Digital banking, fraud detection, and regulatory reporting — auditable by design.
We build the software banks run on top of their core: digital-banking experiences, fraud and risk models, regulatory-reporting pipelines, and customer-servicing systems — grounded in the data your institution already holds and wired to the core and ledger systems you already run.
We build the application and intelligence layer, not a chartered core-banking platform sold as a regulated product. Fixed scope, one accountable lead, production in 4–8 weeks, every line of code and every model assigned to you.
Because in banking, the software is the regulated product — and the cost of a wrong answer is a fraud loss, a compliance breach, or a customer’s money in the wrong place. A bank’s stack is rarely short of data; it is short of safe ways to move.
Decades-old core systems sit behind brittle integrations, customer data is fragmented across channels, fraud rules fire thousands of false positives that bury the real signal, and every new feature has to clear security, audit, and a regulator before it ships. The result is the failure mode that defines the industry: change is slow because change is dangerous, and the institutions that can’t ship safely fall behind the ones that can.
That caution is rational — but it is also expensive. The gap isn’t ambition; it’s the engineering that lets a regulated institution ship modern software without putting money, data, or its license at risk. That is what banking software development, done properly, delivers.
This isn’t one product. It’s a set of high-leverage applications that sit on top of your core and ledger systems. 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 web and mobile experiences customers actually use — onboarding, account servicing, transfers, statements, alerts — wired to the core so balances and transactions are real-time, not overnight. Benefit — fewer drop-offs, lower branch and call-center load, and a digital channel that retains customers.
For example, a customer opens and funds an account from their phone in one sitting instead of abandoning a multi-day paper process — so the application converts instead of stalling at a branch visit.
Learns the normal pattern of an account’s activity and scores transactions for fraud in real time, flagging the genuine anomaly while letting legitimate activity through — instead of a static rule that blocks good customers and misses new schemes. Benefit — more fraud caught earlier, with fewer false declines frustrating real customers.
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 normal grocery run clears untouched — catching the fraud without the false decline that drives a service call.
Assembles the data behind AML/KYC, transaction monitoring, and regulatory filings into auditable, repeatable pipelines — replacing the manual spreadsheet assembly that eats analyst time and invites error. Benefit — lower compliance cost, faster filings, and an audit trail examiners can follow.
For example, a suspicious-activity review that took an analyst hours of pulling data from five systems is pre-assembled into one reviewable case with its evidence attached — so the team reviews and decides instead of hunting for records.
Answers balance, transaction, payment, and account questions across chat and voice from your own systems of record — and routes anything sensitive or low-confidence to a banker. Benefit — faster resolution, reclaimed agent capacity, and 24/7 servicing without after-hours gaps.
For example, a customer disputing a charge at midnight gets the transaction details and the dispute started immediately, with the case handed to a specialist in the morning — instead of waiting on hold the next day.
Builds the data and decisioning layer behind loan 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 denied applicant gets a defensible explanation and the lender keeps an examinable record — rather than reconstructing the rationale later.
Wraps, exposes, and incrementally modernizes the legacy core through APIs and a stable integration layer — so new products ship against modern interfaces without a rip-and-replace of the system the bank runs on. Benefit — modern features at a safe pace, and far less risk than a big-bang core replacement.
For example, a new digital product launches against an API layer in front of the core in weeks, instead of waiting on a multi-year platform migration before the bank can move.
The scope below is the application and intelligence layer — the digital experiences, models, and pipelines that run on top of your core. We build software for banks; we do not sell a chartered core-banking platform as a regulated product, and we integrate with your core rather than replacing it.
Web and mobile banking experiences — onboarding, servicing, transfers, statements, notifications — built against your core through a stable integration layer so customer-facing data is real-time and consistent.
We build the machine-learning models that score transactions and behavior for fraud and risk, validated against your historical data so alerts are trustworthy — not a false-positive generator analysts learn to ignore — with human-in-the-loop review on the decisions that matter.
Auditable, repeatable data pipelines behind AML/KYC, transaction monitoring, and regulatory filings — built on trusted data through our data engineering work, so the numbers reconcile and the audit trail holds up to examination.
Chat and voice servicing wired to your systems of record, answering account questions and starting routine workflows, with sensitive or low-confidence interactions routed to a banker rather than guessed at.
Banking software lives or dies on security. We build under the access controls and review gates your institution already runs — scoped permissions, encryption, audit logging — folding in our secure software and DevSecOps practices rather than bolting security on at the end.
We expose and incrementally modernize the legacy core through APIs and an integration layer — modernizing the brittle pieces without a big-bang replacement of the system the bank depends on, so new products ship at a safe pace.
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 regulated institution — one accountable lead, fixed scope, no handoffs to account managers.
