Fraud in Fintech Mobile Apps: Attack Vectors and How to Prevent Them
In fintech, a large share of customer onboarding and day-to-day operations already happens through mobile. That concentration has made mobile the primary target of fraud in fintech mobile apps.
Attackers are no longer limited to stealing passwords. They manipulate the device, spoof its identity to the system, and carry out fraud from the legitimate user’s own device.
This article reviews the most relevant vectors (device spoofing, app tampering, and the device fingerprinting techniques that help detect them), backed by verified data and documented industry examples.
What Attack Vectors in Fintech Mobile Apps Are
An attack vector is the specific path an attacker uses to compromise a fintech app: from installing a banking trojan to manipulating the device’s runtime environment.
In most documented cases, fraud in fintech mobile apps follows a recognizable sequence. The table below summarizes its typical phases.
| Phase | What happens | Attacker’s goal |
| Infection | Banking malware installed via sideloading, unofficial app stores, or phishing links | Gain persistence on the device |
| Environment reconnaissance | The malicious app detects root, jailbreak, emulators, or virtual cameras | Prepare for manipulation or spoofing |
| Signal capture | Overlays on top of the legitimate app, keylogging, interception of SMS one-time passcodes | Steal credentials and authentication factors |
| Fraud execution | Transfer or product sign-up carried out from the compromised device itself | Complete the operation without triggering classic alerts |
Table 1. Typical phases of fraud in fintech mobile apps.
According to ENISA’s Threat Landscape 2025 report, the EU cybersecurity agency, mobile threats are the category with the greatest weight in the distribution of incidents analyzed between July 2024 and June 2025: 42.4%, confirming that mobile devices remain a primary attack surface across the entire EU and all sectors, not just the financial one.
Fraud in Fintech Mobile Apps, by the Numbers
The available official data doesn’t always cover the same scope: some figures cover the whole EU and all sectors, others are limited to Spain, and others are specific to the financial sector. They should be read with that context in mind.
| Data point | Figure | Scope and source |
| Cybersecurity incidents managed by INCIBE in 2025 | 122,223 (+26% vs. 2024) | Spain — Balance de Ciberseguridad 2025 (Cybersecurity Report), INCIBE |
| Malware incidents (trojans, infostealers, botnets, spyware, and other variants; no specific banking breakdown) | 55,411 (45% of the total) | Spain — Balance de Ciberseguridad 2025, INCIBE |
| Online fraud incidents | 45,445 (4 out of every 10 incidents, +19%) | Spain — Balance de Ciberseguridad 2025, INCIBE |
| Most affected sector among essential operators (banking) | 34% | Spain — Balance de Ciberseguridad 2025, INCIBE |
| Malware incidents affecting credit institutions / individuals / crypto providers | 36% / 24% / 15% | EU, financial sector, Jan. 2023-Jun. 2024 — ENISA Threat Landscape: Finance Sector, based on 21 malware incidents (6% of the report’s 488 total incidents) |
| Cybersecurity incidents analyzed at EU level, all sectors | 4,875 (Jul. 2024-Jun. 2025) | EU, general context — ENISA Threat Landscape 2025 |
Table 2. Fraud in fintech mobile apps by the numbers (official sources; each row indicates its geographic and time scope).
These figures describe the scale of the problem, but they don’t explain why so much of that fraud is proving harder to detect than it was just two years ago. That calls for a closer look at the data.
What the Aggregate Figures Don’t Show
The usual headline (attacks on banking apps are rising) hides a deeper shift: much of the fraud no longer happens from outside the device, but from within it.
ENISA documents the resurgence of the Medusa trojan with new capabilities aimed at On-Device Fraud through account takeover, expanding its victim base to France and Italy. That expansion matters because its predecessor, TangleBot, already operated in Spain, Turkey, Italy, and France: the perimeter isn’t new, it’s being reinforced. The same report also cites BingoMod, which empties the victim’s accounts and then wipes the device to hinder forensic analysis.
In On-Device Fraud, the malware doesn’t exfiltrate credentials to operate from another device: it executes the transaction directly on the victim’s phone, using their already-authenticated session. That shift in pattern is what matters, because it redefines which fraud signals still hold up.
| Variable | Classic remote fraud | On-Device Fraud |
| Session origin | New device or IP, different geolocation | The same device and the same session as the legitimate user |
| Typical risk signal | Unknown device, geographic jump, different fingerprint | Almost no classic signal is triggered |
| Common vector | Phishing and reuse of stolen credentials | Malware with remote control or accessibility access over the device itself |
| Detection difficulty | Medium: established device and IP rules exist | High: requires behavioral and channel analysis |
Table 3. Classic remote fraud vs. On-Device Fraud.
INCIBE’s analysis of the TrickMo banking trojan illustrates this pattern well: the malware abuses Android’s accessibility services to overlay fake screens and record the device’s screen in real time, intercepting one-time codes received both by SMS and generated by authenticator apps. That nuance matters: it dismantles the idea that simply switching from SMS to an authenticator app is enough to close this vector.
