AI & Software Development for Fintech
Teamvoy builds the software and the AI that sits inside regulated financial products: fraud and risk decisioning, AML workflows, customer-facing assistants, and the platform underneath them. Every model decision is explainable before it reaches production.
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Teamvoy has delivered engineering work for Nasdaq, Panasonic, Afriland First Bank, and 50+ other companies since 2013.
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AI & Software Development Services We Offer Fintech Teams
Six areas of work. Each is scoped and priced on its own, so you can start with the one carrying the most risk or the most manual effort.
Fraud and risk decisioning
Real-time scoring inside your latency budget, with the decision, the features behind it and the model version written to an audit record you can show a supervisor.
– Real-time transaction scoring
– Feature pipelines and drift monitoring
– Explainability and reason codes
– Challenger and champion model rollout
– Decision audit records
AML and compliance workflows
Alert triage, case enrichment and narrative drafting that reduce analyst load without removing the analyst. Every automated step leaves a record of what it saw and why it acted.
– Alert triage and prioritisation
– Case enrichment from internal sources
– Sanctions and PEP screening integration
– SAR narrative drafting with human sign-off
– False-positive rate measurement
Customer-facing AI
Support assistants, onboarding help and in-product guidance built with retrieval over your own content, refusal behaviour on anything that constitutes advice, and a boundary your PII does not cross.
– Retrieval over your own documentation
– Guardrails and refusal behaviour
– PII boundary and redaction
– Escalation to a human agent
– Conversation quality evals
Fintech platform engineering
The product itself: ledgers, payment integrations, onboarding, servicing and back office. The part that has to be correct before any model is worth adding to it.
– Ledger and balance services
– Payment rail integration
– Onboarding and servicing flows
– Back-office and operations tooling
– Reconciliation and settlement
Data platform and feature infrastructure
The pipelines, feature store and lineage that decide whether a model can be trusted. Most failed fintech AI is a data problem wearing a model problem costume.– Event pipelines and streaming
– Feature store and point-in-time correctness
– Data lineage and retention policy
– Data residency and boundary controls
– Warehouse and reporting layer
Model operations and evals
The eval suite, monitoring and rollback path that keep a model honest after launch. Written before the model ships, not after the first incident.
– Eval harness build and maintenance
– Drift and regression detection
– Per-call cost budgeting
– Model fallback and provider resilience
– Automated rollback
Not sure whether your data can
support the model yet?
Why choose Teamvoy for AI and software development in fintech?
Four positions we hold on building automated decisions inside a regulated business.
The engineers who scope the work build the work
You own the source code from the first commit
A model decision you cannot explain does not ship
We will tell you when the answer is not a model
One automated decision, seven layers
An approve or decline looks like one call. It is seven handovers, and each one carries a failure mode that a supervisor will eventually ask about.
01. Event capture
The transaction, application or message arrives and is written to an immutable event log. Breaks when the log is lossy and a decision cannot be reconstructed six months later.
02. Feature and context assembly
History, device signals and reference data are gathered into the input the model sees. Breaks on point-in-time leakage, where a feature carries information that did not exist at decision time and the model scores far better in testing than in production.
03. Model or agent call
The classifier, the language model or the agent loop runs against that input. Breaks when the latency budget is set by the product and the model was chosen without one.
04. Policy and guardrails
Hard rules run after the model: exposure limits, prohibited outcomes, jurisdiction checks.\r\nBreaks when policy is folded into the model instead of sitting outside it, and a rule change becomes a retraining project.
05. Decision write-back
The outcome is written to the ledger, the case system or the customer-facing surface. Breaks when a timeout is retried without an idempotency key and one application is declined twice.
06. Human review and escalation
Borderline and high-impact cases route to an analyst with the reasons attached. Breaks when the queue is tuned for model confidence rather than analyst capacity, and the backlog becomes the real control.
07. Audit, evals and monitoring
Every decision, its inputs, its model version and its reviewer are retained and measured. Breaks when drift is only noticed through complaint volume, which is months after it started.
Fintech AI and Software Development Success Stories
Eliminating the 90% Noise: How Teamvoy Re-Engineered Trade Surveillance Software for a Global Exchange
AI-Native Engineering for Faster Time-to-Market
High-Performance Web Platform for Scalable Trade Surveillance
We engineer for:
What our fintech engineers work in.
Frontier and open models
Agent frameworks and infra
Fintech platform
Data, streaming and storage
Eval, observability and cloud
How to start an AI or software project in fintech?
There are three ways to start, depending on how well defined the problem is and how ready your data already is.
Readiness Audit
We review the workflow, the data behind it, the integration surface and the regulatory footprint, then write down what is buildable now and what needs data work first. You keep the document either way.
First Workflow
One decision path built and running in your environment, with the eval suite, the guardrails and the audit record in place before it sees production traffic.
15-Min Technical Call
A direct call with a senior engineer about the workflow, the data behind it, and whether a model is the right answer at all.
How our fintech AI and software development compares.
A practical comparison of scoping, data readiness, explainability, ownership and exit.
Talk To A CTO About Your Fintech AI Project
Talk to a Chief Technology Officer on the first call.
