Software development and AI pod teams for fintech

Ryz gives fintech companies senior Latin American engineers and dedicated AI pod teams that build the parts of a financial product that are hard to get right: ledgers, card and account programs, lending flows, KYC onboarding and fraud controls. Our engineers have shipped systems where every balance must reconcile and every partner audit asks for evidence. They work on US business hours, so they ship alongside your product team every day.

What we build for fintech teams

Fintech companies rarely hold every license themselves. They build on sponsor institutions, processors and data providers, and the engineering is shaped by those partners' requirements. Our teams build:

• Double-entry ledgers. Immutable journal entries, balance snapshots, multi-currency handling and idempotent posting, on Postgres, TigerBeetle or services like Modern Treasury.
• Card issuing programs. Integrations with issuer processors such as Marqeta, Lithic or Galileo, including authorization webhooks, spend controls, disputes and card lifecycle events.
• Account and money movement products. ACH, RTP and FedNow transfers, payouts, holds and returns, built around your partner institution's limits and review queues.
• Lending products. Application flows, underwriting rules, credit bureau pulls, repayment schedules, servicing and collections, for consumer, small business and buy-now-pay-later credit.
• Onboarding, KYC and KYB. Identity verification, sanctions screening and business verification flows with vendors such as Persona, Alloy, Socure or Middesk.
• Partner and program reporting. The daily files, reconciliations and compliance reports your sponsor institution or processor expects on time.
• Customer-facing apps. React Native, Swift and Kotlin apps with secure authentication, device binding and step-up verification. See our mobile app development work.

Where AI pods help in fintech

Fintech teams usually have the data and the product surface for AI, but not the spare senior engineers to build it properly. An AI pod builds the system in your cloud and runs it in production. Use cases that hold up:

• Fraud and risk scoring. Combining rules with models on device, behavioral and transaction signals, at authorization latency. The engineering challenge is feature freshness and a feedback loop from investigators and chargeback outcomes. One of our pods built fraud detection for a global fleet company that scores each item in under 30 seconds and has surfaced $5.94M in fraud confirmed by the client's fraud team.
• Support agents with account context. Agents that answer "where is my transfer" or "why was my card declined" from real account state, with strict tool permissions so the model can read but not move money. See AI agent development.
• KYC and document review. Extracting and checking fields from IDs, business formation documents and account statements uploaded by applicants, with confidence thresholds that route edge cases to an analyst.
• Underwriting assistance. Cash-flow analysis from linked accounts for credit decisions. Any model input has to survive fair lending review, so the pod documents features and the reason codes they produce.
• Compliance operations. Drafting suspicious activity narratives and summarizing alert history for an investigator who still makes the call.

More detail is on our case studies page.

Regulations and constraints our engineers work within

Our engineers have experience working within these requirements, alongside your compliance team and your partners. Ryz is not certified against any of them and does not certify your systems.

• PCI DSS v4.0.1. Scope reduction through tokenization, keeping card data out of logs and analytics, and the evidence your QSA or processor requests.
• Regulation E and Regulation Z. Error resolution timelines for electronic fund transfers, and disclosure requirements for consumer credit, both of which drive product and support tooling.
• ECOA, Regulation B and FCRA. Adverse action notices with specific reasons, and permissible purpose for credit reports.
• BSA/AML, CIP and OFAC. Customer identification, ongoing monitoring and sanctions screening, often defined in your partner institution's program agreement.
• UDAAP. CFPB scrutiny of unfair, deceptive or abusive practices, which reaches app copy, fee flows and AI-generated customer messages.
• State money transmission rules. Licensing and safeguarding requirements that affect how funds flow and are reported.
• SOC 2 and GLBA. The security controls enterprise customers and partners expect, and privacy and safeguards rules for consumer financial data.
• Nacha Operating Rules. ACH return rates, authorization requirements and the fraud monitoring obligations phased in during 2026.

Integrations and data

• Account data aggregators such as Plaid, MX and Finicity, and tracking the CFPB's Section 1033 personal financial data rights rule as it evolves.
• Processor and partner APIs: Stripe, Adyen, Marqeta and Lithic, partner-program platforms such as Unit, and the SFTP file exchanges many partners still require.
• Nacha ACH files, ISO 20022 messages for RTP and FedNow, and ISO 8583 card authorization flows.
• Credit bureaus (Experian, Equifax, TransUnion) and Metro 2 furnishing files.
• Event pipelines on Kafka or Kinesis into Snowflake or BigQuery for finance, risk and product analytics.

Partner files often arrive late or change format without notice, so our engineers build reconciliation that tolerates partial days, flags every unmatched item for finance and records how each break was resolved.

How teams engage Ryz

Staff augmentation is the most common model for fintech. You add senior engineers to existing squads: a backend engineer for the ledger, a mobile engineer for onboarding, a data engineer for partner reporting. They work on your team, in your repos and standups. Our hire fintech developers page describes the profiles.

An AI pod fits when you want fraud scoring, a support agent or document review built and running, without pulling engineers off the roadmap. A dedicated development team fits a scoped build, such as a new card program or lending product, that a Ryz team owns end to end.

Rates are $7,000 to $10,000 per month for mid-level engineers, $10,000 to $15,000 for senior engineers, and $15,000 and up for leads. Two senior engineers for nine months at $12,000 is $216,000. You see the plan, price and names before anyone starts.

When Ryz isn't the right fit

• You need an hourly freelancer for a two-week integration, or a self-serve trial first. A freelance marketplace is faster for that.
• You want a white-label platform or licensed fintech product. Ryz builds systems with your team; it does not sell software.
• You need engineers covering European or Asian hours or follow-the-sun support.

Related

• Payments software development
• Financial services software development
• Best staff augmentation for fintech
• Best fraud detection engineering teams
• API development

FAQ

How do your engineers handle regulated customer data?

They work in your environment and under your controls: your cloud accounts, SSO, device policies, secrets management and data access approvals. Your security team decides what production data they can see, and many fintech teams keep engineers on tokenized or masked data. Ryz does not hold PCI DSS or SOC 2 certification on your behalf.

Have your engineers worked with sponsor institution programs?

Yes. Our engineers have built products on partner institution and processor programs, including the reporting files, reconciliation jobs and review queues those partners require.

Can an AI pod build fraud scoring that runs at authorization time?

Yes, if your latency budget and data allow it. The pod designs the feature pipeline, model serving and fallback rules together so a slow model never blocks an authorization, and builds the investigator feedback loop that keeps the model current.

What does a fintech engineering team cost?

Engineers typically run $7,000 to $15,000 per month each, depending on seniority, with leads at $15,000 and up. Multiply team size by duration by rate for a project budget. Quotes are scoped per team.

How is this different from your hire fintech developers page?

That page is about adding individual engineers. This page covers the systems fintech teams build, the regulations that shape them and how AI pods fit, so you can decide which engagement model you need.

Questions we didn't answer? Email info@ryzlabs.com.