Your user growth may be lying to you.

For decision-makers in the global digital economy, verified users are the metric that drives valuation. Growth fuels investor confidence across digital banking onboarding, micro-lending platforms, superapps, and crypto exchanges in high-growth markets.

However, this metric contains a structural flaw.

As financial institutions scale remote identity verification and biometric onboarding, they are increasingly exposed to an industrialized adversary that distorts growth analytics and silently drains operational budgets: device farms and emulator-based attacks.

Unlike traditional identity theft, which targets accounts one by one, emulator attacks are engineered for scale. Criminal syndicates operate racks of thousands of smartphones — or virtualized device instances running on servers — to automate mass account creation.

For organizations, this creates what can only be described as a hidden tax on growth.

You are not just facing fraud risk. You are paying marketing acquisition costs, biometric API fees, cloud processing resources, and manual review hours to onboard thousands of synthetic users. In high-growth fintech environments, bot traffic can reach double-digit percentages of new registrations, materially distorting both unit economics and risk models.

The Mechanics of Industrialized Fraud

To understand the scale of the issue, we must examine the attack economics.

Manually bypassing a liveness check on a single device is slow and unscalable. Device farms and emulators remove that constraint entirely.

Emulators
Software environments that replicate Android or iOS devices on desktop infrastructure. A single machine can run hundreds of parallel onboarding sessions simultaneously.

Device Farms
Large arrays of physical smartphones controlled by automation frameworks interacting directly with your mobile biometric SDK.

When combined with virtual camera software, attackers execute injection attacks. Instead of presenting a face to a physical lens, they inject a pre-recorded or AI-generated video directly into the application’s data stream.

The result is synthetic traffic that behaves like legitimate users — until financial damage occurs.

Why Legacy Biometrics Are Blind to Emulators

The core issue is simple: traditional biometrics validate the face, but not the device capturing it.

Many institutions have invested heavily in KYC stacks architected for yesterday’s threat model. First-generation biometric systems focus primarily on Presentation Attack Detection (PAD), analyzing optical artifacts such as glare, blur, or depth inconsistencies to determine whether a mask, screen, or photo is being presented.

However, in an emulator or injection attack, the video stream is digitally pristine. Because the video is inserted directly into the API or virtual camera driver, it lacks the optical imperfections of a real sensor.

This creates two compounding problems:

The Technical Gap
Legacy systems that only validate “Is this a face?” correctly answer “Yes.”
They fail to ask the critical preceding question: “Is this a real device?”

The Budget Leak
Even if downstream controls eventually flag suspicious behavior, organizations have already incurred the full biometric and processing cost of thousands of automated attempts.

In effect, many platforms are using high-precision biometric engines to filter what should have been blocked as synthetic device traffic.

The Economic Impact Across Sectors

While banks were early targets, the pattern is now consistent across the broader fintech ecosystem: wherever there is an incentive to sign up, automation follows.

1. Digital Lending & Multi-Finance — The Ghost Borrower

For P2P lenders and Buy Now, Pay Later (BNPL) platforms, the risk is existential.

Device farms create synthetic identities that pass standard due diligence, gradually build thin credit profiles, and then execute coordinated bust-outs — maxing out credit lines simultaneously before disappearing.

The damage is twofold:

• Direct fraud losses

• Long-term corruption of credit risk models trained on non-human behavior

2. Crypto Exchanges — The Sybil Economy

In crypto, user acquisition frequently includes airdrops, referral bonuses, or token incentives.

Attackers exploit emulators to generate thousands of accounts and wallets to harvest these rewards at scale — classic Sybil attacks. Without robust injection attack detection, exchanges may unknowingly distribute significant token value to a single coordinated actor.

Beyond financial loss, this creates regulatory exposure under tightening Crypto KYC and AML frameworks in jurisdictions such as Singapore and Hong Kong.

3. E-Wallets & Superapps — Promo Drain

For digital wallets and superapps, the immediate impact is marketing efficiency.

A $5 new-user incentive can be drained in hours by automated farms. Platforms report impressive top-of-funnel growth to investors, but retention collapses because the “users” never existed.

The result: inflated CAC, distorted growth metrics, and wasted subsidy budgets.

The Solution: A Device-Centric Defense

To stop this operational bleed, organizations must shift from a face-centric to a device-centric defense strategy. The objective is to detect automation tools before expensive identity processing begins.

At Oz Forensics, this is implemented through our Certified Injection Attack Detection (IAD) module.

1. Detect the Environment — The Zero-Cost Filter

Oz IAD analyzes the integrity of the mobile environment before biometric processing is triggered. It identifies:

• Virtual camera drivers

• Emulator environments and rooted devices

• Automation signatures typical of bot farms

• Video metadata anomalies (frame rate, sensor noise patterns)

By placing IAD at the start of the onboarding workflow, suspicious sessions can be blocked instantly — before biometric costs are incurred.

This ensures you stop paying to verify bots.

2. Biometric Assurance — ISO 30107-3

Once device integrity is established, Oz deploys advanced liveness detection aligned with ISO 30107-3 Level 2, independently tested by iBeta.

Organizations can choose:

• Active liveness for high-risk scenarios

• Passive liveness for maximum conversion

This layered approach ensures that the verified device is operated by a live human — not a mask, replay, or deepfake.

3. Certified Trust — CEN/TS 18099

In a market crowded with vendor claims, independent validation matters.

While ISO 30107-3 addresses presentation attacks, CEN/TS 18099 has emerged as the key technical specification for injection attack detection. Oz Forensics is among the few providers whose IAD module has been independently evaluated against this standard by BixeLab.

Deployment Flexibility: On-Premise & Data Sovereignty

For institutions operating under strict data residency or latency requirements, deployment architecture is critical.

Oz Forensics supports fully on-premise liveness and IAD deployment, enabling banks and fintechs to process biometric and device intelligence within their own secure infrastructure.

Benefits include:

• Compliance with local data sovereignty laws

• Reduced network latency

• Greater control over sensitive biometric workflows

• Consistent user experience in low-connectivity regions

Conclusion: Protecting Growth Economics

Security is no longer only about stopping fraud — it is about protecting the unit economics of digital growth.

Device farms and emulators inflate acquisition costs, pollute risk data, and silently erode marketing efficiency. By integrating certified injection attack detection at the start of the onboarding journey, organizations can eliminate this hidden tax on growth.

Every onboarding dollar should be spent on a real customer.

Stop paying to onboard bots. Start verifying real growth.

→ Assess your exposure to emulator-driven fraud with Oz IAD