Fintech software that passes the audit, not just the demo.
Moving money means your platform is judged by bank partners, auditors and regulators as much as by customers. We help fintechs and financial services firms build and review systems that hold up to that scrutiny, and apply AI where it genuinely helps: fraud triage, KYC operations and support. Our engineers work AI-first, with senior review on every change that touches money or customer data.
What bank partners, auditors and regulators will ask about your platform
Requirements vary by licence, product and country, and your compliance team or counsel should lead on interpretation. These are the technical questions that come up in almost every fintech partner onboarding, audit or due diligence we see.
Can you prove the ledger is correct?
Double-entry records, immutable history and daily reconciliation against bank and processor statements, with breaks investigated rather than adjusted away.
What happens when a request is retried?
Idempotency keys and careful state handling stop a network timeout from becoming a duplicate payment.
Who can access production data, and how is it logged?
Least-privilege access, separation of duties for releases and tamper-evident audit logs are standard expectations.
How are card data and secrets kept out of scope?
Tokenisation through your payment provider shrinks PCI DSS scope; keys belong in a managed key service, not in config files.
How do you manage third-party and outsourcing risk?
Including AI providers: where data is processed, retention terms, and what happens if a vendor fails.
Is there a tested incident and recovery plan?
Backups that have actually been restored, defined recovery objectives and a notification process for customers and partners.
Where AI earns its place in fintech operations
The best AI use cases in financial services rarely make autonomous decisions about customers. They take repetitive analysis off skilled people and leave the decision, and the accountability, with them.
Fraud alert triage
Summarise the account history, device signals and related transactions behind an alert so analysts clear false positives faster and focus on real cases.
KYC document processing
Extract and cross-check data from identity documents, proof of address and company registries, routing mismatches to a human instead of approving silently.
AML investigation support
Draft case narratives from transaction monitoring alerts for an investigator to review and edit, with every statement tied to underlying records.
Reconciliation exceptions
Classify and suggest matches for unreconciled items across bank files, processor reports and the internal ledger.
Policy-grounded support assistants
Answer customer and staff questions from approved product terms and procedures, with clear escalation for disputes and complaints.
Credit decision support, carefully
Models can inform underwriting, but explainability, fairness testing and model risk governance must come first. We will be candid about where not to use them.
Embedded finance provider, licensed core platform or custom build?
Most fintechs combine these. The question is which layer you own, because that decides your speed, your margin and your compliance burden.
| Embedded finance or BaaS provider | Licensed core platform | Custom build on regulated partners | |
|---|---|---|---|
| Time to first product | Fastest | Moderate, heavy configuration | Slowest, most flexible |
| Control of ledger and experience | Limited to the provider model | Good within platform limits | Full |
| Compliance and security burden | Shared, partly with provider | Shared with vendor | Largely yours |
| Cost profile | Per-account or per-transaction fees | Licence plus implementation | Engineering and ongoing ownership |
| Main risk | Partner dependency and pricing | Vendor lock-in | Underestimating operational rigour |
An AI-native team lowers the build cost of a custom layer, but it does not reduce the controls you must operate once you own the ledger.
AI-native engineering in a regulated fintech codebase
AI coding agents are a strong fit for much of fintech engineering: integration adapters for banking and payment APIs, exhaustive test suites for fee and interest calculations, data migration scripts and documentation that auditors actually ask for. Our engineers run several agents in parallel on this work, which typically shortens delivery and frees senior time for the parts that deserve it.
Those parts are the ones where a subtle mistake becomes a regulatory or financial incident: ledger design, concurrency, rounding and currency handling, authorisation rules and anything that touches personal or card data. They are designed by experienced engineers and every AI-assisted change is reviewed before merge, with the review recorded so your change management evidence exists without extra paperwork.
We also keep production customer data away from AI development tools, use synthetic or masked data for testing, and make sure the AI services used in delivery sit under terms your risk team would accept.
Questions we often hear
How is AI used in fraud detection?
Machine learning models score transactions and account behaviour for signs of fraud, such as unusual devices, velocity patterns or account takeover signals. Newer generative AI tools help analysts by summarising evidence behind alerts and drafting case notes. The most effective setups combine models, rules and human review, and are tuned to reduce false positives that frustrate genuine customers.
Can AI-generated code be used in financial software?
Yes, provided it goes through the same design, review and testing standards as any other code. AI tools are well suited to tests, integrations and documentation. Logic that handles balances, rounding, concurrency and access control needs an experienced engineer to design and review it, with evidence of that review kept for audits.
Do I need PCI DSS compliance for my fintech app?
If your systems store, process or transmit cardholder data, PCI DSS applies to them. Most startups reduce their scope significantly by using a payment provider that handles card data through hosted fields or tokenisation, so raw card numbers never reach their servers. Your acquirer or payment provider will confirm which validation level applies.
Do you work with MAS-regulated or other licensed firms?
We are Singapore-registered and familiar with the kinds of technology risk expectations regulators such as MAS set out, as well as common international frameworks. We support your compliance and risk teams with architecture, controls and documentation, but interpretation of regulatory obligations stays with them and your advisers.
How long does it take to build a fintech MVP?
Built on an established payment or embedded finance provider, a focused fintech MVP can often be delivered in a matter of weeks by an AI-native team. Owning your own ledger, holding a licence or integrating directly with banks adds considerable time for controls, testing and partner onboarding, so plan for months rather than weeks.
Related reading and tools
Tell us about your financial product.
Launching, scaling or preparing for a bank partner review? Share where you are and what worries you. We will reply within 24 hours with a candid view of the technical and AI priorities.
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