Device Intelligence for Fintech has emerged as a critical security layer as modern fraud evolves beyond identities into device-driven abuse. This article aims to help fintech platforms understand how multi-account abuse occurs, how it can be detected at scale, and why device-first fraud prevention is essential to protect growth and trust.
To understand why fintechs are rethinking fraud prevention, it’s important to first examine the new fraud reality they face.
The New Fraud Reality for Fintechs
Account takeovers and multi-accounting have become two of the most damaging fraud threats for fintech platforms in recent years. When left unchecked, they enable fraudulent transactions, money laundering, loan application fraud, and large-scale abuse of rewards; directly impacting revenue, compliance, and customer trust.
The problem is accelerating. According to the Veriff Fraud Report 2025, account takeover cases increased by 13% compared to 2023, while multi-accounting grew by 10% year-on-year. What makes multi-account abuse especially dangerous is speed. Once fraudsters gain access, they can create and exploit multiple accounts within minutes, causing losses that are hard to reverse.
Traditional defenses such as IP checks, OTPs, KYC, and cookies are no longer enough. Fraudsters have learned how to bypass these controls at scale, leaving fintechs with limited visibility into repeat abusers. This has made it clear that fintech platforms need a deeper detection layer. One that goes beyond identity and sessions, driving the shift toward Device Intelligence for Fintech.
How Device Intelligence Detects Multi-Account Abuse & Account Takeovers Across Fintech Platforms
Account takeovers and multi-account abuse are fundamentally device-driven crimes. Every fraudulent action starts from a device; whether it is account creation, login, abuse, or monetization. That’s why the only reliable way to detect repeat abusers is by understanding the device itself.
In this context, understanding a device means analyzing the signals it generates: its technical attributes, environment, and consistency over time. By correlating these signals over time, fintech platforms can detect risky devices early and identify patterns linked to multi-account abuse and fake accounts, before fraud escalates.
How Device Intelligence Identifies Multi-Account Abuse at Scale
Device intelligence enables fintech platforms to identify and link fraudulent devices at scale, even when fraudsters attempt to mask their identity across multiple accounts. Instead of relying on isolated signals, it focuses on the one constant behind repeated abuse, ’the device’ used to access the platform.
By compiling a wide range of attributes, device intelligence creates a persistent and unique device identifier (often referred to as a device fingerprint). This allows fintechs to recognize the same underlying device across different sessions, accounts, and user identities, making multi-account abuse easier to detect and contain.
To achieve this level of accuracy, device intelligence evaluates three core parameters that work together to establish device uniqueness and risk.
Deep Device Attributes (Hardware + Software)
A few of them include:
- Operating system and version
- Installed hardware characteristics
- Screen resolution and display parameters
- Installed fonts and keyboard layout
👉 Why these work: they’re hard to spoof consistently and remain stable across sessions and accounts.
Browser, App, and Network Configurations
A few of them include:
- Browser name, version, and user-agent string
- Browser privacy and cookie settings
- Installed browser extensions
- HTTP header attributes
👉 Why these work: abnormal combinations often indicate emulators, automation tools, or controlled environments.
IP, Geolocation, and Environmental Signals
A few of them include:
- IP address and network type
- Geolocation and time-zone alignment
- Browser language and operating system language
- Audio and HTML5 canvas fingerprinting data
👉 Why these work: they expose impossible travel, location spoofing, and coordinated multi-account activity.
How Device Intelligence Stops Multi-Account Abuse in the Fintech User Journey
The flowchart below illustrates how device intelligence enables scalable multi-account fraud prevention across key fintech user touchpoints.
What is Device Risk Scoring & its Role in Detecting Abusers Early
A device risk score (also known as a device trust score) is a dynamic numerical indicator assigned to every individual device that accesses a fintech platform. The score typically ranges from 0–100, 1–5, or A–F (based on preference), and it reflects how trustworthy that specific device is at any given moment.
