Industry · Banking
We build the software banks run on top of their core — digital-banking experiences, fraud and risk models, regulatory-reporting pipelines, and servicing systems — grounded in your data and wired to the core and ledger you already run. The application and intelligence layer, not a chartered core sold as a regulated product.
On top of your core, not replacing it
The real problem
Because in banking the software is the regulated product, and a wrong answer is a fraud loss, a compliance breach, or money in the wrong place. A bank's stack is rarely short of data; it's short of safe ways to move.
Decades-old cores sit behind brittle integrations, data is fragmented, fraud rules bury the real signal, and every feature must clear security, audit, and a regulator. Change is slow because change is dangerous — and the gap is the engineering that lets a regulated institution ship without risking money, data, or its license.
Of payroll spent on compliance at the smallest banks — versus 6–10% at the largest. Fixed overhead that won't scale down.
Of U.S. banked households use mobile banking as their primary access — so the digital experience is the bank.
Where it earns its keep
Not one product — high-leverage applications on top of your core and ledger. Illustrations show how the technology helps, not Silicon Prime client results.
Web and mobile experiences customers actually use — onboarding, servicing, transfers, statements, alerts — wired to the core so balances are real-time. Fewer drop-offs, lower branch load, a channel that retains.
A customer opens and funds an account from their phone in one sitting, instead of abandoning a multi-day paper process.
Learns an account's normal pattern and scores transactions for fraud in real time — flagging the genuine anomaly, letting legitimate activity through. More fraud caught earlier, fewer false declines.
A card-not-present charge that breaks the pattern is held for step-up verification in milliseconds, while the normal grocery run clears.
Assembles the data behind AML/KYC, transaction monitoring, and filings into auditable, repeatable pipelines — replacing manual spreadsheet assembly. Lower cost, faster filings, an audit trail examiners can follow.
A suspicious-activity review that took hours across five systems is pre-assembled into one reviewable case with its evidence.
Answers balance, transaction, and account questions across chat and voice from your systems of record — routing anything sensitive to a banker. Faster resolution, reclaimed agent capacity, 24/7 servicing.
A customer disputing a charge at midnight gets the details and the dispute started immediately, with the case handed to a specialist by morning.
Builds the data and decisioning layer behind origination and underwriting — pulling signals, applying policy, surfacing the explanation — a human owning every adverse decision. Faster, consistent decisions with fair-lending documentation.
A denied applicant gets a defensible explanation and the lender keeps an examinable record, instead of 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. Modern features at a safe pace, far less risk.
A new digital product launches against an API layer in front of the core in weeks, instead of waiting on a multi-year migration.
In banking, the software is the regulated product. A wrong answer is a fraud loss, a compliance breach, or money in the wrong place. We build inside your access controls and audit logging, with a human gate on every consequential decision.
Third-party evidence · each figure dated & sourced
Independent, named-source findings — cited as industry evidence, not Silicon Prime's own client results.
In annual value generative AI alone could add to banking — equal to 2.8–4.7% of industry revenues, if its use cases were fully implemented.
Card-fraud detection. Mastercard reports its gen-AI tech doubled detection of compromised cards and sped at-risk-merchant identification 300% — a vendor-reported result, cited as evidence of what fraud models can do.
Use mobile banking as their primary access. Of U.S. banked households — confirming the digital experience is, for most customers, the bank.
We set the baseline each system targets — fraud caught, false-decline rate, filing time, digital conversion — at kickoff and report against it, so the value is measured, not assumed.
What's included
The application and intelligence layer on top of your core — we build software for banks; we don't sell a chartered core, and we integrate with yours rather than replacing it.
Web and mobile experiences — onboarding, servicing, transfers, statements, notifications — built against your core through a stable integration layer so customer-facing data is real-time and consistent.
Machine-learning models that score transactions and behavior for fraud and risk, validated against your historical data so alerts are trustworthy — with human-in-the-loop review on the decisions that matter.
Auditable, repeatable data pipelines behind AML/KYC, transaction monitoring, and filings — built on trusted data 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 DevSecOps rather than bolting it 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, so new products ship at a safe pace.
What you get — all assigned to you under full work-for-hire IP transfer
How it runs
The same delivery model behind all our work, tuned for a regulated institution — one accountable lead, fixed scope, no handoffs.
STEP 01
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 must live within.
Output: a ranked plan & the metric we'll be judged on
STEP 02
Connect to the core, ledger, and systems of record through governed, scoped, read-where-possible integrations, inside your access controls and audit logging. We read from your core; we don't replace it.
Output: a trusted, auditable data foundation
STEP 03
Develop the application and, where it applies, train and validate the model against your historical data — in your own cloud or on-prem, with a security review and an audit trail built in from the start.
Output: a system tested on your real data, not a demo
STEP 04
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
The track record
A system that touches money, fraud, or a filing has to be right, stay right, and prove it was right. To be straight: our named case studies are not in banking — what transfers is the engineering rigor a money-critical system demands.
A Stanford-rooted Responsible AI lab, founded 2011, run by founder Kelvin Tran. If your problem is a genuine stretch, we'll tell you, scope a contained pilot to prove it, and put the accountability in writing.
Money-movement engineering · acquired 2017
A marketplace we built end to end, including its payments and transaction infrastructure, which processed $120M+ before Caterpillar acquired it. Money-movement engineering adjacent to banking, though not a regulated bank.
Production reliability · 200+ locations · 4 yrs
Software critical to daily operations across 200+ locations; over four years we moved releases from every two weeks to twice a week with zero critical defects. The "ship fast, never break what operations depend on" standard a fraud engine or core-integration layer must meet.
The application layer, honestly scoped. We build the digital experiences, models, and pipelines on top of your core — not a chartered platform sold as a regulated product. A faster, lower-risk engagement with no overpromising.
Auditable and secure by design. In banking the audit trail and access controls are the product. We build inside the security gates and logging you already run, 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 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.
Related work
For fintech and payments companies building new financial products — startups and scale-ups, where this page is for regulated banks on top of a core.
Fintech software →The fraud, risk, and decisioning models — validated against your historical data, with human-in-the-loop on consequential calls.
ML development →Security and audit gates built into delivery from the first commit — the access controls and logging a banking system lives on.
DevSecOps services →Thirty minutes · no pitch deck
Bring the use case and the core you run — we'll tell you honestly what's worth building, how it integrates without replacing your core, and what metric we'd be judged on.
