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Financial crime is evolving fast. We believe the Gartner Hype Cycle for Fraud and Financial Crime Prevention, 2025, highlights increases in sophisticated threats, ranging from deepfakes and synthetic identities to AI-powered phishing.
As traditional fraud prevention controls lose ground to AI-driven attacks and other tactics, banking leaders are facing mounting pressure to bolster fraud defenses, navigate an ever-expanding web of regulations, and still offer customers a seamless experience. Meeting these demands calls for a new generation of tools designed to outpace both fraudsters and shifting compliance standards. In this blog post, we’ll cover some of our takeaways from the Gartner report.
Why financial crime is escalating
In our opinion, the latest Gartner analysis paints a clear picture: Fraudsters are increasingly using AI tools to automate attacks, craft convincing synthetic identities, and evade legacy security measures.
At the same time, customer expectations and digital activity continue to rise. People open accounts online, move money from their phones, and expect fast decisions with minimal friction. Online banking has become the default, raising expectations for fast, frictionless service while also increasing the compliance burden.
Combined, this has all created an environment where account takeover, cyberattacks, and data breaches are now top concerns for the banking industry — an environment where banks need to surface and stop new attack vectors as quickly as they appear, all without driving customers away.
Key findings from the Gartner Hype Cycle
To us, the Gartner Hype Cycle for Fraud and Financial Crime Prevention, 2025, underscores several trends shaping the future of risk management in the banking industry:
- The AI arms race: Both fraudsters and defenders use AI and machine learning (ML). Fraudsters employ generative AI to create fake identities and automate attacks, while banks rely on advanced ML models — including self-supervised and supervised learning — to detect anomalies, retrain systems, and use synthetic data for safer model development.
- Biometrics and behavioral analytics: Static identity checks have become insufficient. Banks are increasingly adopting physiological and behavioral biometrics, analyzing gestures, keystrokes, and device usage patterns to spot fraud and support continuous authentication throughout a session.
- Growth in new account fraud and account takeover: Automated attacks and synthetic identities are fueling a rise in account takeover and new account fraud attempts. Pre-emptive, real-time detection systems are now essential for identifying suspicious behavior before fraud occurs.
- Stricter data privacy requirements: With a majority of the global population now covered by modern privacy regulations, banks must ensure robust data governance and privacy management, providing transparency and control over personal data.
- Automation that preserves customer experience: Hyperautomation and low-code tools are streamlining know your customer (KYC), anti-money-laundering (AML), and fraud response processes. The aim is to reduce manual effort and customer friction, enabling faster and more accurate onboarding and transaction approvals.
These themes point to a future where adaptive, intelligent, and privacy-conscious solutions are necessary to stay ahead of both fraudsters and regulatory demands.
Device intelligence: The backbone of adaptive, privacy-compliant fraud prevention
Device intelligence has become an essential component of next-generation fraud prevention. At Fingerprint, we analyze more than 100 device, network, and behavioral signals each time a visitor interacts with a website or application. This allows us to generate a persistent visitor ID — a unique identifier that remains stable even when users clear cookies, switch networks, or browse in incognito mode.
This matters because fraudsters frequently use tactics like device spoofing, virtual private networks (VPNs), or emulators to conceal their identity. Device intelligence reveals these behaviors in real time, giving risk and fraud teams a detailed context for every interaction. Our platform’s 20+ Smart Signals — including Bot Detection, VPN Detection, and Browser Tampering Detection — surface high-risk attributes instantly, so teams can respond proactively.
Persistent identification is crucial for staying ahead of AI-powered fraud. The visibility that Fingerprint provides helps flag suspicious devices or anomalies that may signal an attack in progress.
Training ML models with real-world device data
Modern fraud prevention relies on ML models that adapt quickly to new threats. Device intelligence plays a central role by providing a continuous stream of high-quality, real-world data. Each device interaction — whether a login, transaction, or account creation — delivers valuable signals for self-supervised, supervised, and unsupervised ML models.
This steady flow of data enables fraud and risk teams to retrain models frequently, identify emerging attack patterns, and avoid the stale decision rules that can leave organizations exposed. The result is a fraud prevention engine that is always learning and evolving, improving detection rates, minimizing false positives, and staying responsive to the fast-changing tactics of fraudsters.
Reducing false positives to keep customers happy
Finding the right balance between security and user experience remains one of the biggest challenges in fraud prevention in the banking industry. Overly strict controls can create false positives, blocking or delaying legitimate customers and causing frustration or customer churn.
Device intelligence helps address this challenge by enabling more nuanced risk assessments. By combining device signals — such as location, device type, and network attributes — with behavioral analytics that can, for example, surface possible bot activity, Fingerprint can distinguish between legitimate users and suspicious activity. For example, if a familiar device suddenly shows signs of tampering or connects from a high-risk location, that context can prompt additional verification only when truly necessary.
This targeted approach reduces unnecessary step-up authentication and manual reviews, allowing genuine customers to enjoy smooth onboarding and transactions while stopping fraudsters before they can cause harm.
Bolstering KYC, AML, and account takeover prevention
Device intelligence is a critical part of compliance and fraud prevention workflows, from onboarding to ongoing monitoring. Fingerprint can provide additional signals to help strengthen Know Your Customer (KYC) and Anti-money Laundering (AML) processes, such as detecting device anomalies (e.g., use of emulators, rooted or jailbroken devices, and mismatched geographies) that may indicate synthetic identity fraud or money mule activity.
Additionally, the Velocity Signals Smart Signal, which highlights unusually high numbers of IP addresses or countries associated with a single device, helps uncover patterns of abuse or organized fraud rings. When a login or transaction comes from an unfamiliar device or shows signs of tampering, it can trigger step-up authentication or additional review, providing a robust defense against account takeover.
Integrating device intelligence into these workflows allows organizations to make risk-based decisions in real time, reducing manual effort while ensuring compliance with regulatory requirements.
Building a future-ready fraud prevention strategy
With AI tools becoming increasingly more sophisticated, financial crimes are only set to escalate. Rather than taking a static approach to fraud prevention, banks need to have an adaptable, future-ready fraud prevention strategy in place to successfully fight fraud.
Read more in the Gartner Hype Cycle for Fraud and Financial Crime Prevention, 2025.
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GartnerⓇ 2025 Hype Cycle™ for Fraud & Financial Crime Prevention; Vatsal Sharma, Pete Redshaw; 21 July 2025
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