Device intelligence is becoming a foundational capability for digital banks, fintech lenders, and neobanks navigating increasingly complex risk environments. With online fraud schemes growing in complexity and volume, traditional rule-based systems are struggling to keep up.
According to Alloy's 2024 Financial Fraud Statistics, more than 50% of surveyed banks, fintechs, and credit unions report an increase in business fraud and over ⅔ report an increase in consumer fraud. The same report indicates that by 2027, advances to Gen AI will cost banks an estimated $40 billion. Over half of financial institutions report increasing investment in third-party fraud prevention, with 3 out of 4 choosing to invest in an identity risk solution.
Device intelligence enables more precise decisions at every stage of the customer journey – from onboarding to authentication to ongoing monitoring.
Drawing on years of applied knowledge, JuicyScore brings a clear vantage point to this topic. Our AI-powered DeviceID system analyzes over 65,000 parameters – including network connection, device characteristics, installed software, and user behavior – to produce high-confidence risk scores and support smarter, probabilistic decision-making.
In this article, we’ll explore how device intelligence is transforming fraud prevention, credit scoring, and onboarding for digital financial services. From detecting synthetic identities and emulated devices to analyzing behavioral anomalies and ensuring regulatory compliance, device intelligence offers a privacy-conscious, real-time layer of insight essential for navigating today’s complex digital risk landscape.
What Is Device Intelligence?
Device intelligence refers to the collection and analysis of data points related to a user’s device and behavior during online sessions. This includes operating system versions, browser fingerprints, emulator detection, remote access tool usage, and behavioral anomalies. For example, consistent typing speed or suspicious session replay patterns may indicate scripted fraud attempts.
Device intelligence works best when it leverages anonymized, privacy-respecting data – enabling fraud prevention that aligns with global regulatory standards.
Device Intelligence vs. Device Fingerprinting
While often used interchangeably, these two concepts slightly differ. Device fingerprinting focuses on static hardware and software characteristics – like screen size, browser version, or installed fonts. Device intelligence goes deeper.
It incorporates:
- Behavioral analytics during sessions
- Real-time response patterns
- Device manipulation techniques
- Remote access tool detection (RATs)
- Signs of jailbroken or emulated environments
This added depth allows for more accurate fraud detection by shifting the focus away from personally identifiable information (PII) and toward behavioral and contextual signals. As highlighted in JuicyScore’s article on true device analysis, this shift is critical because static fingerprints alone often miss repeat fraud attempts made through slightly modified or masked devices.
Why Device Intelligence Matters in Digital Banking and Lending
Device intelligence helps stop threats that static systems often miss in digital banking. For instance, synthetic identity fraud – where attackers blend real and fake information – can slip past standard checks. But if that user logs in from a manipulated or suspicious device, device intelligence picks it up immediately.
As demonstrated in our blog post on optimizing custom anti-fraud models, integrating dynamic device and behavioral signals significantly improves the accuracy of risk models – especially in markets where traditional financial data is limited or unreliable.
This approach is particularly impactful when combined with machine learning techniques that adapt to new patterns of fraud. We go into this in depth in our article on Deep Machine Learning: On the Path to the Truth. Combining multi-layer data points – from behavioral biometrics to environmental signals – makes it possible to build AI models that go beyond black-and-white judgments. Instead, these systems reveal nuanced, probabilistic insights about user behavior, context, and intent.
Use Cases: Where Device Intelligence Delivers Value
1. Account Takeover Prevention
Real-time fraud detection with device intelligence makes it easier to spot abnormal login behavior or device swaps that indicate an account is under threat. Especially when paired with remote access tool detection and session replay pattern recognition, it can preemptively shut down suspicious access.
2. Secure Credit Scoring Alternatives
When traditional credit histories are missing or incomplete, device intelligence in alternative credit scoring adds another layer to evaluate intent and trustworthiness. Devices with consistent, low-risk behaviors over time can serve as trustworthy proxies for thin-file or new-to-credit users.
3. Filtering Out Emulators and Bots
Fraud rings often test systems at scale using virtual environments. Device intelligence that detects emulator usage, remote access, and other spoofing techniques helps neutralize these attempts before they reach application or payment flows. These signals can be combined into a trust model that dynamically updates based on usage frequency, tampering attempts, and anomalies.
4. Enhancing Customer Onboarding
Smooth onboarding is key – but not at the cost of security. Device intelligence improves customer onboarding by evaluating behavioral signals early, often before login credentials are even submitted. Adaptive scoring methods help distinguish legitimate users from scripted or manipulated flows, reducing false positives and improving user experience.
