove it before you commit, and put the accountability in writing.
We’ve shipped payment rails that moved real money. The payments and transaction infrastructure behind YardClub’s $120M+ in volume is first-party, in-domain proof — not a generic SaaS portfolio retold with fintech words.
Correct, fast, and auditable by design. In fintech the reconciliation, the latency budget, and the audit trail are the product. We engineer toward standards like PCI-DSS and SOC 2 and fold in DevSecOps from the first commit — not as a pre-launch scramble.
Models you can defend, validated on your data. We validate fraud, risk, and decisioning models against your historical record before they go live, with human-in-the-loop review on consequential decisions — because an unexplainable decline is a liability, not a feature.
Founder-led, built to transfer. One accountable lead, not a relay of account managers; the code, models, and pipelines are assigned to you, with your team trained to run and extend them when we step back.
For regulated banks and banking institutions — digital banking on a core, AML at institution scale, examiner-ready reporting, and legacy-core modernization. Banking software →
The fraud, risk, and decisioning models behind a fintech build — validated on your historical data so alerts are trustworthy, not a false-positive generator. ML development →
Real ranges for a fintech build, plus how we set the target metric at kickoff so value is measured against a baseline. Cost guide →
What teams want to know before they commit to building fintech software that moves money.
Genuine, in-domain experience. We built YardClub’s payments and transaction infrastructure end to end — the money movement and reconciliation behind a marketplace that processed $120M+ before being acquired by Caterpillar in 2017. That’s real payments and transaction engineering, not a generic portfolio. To be precise: a marketplace’s payment rails are not a chartered bank or a licensed processor, so for the regulated-entity pieces we build the software layer and integrate with your banking and processing partners rather than claiming to be one.
No — we’re a software engineering and AI firm that builds the application and intelligence layer fintech products run on: payment and transaction infrastructure, fraud and decisioning models, regulatory-reporting pipelines, and developer APIs. We integrate with your processors, banking partners, and rails rather than becoming them, and we don’t sell a charter or a license as a product. Keeping that boundary clear is part of why our engagements are fast and lower-risk; you remain the accountable regulated entity, and we build the software and controls around that.
We validate every model against your historical data before it goes live — measuring detection and false-positive rates on your own record, not a vendor benchmark — and design human-in-the-loop review into the decisions that carry regulatory or customer-impact weight. The stakes are real: online payment fraud is projected to exceed $362 billion globally for 2023–2028 (Juniper Research, June 2023). But in a regulated setting an unexplainable model is a liability, so explainability and an examinable decision trail are part of the build, not an afterthought.
The software runs in your own cloud environment under your access controls; integrations to processors, banking partners, and systems of record are scoped, permissioned, and audit-logged; and every engagement starts with an NDA and a security review. We engineer toward standards like PCI-DSS and SOC 2, document every data path so your security and compliance teams can verify rather than trust, and design within the regulatory constraints you operate under rather than discovering them late. We build the software and controls; your company remains the accountable regulated entity.
Yes — that’s the core of the work. We build payment acceptance, payouts, ledgering, and reconciliation against your processors and banking partners, with idempotent transaction handling that survives retries and partial failures and balances to the cent. The reconciliation, the edge cases, and the failure modes are the job; the screens on top are the easy part once the rails are correct.
Our banking software development page is for banks and banking institutions — digital banking on top of a core, fraud and AML at a regulated institution’s scale, examiner-ready reporting, and legacy-core modernization. This page is for fintech and payments companies — startups and scale-ups building new financial products: payment rails, real-time decisioning, fraud models, regtech, and embedded finance. The engineering rigor is shared; the legacy-core reality and examiner weight are heavier on the banking side, while the speed-to-market and greenfield-build pressure are heavier here.
You do — completely. The applications, payment and decisioning logic, trained models, data pipelines, and audit artifacts transfer under full work-for-hire IP assignment signed at kickoff, and your team is trained to operate, retrain, and examine them. Keep us on a reduced retainer or take the keys; the engagement is built around the handover, not around locking you in.
Most engagements reach production in 4–8 weeks under a fixed-scope, ROI-tied model with one accountable lead, and we typically prove the metric on a contained pilot before scaling. Build cost depends on scope — our AI development cost guide gives real ranges — and we set the target metric at kickoff so the value is measured against a baseline rather than assumed.
Thirty minutes · No pitch deck
Bring the problem you want to attack — a payment flow that has to reconcile, fraud losses you can’t rule your way out of, a decisioning latency budget, a compliance grind — and we’ll tell you honestly whether the data and rails support it, what it takes to build, and what it costs to run.