For Risk & Fraud teams
Approve 25% more, at less risk. Catch fraud on day zero at onboarding.
Alternative credit signals for thin-file, NTC, and self-employed scoring. Real-time fraud detection using 800+ ecosystem signals.
Alternative credit signals for thin-file, NTC, and self-employed scoring. Real-time fraud detection using 800+ ecosystem signals.
Real outcomes, measurable by default - underwriting, fraud and collections in one decisioning stack.
Score thin-file, NTC, and self-employed applicants using 800+ ecosystem signals legacy models can't reach. Approve 25% more at zero additional risk.
Real-time fusion of device, identity, velocity, and ecosystem-behavior signals. Fraud rings caught at application - not discovered six months later in the chargeoff queue.
One interface: configure underwriting rules, monitor fraud patterns, audit signal decisions. No context-switching. Built for risk ops.
Build on your historical book, drop scores into your LOS, decide in real time, and monitor signal health - without re-platforming.
Build models on 800+ ecosystem features in LattIQ Studio or your own stack, then back-test on your historical book before going live.
One API call from your loan origination system returns credit tier, fraud probability, cohort and reason codes - under 500ms, no infra change.
Real-time scores at every decision point - onboarding, transaction, limit review, collections triage. Approve more, flag faster, lose less.
Continuous performance tracking, drift detection and signal-health alerts. We watch the signals so you can watch the portfolio.
Your credit team already runs decisioning. We'll show you exactly what 800+ ecosystem signals add-approval lift, fraud catch rate, model accuracy-on your own data. 30 minutes. No commitment.
Talk to our risk experts →800+ signals on 500M+ users, sourced directly from the origin - enterprise-grade consumer products and platforms that generate this data as part of their core business operations. No data brokers or middlemen, and no central data store: every signal is pulled fresh, on demand, just in time for your specific purpose.
No. LattIQ runs inside your own environment, so your data stays with you - first-party variables and labels are never moved or exposed, they're processed where they already live. Matching across the ecosystem relies solely on pseudonymized identifiers, so raw personal data is never exposed or exchanged.
Match rates vary from signal to signal. Because our partnerships focus on horizontal consumer platforms with very large user bases, we typically see hit rates of 85%+. The exact overlap for your customer base can be established during a proof of concept.
Yes. Risk teams and data scientists get full control over which signals to use, how to transform them, and how to build models that suit your portfolio - codifying your tribal knowledge along the way. The platform provides all the tooling and frameworks required to support an entire ML workflow, end to end.
Yes. The platform is DPDP- and GDPR-aligned by architecture, ISO/IEC 27001 certified, and also meets regulators' requirements for retrospective explainability and model risk management. Usage of ecosystem signals is approved by user consent on both the supply and demand side. All raw data is abstracted at source, so what you use in your models are crisp signals: aggregates and pre-engineered features, never raw personal data.