Over the past decade, fintech has reshaped financial services with faster payments, seamless digital lending, and new customer experiences. But growth has also widened the attack surface.

According to Alloy’s 2024 State of Fraud Benchmark Report, more than 50% of banks, fintechs, and credit unions saw business fraud rise, while nearly two-thirds reported growth in consumer fraud.

Fraudsters are scaling their efforts through automation, stolen credentials, and synthetic identities. Deloitte’s Center for Financial Services warns that advances in generative AI could drive banking-sector losses of around $40 billion by 2027. Meanwhile, regulators and customers expect stronger safeguards for personal data and higher standards of privacy.

For fintech leaders, the core challenge is to design risk systems that can withstand these pressures while remaining privacy-conscious. Device intelligence is emerging as one of the most effective ways to achieve that balance.

Why Device Intelligence Is Essential in Fintech

Traditional risk frameworks relied heavily on static inputs – personal documents, credit bureau data, or payment history. These signals remain useful, but they have two limitations. First, they can be compromised through large-scale breaches or identity theft. Second, dependence on personal information raises compliance concerns as privacy regulation grows more stringent worldwide.

Device intelligence addresses these gaps. By analyzing technical and behavioral signals generated during a session, fintech companies can assess risk in real time without processing sensitive identifiers. With JuicyScore device intelligence, for instance, institutions gain access to more than 220 non-personal parameters – including indicators of virtualization, anomalies in browser integrity, and traces of remote access. This additional layer strengthens fraud prevention and allows more confident decision-making.

Precision Without Customer Friction

A frequent concern for fintech leaders is whether stronger fraud controls will increase friction for legitimate users. Traditional approaches, such as manual reviews or multi-step authentication, often slow down the customer journey. Device intelligence avoids that problem by operating invisibly in the background.

The emphasis is on precision. Poorly tuned systems risk rejecting good customers or generating unnecessary friction. Advanced device intelligence solutions focus on non-personal device data, enabling models to deliver highly accurate assessments while remaining unobtrusive. In practice, this means blocking complex schemes without disrupting legitimate customers and financial services– a balance that makes device intelligence a practical choice for fintech organizations.

Privacy and Regulatory Alignment

Privacy has become more than a compliance obligation – it is now a competitive differentiator in fintech. Customers expect their data to be safeguarded, while regulators are introducing stricter frameworks across regions. Device intelligence aligns with this shift because it does not rely on identifiers such as names, addresses, or government IDs.

Instead, it evaluates the integrity of each session: whether the device is genuine or emulated, whether automation tools are present, and whether behavioral patterns reflect human use or artificial activity. This approach strengthens fraud controls while reducing the exposure of sensitive data. 

Beyond Transactions: A Broader View of Risk

Fraud prevention in fintech often centers on the transaction stage, where payments and transfers are verified. Yet by this point, companies have already invested in onboarding, verification, and system resources. Stopping fraud late still carries a cost.

Device intelligence allows earlier intervention. It can flag suspicious sessions at the account creation stage, during login attempts, or at the first sign of unusual activity. For lenders, this means protecting origination pipelines. For payment providers, it means detecting bots or unauthorized access attempts before funds move. In both cases, the outcome is stronger portfolio quality and reduced operational losses.

Strategic Value for Fintech Decision-Makers

The business case for device intelligence extends beyond reducing fraud. By moving away from static personal data and adopting scalable device-based scoring, fintech institutions gain strategic flexibility. They can accelerate market expansion, reduce dependence on external data providers, and streamline onboarding by minimising friction.

For executives, this is more than a defensive capability – it is an enabler of sustainable growth. In highly competitive markets, the ability to strike a balance between privacy and precision supports both customer trust and long-term performance. 

Conclusion: Building Trust in the Digital Finance Era

Fintech is built on trust – between lenders and borrowers, payment providers and merchants, platforms and customers. That trust can be eroded quickly by incidents of fraud or privacy missteps. Device intelligence strengthens both sides of the equation.

By offering precise, non-intrusive fraud detection that aligns with modern privacy standards, it allows fintech organizations to secure their operations without undermining customer experience. For decision-makers, the path forward is not a choice between security and growth. With device intelligence, both objectives can be pursued together, enabling resilient, compliant, and future-ready financial ecosystems.