1. Regarding the 1st party fraud problem, there are fraud losses in the bad debt P&L, usage is monitored by the FMS, applications are handled by Credit risk, and nobody generally owns first-party fraud. Concretely, what type of solution can fix an organizational problem? Software doesn’t change reporting lines…
it is right that software alone doesn’t fix org charts, but the silo persists because fraud and credit run on separate systems with separate data. When application fraud scoring, credit decisioning, and intent-to-pay signals run in one orchestrated decision flow, you can finally tag an account as first-party fraud risk at origination and pass that flag downstream into collections strategy. The output, in addition to delivering a decline/approve, can be a shared risk label both teams act on. Several of our clients started exactly there: a joint fraud-credit decision workflow, before touching any reporting line.
2. By the time we detect a fraud, the handset is often already shipped. What new signal do you suggest to use, and at what latency?
The honest answer is: probably no single signal you don’t have yet. The gap is combining them in real time at the decision point : device fingerprint change, behavioral velocity, network signals via CAMARA/Open Gateway APIs… most operators have access to these, but they sit in different tools and never converge before the order is approved. We believe orchestrating those calls in a waterfall within the transaction, milliseconds, not batch, is the right approach.
3. We see fraud-as-a-service kits iterating daily, but my rule team takes up to 3 weeks to deploy a change through IT or requires a change request to our vendor. Every vendor says “AI fights AI”, but can you concretely explain how to better manage time-to-deploy for a new model or rule, and who needs to be in charge?
Fair challenge, and it’s the right metric to buy on. In our platform the fraud team owns the decision logic: low-code, so a rule change or a champion/challenger test is deployed by your analysts in hours, outside usual IT release cycles. On the model side, you can drop in a retrained model alongside the live one, route 10% of traffic, and compare before switching. The point isn’t that our AI is smarter than theirs, but very design of the solution makes the iteration loop become shorter, which is the only durable advantage.
4. We get blamed for fraud losses as well as for abandoned carts. Can you share real numbers that back the frictionless security headline? Referral rate, false positive reduction… and how do you also measure the revenue side?
We’d never quote a universal number, because is primarily depends on the baseline referral rate. What matters is the mechanism. A risk-based waterfall, where low-risk applicants pass on cheap passive checks; data costs and friction are only escalated for the ambiguous middle. That alone typically collapses the manual review queue, because most referrals today are caused by blunt, one-size-fits-all rules. And because the decisioning and the data are in one place, we build the conversion dashboard next to the fraud dashboard. And our simulation capability makes it easy to assess instantly the impacts on both conversion and loss. That dual KPI view is, frankly, how fraud managers can better defend their budget.
5. We’ve got a 15-year-old FMS that’s excellent at IRSF, but useless at onboarding, plus a credit engine, plus a KYC vendor, plus device intelligence. You’re proposing to be tool number five. That’s yet another integration project in our IT backlog. All that for just orchestration. Realistically, when could we expect decisions in Production?
Your 5th tool objection would be legitimate if we were another point solution. Although we explained how powerful our proprietary Fraud block is, our platform sits as the decision layer that calls your existing four vendors through pre-built integrations from our data marketplace, so the business case is consolidating the decision, not replacing the tools. Typical first use case: say, online subscription with device financing. It goes live in weeks because there’s no rip-and-replace: your FMS keeps doing IRSF, we handle the onboarding decision it was never designed for. And over time you get leverage on those four contracts, because you can swap a data provider in the waterfall without touching the journey.
6. What innovations are you seeing across the eco system to combat this?
The biggest innovation is what could be called Open Telco: GSMA Open Gateway and CAMARA APIs turning network intelligence into usable fraud signals. APIs such as SIM Swap, Number Verification, Device Swap, KYC Match or Number Recycling help detect account takeover, fake onboarding, mule activity and social-engineering risks in real time.
But these signals are not a standalone answer. They become powerful when combined with device intelligence, behavioural analytics, graph analysis, AI-assisted investigation and dynamic step-up controls. The real innovation is orchestration: using better ecosystem signals to make faster, proportionate and explainable fraud decisions.