5. Monitoring Online Transactions
Device intelligence tools can run live assessments during key moments in the user journey – logins, application starts, payment authorizations – reducing exposure without impacting UX. These assessments, guided by privacy-first principles, avoid collecting any personal identifiers but can still provide high-fidelity signals that inform authentication or authorization decisions.
6. Detecting Secondary and Multi-Account Fraud
A rapidly growing challenge is detecting secondary fraud – when fraudsters attempt to re-enter systems using new devices or slightly altered configurations. As we’ve outlined in the update on JuicyID v16, next-generation device intelligence addresses this by identifying probabilistic links between devices, even when direct matches are obfuscated. Such functionality is especially useful in scenarios involving fraud rings or shared devices.
Behavioral Signals: A Deeper Layer of Trust
Beyond technical attributes, behavioral analytics provide a crucial additional layer in identifying fraud. By detecting micro-patterns such as robotic scrolling, identical action timing, or unnaturally flawless sessions, device intelligence exposes signs of automation and manipulation.
In high-risk digital environments where identities can be faked or borrowed, behavioral traits – like hesitations in clicks, typing rhythm, or error frequency – offer authentic signals of user behavior. These subtle indicators help build adaptive trust models that evolve continuously, distinguishing real users from scripted flows with increasing precision.
Balancing Risk Intelligence with User Privacy
Device intelligence solutions must balance insight with user privacy. The most effective systems rely on anonymized technical and behavioral signals – ensuring compliance with data protection frameworks like the GDPR while building user trust.
Today, privacy-first design is more than a regulatory necessity – it’s a strategic differentiator. Financial institutions are increasingly drawn to tools that reduce data exposure while maintaining high levels of predictive accuracy.
India’s Digital Personal Data Protection Act (DPDP) 2023 illustrates the expanding international scope of data protection laws. This comprehensive regulation mandates strict controls over how companies collect, store, and use personal data. It reflects a global movement toward stronger data sovereignty and user rights – and reinforces why privacy-preserving technologies are crucial for any solution aiming to scale internationally.
As Manish Thakwani, Head of Business Development for India & South Asia at JuicyScore, puts it:
We believe that device data and a strong digital profile can play a more important role and give much more value in a decision making process. Moreover, non-personal data will never lead to the problems connected to data breaches – such data is simply of no use to any fraudster, but can improve the decision making model significantly. Digital profile data can strengthen the decision-making model by 5-15% Gini points and impact the approval rate, giving the relative growth starting from 10%.
By designing with privacy at the core, device intelligence systems offer a clear path into regulated markets – helping financial service providers stay ahead of evolving legal requirements while maintaining operational agility.
Ready to Strengthen Your Fraud Strategy?
Looking to strengthen your fraud defenses with zero friction? Book a free JuicyScore demo to see how our privacy-first device intelligence can detect threats before they escalate – and support smarter, faster decisions across your user journey.
Key Takeaways
- Device intelligence combines technical and behavioral data to improve fraud detection.
- It outperforms static device fingerprinting by adapting in real time.
- It’s critical in detecting synthetic identities and manipulated devices.
- Used in digital banking, it prevents account takeovers and enhances onboarding.
- Behavioral signals offer unique insights into user intent.
- Privacy-first device intelligence solutions support compliance without sacrificing performance.
- Device intelligence integrates seamlessly into risk models and evolves through machine learning.
- It supports detection of secondary and multi-account fraud, even in obfuscated or shared environments.
- Modern solutions incorporate real-time scoring, adaptive learning, and behavioral biometrics.
FAQs
What is device intelligence in fraud prevention?
It’s the use of real-time technical and behavioral signals from a user’s device to assess fraud risk without relying on PII.
How does device intelligence detect synthetic identities?
By analyzing device behavior and setup, it can spot anomalies like emulators or multiple accounts tied to a single device.
What’s the difference between device fingerprinting and device intelligence?
Fingerprinting is static and surface-level. Device intelligence adds behavioral analysis, emulation detection, and real-time context.
Can device intelligence be integrated into any risk model?
Yes. For example, JuicyScore’s models are customizable and integrate via API to strengthen existing decision systems. You can read more about it here.
Is device intelligence safe from a privacy standpoint?
Yes – when implemented correctly, it uses only non-personal data and complies with privacy regulations like GDPR.
How does device intelligence improve onboarding?
By evaluating risk signals before login or form submission, it filters fraud without creating friction for real users.
Why should banks, fintechs, and other financial organizations invest in device intelligence?
Because the cost of undetected fraud – especially with synthetic IDs – far outweighs the investment in intelligent prevention tools. For context, JuicyScore’s clients have seen an average ROI of 10X+ after implementing our solutions.