Scope the use case and the outcome it targets (fraud loss, false-decline rate, filing time, digital conversion), and confirm the data, controls, 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 the core, ledger, and systems of record through governed, scoped, read-where-possible integrations, inside the access controls and audit logging your security team already runs. We read from your core; we do not replace it.
Output: a trusted, auditable data foundation
Develop the application and, where it applies, train and validate the model against your historical data, in your own cloud or on-prem environment, with a security review and an audit trail built in from the start.
Output: a working system tested on your real data, not a demo
Ship behind a staged rollout (shadow mode, then a pilot, then wide), prove the metric moves and the controls hold, 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.
A system that touches money, fraud, or a regulatory filing has to be right, stay right, and prove it was right — which is exactly the production discipline Silicon Prime is built on.
To be straight about it: our named case studies are not in banking. What transfers directly is the engineering rigor a regulated, money-critical system demands, and we’ll show it through honestly-labeled adjacent work rather than a banking case we don’t have.
The closest financial-domain work is YardClub — a marketplace we built end to end, including its payments and transaction infrastructure, which processed $120M+ in transactions before the company was acquired by Caterpillar in 2017. That is transaction and money-movement engineering, adjacent to banking, though a marketplace is not a regulated bank.
The clearest proof of production reliability is BJ’s Restaurants, a 200+ location operation whose software is critical to daily operations: over four years we moved their release cadence from every two weeks to twice a week while sustaining zero critical defects, through evals before release, staged rollout, and continuous production monitoring. Different industry — but precisely the “ship fast, never break the thing operations depend on” standard a fraud engine or a core-integration layer has to meet.
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.
The application layer, honestly scoped. We build the digital experiences, models, and pipelines on top of your core — not a chartered core-banking platform sold as a regulated product. That boundary means a faster, lower-risk engagement and no overpromising on a regulated-platform claim we don’t make.
Auditable and secure by design. In banking the audit trail and the access controls are the product. We build inside the security gates and logging your institution already runs, folding in DevSecOps from the start — not bolting compliance on at the end.
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 in a regulated setting an unexplainable model 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 examine them when we step back.
What banks and banking institutions want to know before they commit to building.
No — we’re a software engineering and AI firm that builds the application and intelligence layer banks run on top of their core: digital-banking experiences, fraud and risk models, regulatory-reporting pipelines, and customer-servicing systems. We integrate with the core you already run rather than replacing it, and we don’t sell a chartered core-banking platform as a regulated product. Keeping that boundary clear is part of why our engagements are fast and lower-risk; for a core platform itself we’ll point you to the right vendor and build the layer around it.
We’ll be straight: our named case studies are not in banking. The closest financial-domain work is YardClub — a marketplace we built end to end, including its payments and transaction infrastructure ($120M+ processed, acquired by Caterpillar) — which is money-movement engineering adjacent to banking, though not a regulated bank. Our deepest production-reliability proof is BJ’s Restaurants, a 200+ location operation held at zero critical defects for four years. For a first banking engagement we scope a contained pilot to prove the value before you commit — and the accountability is the founder’s, in writing.
The software runs in your own cloud or on-prem environment under your access controls; integrations to the core and systems of record are scoped, permissioned, and audit-logged; and every engagement starts with an NDA and a security review. We document every data path so your security, risk, and compliance teams can verify rather than trust, and we design within the regulatory constraints you operate under rather than discovering them late. We build the software and controls; your institution remains the accountable regulated entity.
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 we design human-in-the-loop review into the decisions that carry regulatory or customer-impact weight. Independent results show the upside is real: Mastercard reports its gen-AI fraud technology doubled compromised-card detection (May 2024). 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.
Usually, yes — and incrementally. Rather than a big-bang core replacement, we expose the legacy core through APIs and a stable integration layer and modernize the brittle pieces a step at a time, so new digital products ship against modern interfaces while the system the bank runs on stays intact. Where a component is genuinely too brittle to build on, we’ll be honest about it and scope the safest path — but the default is to add value alongside the core, not to gamble the institution on a platform migration.
Our fintech software development page covers fintech and payments companies — startups and scale-ups building new financial products. This 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 regulatory reporting, and core-system modernization. The engineering rigor is shared; the regulatory weight, the legacy-core reality, and the audit burden are heavier here, and the work is scoped for that.
You do — completely. The applications, 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. If you want to understand why so many bank-AI pilots stall before production, our analysis of why enterprise AI projects fail is the honest version.
Thirty minutes · No pitch deck
Bring the problem you want to attack — fraud losses, a slow digital channel, a manual compliance grind, a legacy core you can’t build on — and we’ll tell you honestly whether the data and controls support it, what it takes to build, and what it costs to run.