If a device begins to exhibit risky characteristics (such as the use of emulators, GPS spoofing, or abnormal configurations), the score adjusts accordingly. This allows fintech fraud detection solutions to identify risky devices early, often before multi-account abuse or other fraudulent activity occurs.
👉 Why it works: By maintaining a unique risk score for each device, fintech teams can automate decisions, reduce manual reviews, and intervene before fraud escalates.
Here is an example:
Use Cases : Fraud Detection in Fintech using Device Intelligence
- WINK, a fully digital neobank in Costa Rica, needed to secure instant account onboarding while strengthening fintech fraud detection solutions across its platform.
- The platform faced threats including multi-account abuse, promo exploitation, account takeovers, and money laundering, often driven by emulators, app cloners, and malicious tools.
- By leveraging device intelligence for fintech, WINK was able to identify fraudulent devices, link hidden accounts, and enable effective multi-account fraud prevention in real time.
- This approach helped prevent account takeovers caused by accessibility exploits while maintaining a seamless customer experience.
- As a result, WINK achieved a 99.9% genuine user rate and strengthened long-term trust in its digital ecosystem.
👉 Read the full case study to see how device intelligence helps fintechs detect and stop fraud at scale. - https://shield.com/case-studies/wink
SHIELD: The Best Device Intelligence Solution for Fintech
SHIELD’s Device Intelligence is built on two core layers: device identification and real-time fraud intelligence. Together, they help fintech platforms accurately identify devices, link hidden accounts, and stop multi-account abuse and account takeovers before losses occur.
At the foundation is SHIELD Device ID, the global standard for device identification. It uniquely identifies every physical device accessing a platform with over 99.99% accuracy, remaining persistent even when fraudsters attempt factory resets, fingerprint scrambling, or other evasion techniques. This makes it highly effective at eliminating fake accounts and device farms at the source. Complementing this is SHIELD Fraud Intelligence, which continuously profiles device sessions and delivers real-time, actionable risk signals. With over 20 configurable risk indicators, it detects malicious tools and techniques and pinpoints the exact moment a device transitions from low risk to high risk.
Today, SHIELD’s device intelligence is trusted by leading digital platforms globally, including Alibaba, inDrive, and fintech leaders such as TrueMoney, WINK, and MayaBank.
Frequently Asked Questions:
How does device intelligence reduce operational costs for fintech fraud teams?
Device intelligence assigns a risk score to every device, allowing fintech teams to automatically identify high-risk activity early in the user journey. This reduces reliance on manual reviews and rule-heavy investigations, which are costly and time-consuming. By automating fraud decisions and focusing resources only on genuinely risky devices, fintech platforms can operate leaner fraud teams, minimize false positives, and stay ahead of fraudsters without increasing operational overhead.
How quickly can a fintech deploy device intelligence across web and mobile platforms?
A device intelligence solution (the base version) can typically be integrated across web and mobile applications in under 24 hours. Once deployed, it begins generating actionable insights on risky and fraudulent devices almost immediately. Integration timelines may vary slightly depending on the level of customization required, but most fintech platforms can start benefiting from device-level fraud detection within a very short setup window.
What types of multi-account abuse does device intelligence detect (promo fraud, loan fraud, referral abuse, synthetic identity)?
Device intelligence helps fintech platforms strengthen multi-account fraud prevention by detecting:
- Promo and referral abuse driven by multiple fake accounts
- Account takeovers originating from compromised or risky devices
- Money laundering across networks of connected accounts
- Synthetic identities and coordinated account creation
- Fake accounts created to exploit fintech incentives and offers
- Loan application fraud using repeated or linked devices
By linking activity at the device level, fintech platforms can detect fake accounts and coordinated abuse patterns that traditional controls often miss.
Conclusion:
Fraud on fintech platforms is fundamentally a device-level problem, and addressing it at scale requires device intelligence. By gaining visibility into devices rather than isolated identities, fintechs can stop multi-account abuse early, reduce operational strain, and protect genuine users.
If you’d like to explore how device intelligence for fintech can be integrated into your platform and support your fraud prevention strategy, schedule a conversation with our fraud prevention experts.
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