Banking & FinTech AI solutions.

FinTech AI solutions for fraud detection, KYC automation and underwriting, on payment and lending systems where every cent reconciles. Every model decision comes with reasons an examiner can follow.

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What makes FinTech AI hard.

The FinTech AI challenges we design for before any code is written, so the AI holds up with real users, real data and real auditors.

Explainable decisions

Credit and fraud models need reason codes, bias checks, versioned features and change control an examiner can trace to a single decision.

Real-time risk

Fraud scores in milliseconds, with identical features in training and serving, so the model behaves in production as it did in testing.

Ledger integrity

Double-entry, idempotent money movement with reconciliation that holds up under retries and partial failures.

FinTech AI solutions we build

The AI use cases we build for FinTech teams, and the software platforms they run on. Each starts from a measurable goal, passes an eval gate before launch and reaches users through a gradual rollout.

The model work comes from our LLM and AI agent development team, tested by QA and AI evaluation. For results in production, browse our AI case studies.

Compliance we plan for

PCI DSS
SOC 2 Type II
SOX & FFIEC
ISO 27001

✓Streaming fraud detection and scoring
✓KYC / KYB automation with document AI
✓AI-assisted underwriting and decisioning
✓Payment orchestration and wallets
✓Ledger and reconciliation services
✓Customer-facing banking apps

FinTech software, shipped.

A recent FinTech engagement and the numbers the client measured. Names withheld where clients asked us to.

Lending decisioning

Manual underwriting compressed from 4 days to 11 minutes. Approval rate up 7 points without lifting the default rate. SOC 2 Type II in 7 months.

Browse AI case studies

FinTech AI development services.

The WAMO Labs services most FinTech AI engagements draw on, from LLM integration to the platform around it.

AI integration

Our AI integration services put LLM applications, AI agents and RAG systems into the products and workflows you already run. Measured by evals, guarded in production, with a cost per request you can predict.

Cloud architecture

Cloud architecture and DevOps that keep AI products fast, secure and affordable. MLOps pipelines, GPU inference and LLMOps on AWS, GCP or Azure, with costs you can forecast.

Mobile apps

Mobile app development for iOS and Android, with native polish and AI features users actually use: on-device ML, LLM assistants, voice and camera intelligence, all backed by solid APIs.

Questions about FinTech AI projects.

Timelines, cost drivers and compliance, answered plainly. More on how we build and evaluate AI in our AI engineering insights.

Which FinTech AI use cases do you build?

FinTech AI projects we build include streaming fraud detection and scoring; KYC / KYB automation with document AI; and AI-assisted underwriting and decisioning. Each one ships with a versioned eval set, guardrails and a gradual rollout, on the platform work listed above.

How long do FinTech AI projects take?

Most engagements start with a four-week immersion. By week 3 a first slice is live behind a feature flag for a small share of users, evaluated and instrumented. By week 4 you have a quarter-by-quarter roadmap with named owners. Full timelines depend on scope, integrations and compliance review.

What drives the cost of FinTech AI solutions?

Four things: the systems we integrate with (core banking, payment and identity-verification providers), the compliance scope (PCI DSS, SOC 2 Type II, SOX & FFIEC and ISO 27001), how much evaluation and human review the use case needs, and inference volume, because cost per request compounds with usage. We size all four during the four-week immersion, so you know what it costs to keep going before you commit to a roadmap.

How do you handle compliance and data privacy in FinTech AI?

We plan for PCI DSS, SOC 2 Type II, SOX & FFIEC and ISO 27001 from the first sprint and decide which data may ever reach a model provider before anything is built. Every AI feature ships with access controls, an audit trail and human review wherever a wrong answer carries real risk.

Building AI in Banking & FinTech?

Tell us the AI use case or product you're working on. We respond within one business day, with a real human and a real opinion.

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