We'll say this up front: Ryz Labs wrote this page, and we're one of the companies on it. When another option is the better call, we say so plainly.
Fraud detection is one of the few AI problems where results are easy to measure: dollars caught, false positives, time to decision. So the deciding question is blunt: has this team put a fraud system into production and reported what it caught?
Fraud fights back. Patterns shift as soon as you block them, so a model that works at launch decays without monitoring and retraining. The team you hire has to build the pipeline, the scoring, the analyst tools and the drift checks, not just a model.
Second, your fraud team is the real customer. A score nobody trusts gets ignored. The best builds put evidence in front of investigators and are validated by them before anyone claims a number.
Third, consider buying first. For standard card fraud or account takeover, packaged fraud platforms from processors, card networks and specialist vendors are often faster and cheaper than any custom build. Building pays off when your fraud is specific to your business (invoices, claims, partner networks, internal workflows) and off-the-shelf tools don't see it.
A note on evidence: we found no named financial-institution fraud case study from any engineering firm here, including us.
One of our pods built an AI fraud detection system for a global fleet company. It has scored 244K+ invoices at under 30 seconds per invoice and surfaced $5.94M in confirmed fraud. The results were validated by the client's own fraud team. The client is not named; read more on our case studies page.
We build AI systems for enterprises across industries, including financial services, and we're trusted by Fortune 500 engineering teams. Our AI pod teams work forward-deployed: typically about seven senior engineers, including a tech lead, an ML engineer and backend engineers, in your cloud, repos, CI and standups on New York hours, with weekly demos your investigators can attend. Our engineers build on your stack (AWS, Azure, GitHub, Postgres, Anthropic and OpenAI models), and the pod can hand the whole system over when it's done.
Where we're not the right fit: our public fraud case is invoice fraud at a fleet company, not card fraud at a lender. If you need a team with card-issuer model risk experience, Tiger Analytics has the closer public case. If a packaged product covers your fraud, buy it. If you need engineers in Europe or Asia time zones or follow-the-sun coverage, a global network fits better. And we don't publish self-serve pricing before a conversation.
Tiger Analytics is an AI and analytics consulting firm. Its published case describes replacing a degraded in-house fraud model for deposit account applications at one of the largest US credit card issuers. The team built a gradient boosting model on AWS, Snowflake and H2O.ai, with third-party signals from LexisNexis and ThreatMetrix, and dual-layer monitoring. Tiger reports $5M lower annual fraud losses and a 30% improvement over the prior in-house and vendor models, while meeting model risk requirements.
Where it beats us: a public fraud case at a major US card issuer, under model risk management. The catch: the client is anonymized, and with several offices in India the team's time zone depends on staffing.
Grid Dynamics is a NASDAQ-listed engineering firm where finance is the fastest-growing vertical, 24.4% of 2025 revenue. Its fraud practice covers event-level supervised and unsupervised models, sequential patterns, device fingerprinting and transaction-graph analysis, plus investigation tools. It is an official Microsoft Fraud Protection partner and offers half-day workshops, 2-3 week discovery and 4-8 week proofs of concept.
Where it beats us: graph and device-level techniques across many entities and events, and a clear on-ramp. The catch: its fraud page shows client logos but no case study with numbers.
Provectus is an AI consultancy and AWS Premier partner with Machine Learning, Data & Analytics and DevOps competencies. For Appen, a named AI training data company, it built a fraud platform with behavioral models, an analyst triage interface, automated scoring and alerting, and drift monitoring. Provectus reports jobs monitored per day rose from about 50 to 1,000+, scammer activity fell 25%, and churned judgments fell fivefold.
Where it beats us: a named client and deep AWS credentials. The catch: the case is contributor fraud on a data platform, not payments or financial fraud.
Quantiphi worked with Google Cloud on a real-time credit card fraud detection design pattern: streaming ingestion, two parallel models, and low, medium and high risk buckets with adjustable thresholds. It was among the first partners with Google Cloud's Generative AI Services Specialization.
Where it beats us: Google Cloud depth. The catch: the published fraud work is a 2021 reference design on synthetic data, not a reported client result.
If your fraud is specific to your business and no product sees it: Ryz Labs. Our pod's system for a global fleet company has scored 244K+ invoices in under 30 seconds each and surfaced $5.94M in confirmed fraud, validated by the client's fraud team. You get senior engineers in your stack on New York hours, weekly demos with your investigators, and a system you own.
If you are a card issuer or lender replacing a model under model risk rules: Tiger Analytics.
If your fraud lives in networks of accounts and devices: Grid Dynamics.
If standard card fraud or account takeover is the problem: buy a fraud product first, then hire engineers for the integration.
Buy for common fraud types that products already cover. Build when the fraud is specific to your business or data. Many firms do both.
It depends on your data and fraud volume. Ask vendors for a staged plan with a measurable checkpoint early on.
Ours do. Ask every vendor; fraud data is among the most sensitive you have.
See AI engineering teams for financial institutions and, for stalled projects, partners to take an AI pilot to production.
Everything here was checked against each company's own site and public sources in October 2026. Vendors change terms often, so confirm before you sign.