This is why behavioral biometrics is gaining ground over controls based solely on device or IP reputation: when fraud happens within the legitimate device itself, it becomes necessary to observe how the user interacts, not just where they’re doing it from.
The Europol IOCTA 2025 report confirms this trend from another angle: the European agency documents an active market for buying and selling credentials and infostealers, which fuels precisely this type of attack carried out using sessions and legitimate devices that have already been compromised.
The report itself adds an uncomfortable nuance for any defense based solely on the device: infostealers don’t just steal passwords, they also extract tokens, cookies, and device fingerprints, allowing attackers to replicate the victim’s digital identity. The device fingerprint, used on its own, can also become stolen merchandise. That’s the strongest argument for combining it with behavioral biometrics and capture-source verification, rather than relying on a single control.
Specific Vectors: Device Spoofing and App Tampering
Device spoofing consists of making a system believe that a signal comes from a device or camera other than the real one. It ranges from virtual cameras to emulators that mimic a physical phone.
App tampering, in turn, is the manipulation of the application’s own code or runtime environment: reverse engineering, code injection, execution on rooted or jailbroken devices, or installing repackaged versions of the app modified with runtime hooking frameworks, all aimed at disabling security controls.
It’s worth distinguishing two variants that are often confused under the generic term “spoofing”: the difference between a presentation attack and an injection attack. The first fools the camera with a photo, mask, or video. The second bypasses the camera entirely and injects a fabricated video directly into the data stream, typically through a virtual camera.
This distinction explains why a vendor evaluated against presentation attacks can still be exposed to injection attacks: these are different controls addressing different questions.
Device fingerprinting acts as a countermeasure against both vectors. It analyzes hundreds of hardware, operating system, and device configuration signals to build a unique fingerprint and detect when that fingerprint doesn’t match the user’s usual profile or that of a legitimate device.
Injection attack defense solutions combine this device fingerprint with capture-source verification and forensic image analysis, so the system doesn’t just ask “is this a real face?” but also “did this signal really come from the legitimate device’s camera?”
We can distinguish genuine images, with natural reflections on the skin, from visually convincing manipulated ones that still carry imperceptible artifacts. However, only a multi-layered approach can detect attacks that use stolen, unmanipulated samples.
— Javier Barrachina, R&D Director at Facephi
Fintech Sector Challenges in the Face of This Type of Fraud
Fintechs operate under constant tension between frictionless onboarding, regulatory compliance, and a user base that includes rooted or jailbroken devices.
| Pain point | Why it matters | What it takes to solve it |
| 100% remote onboarding | Any gap in verification gets exploited at scale, with no physical presence to stop it | Injection and presentation attack detection built into the onboarding flow itself |
| Rooted or jailbroken devices in the user base | They disable native OS controls that the app takes for granted | Runtime environment signals combined with device fingerprinting |
| Growing regulatory pressure: PSD3 and the new Payment Services Regulation (PSR), DORA, and local equivalents | The PSD3/PSR compromise text (April 2026, pending formal adoption) shifts to the payment provider the responsibility of reimbursing the customer if it did not implement adequate fraud prevention mechanisms | Auditable evidence of every verification and every signal analyzed, not just the existence of the control |
| User experience vs. security friction | Adding verification steps reduces conversion and app usage | Continuous multi-biometric authentication that runs in the background |
Table 4. Fintech sector challenges in the face of mobile app fraud.
In the face of these challenges, combining behavioral biometrics, capture-source verification, and forensic image analysis keeps friction low for the legitimate user and high for the attacker, without relying on a single control.
Frequently Asked Questions About Fraud in Fintech Mobile Apps
It’s the technique of passing off a fake signal (a virtual camera, an emulator, or manipulated location data) as coming from a real, legitimate device. The goal is to make the verification system believe the operation is coming from a physical phone and a real person.
It involves manipulating an application’s code or runtime environment, for example through reverse engineering, code injection, or execution on rooted devices, in order to disable or bypass its security controls.
Device fingerprinting analyzes hundreds of hardware, operating system, and configuration signals to build a unique device fingerprint. When that fingerprint doesn’t match a user’s usual profile or shows signs of emulation, the system can step up verification before authorizing the operation.
A presentation attack shows something fake in front of the camera, such as a photo or a mask. An injection attack bypasses the camera and inserts a fabricated video directly into the data stream, typically using a virtual camera or an emulator.
Because executing fraud from the legitimate user’s already-authenticated session avoids classic risk signals, such as a new device or a different IP address. This forces anti-fraud systems to analyze behavior within the session, not just where it originates.
The ISO/IEC 30107-3 standard defines how presentation attack detection is evaluated; certification against it is issued by accredited laboratories. Injection attacks are covered by a more recent specification, CEN/TS 18099, approved by CEN in October 2024 for provisional application (an initial three-year validity period) and ratified in Spain by UNE in January 2025. ISO has also begun developing its own standard for injection attacks, ISO/IEC 25456, using CEN/TS 18099 as a starting point.
