Digital-first sales in 2024 were nearly double those in 2019. This rapid shift towards online interactions continues to fundamentally change how companies remain competitive, maintain consumer loyalty, and accelerate revenue growth.
Fraud and risk teams are at the forefront of this shift, playing a critical role in determining which user interactions to trust, when to trust them, and how quickly to allow them. In many ways, they act as both the accelerator and the brake for a company’s digital transformation—balancing risk and revenue in every decision.
Navigating a Rising Tide
As companies face pressure to move to digital, it’s becoming easier, faster, and more lucrative to be a fraudster than ever before.
In recent years, there has been a sharp rise in social engineering and phishing attacks—alongside sophisticated tactics designed to bypass traditional defenses, such as synthetic identities and manipulated browser environments.
Compounding the challenge is the growing democratization of fraud: social media platforms now serve as hubs for fraud tutorials, while the Fraud Economy thrives on the sale of compromised accounts—companies and their risk teams are navigating relentless and rapidly evolving threats.
Sift’s Q1 2025 Digital Trust Index report found that nearly 65% of consumers knew of, or have themselves, committed some type of payment fraud.
An Eroding Front-Line Defense
Until now, Identity Verifications (IDVs) have been relied on as the front-line defense in determining the trust of an online identity. Yet, this costly and often cumbersome approach is becoming less effective, especially as global fraud rings are learning to collaborate and adapt and consumers demand increased convenience.
In this environment, using identity verification alone isn’t enough to understand which users to trust—businesses need more comprehensive payment fraud detection systems to confidently and precisely distinguish between fraudsters and trusted customers.
Beyond Standard Identity Verification Strategies
While identity verification provides an important line of defense by validating government IDs and biometrics, it represents just one piece of the identity trust puzzle. It answers the question of whether a user can match a government document—what it doesn’t provide is insight on whether a user is going to be a risk to your business. To answer that, you need a deeper understanding of identity.
- To trust, you need insight on intent.
- Intent is based on context.
- Context is based on insight across different dimensions of data.
What is Identity Intelligence?
Identity intelligence is the real-time monitoring and analysis of credentials and identity-related threats to prevent their use by unauthorized users. By understanding a user through many identity analytics and information points, organizations can assess the risk associated with any individual user. It’s a critical aspect of the modern e-commerce economy and one that largely operates behind the scenes for the average consumer.
Fortified Defenses with Cross-Dimensional Decisioning
To confidently assess user intent, fraud and risk teams need a three-pronged approach: combining traditional identity analytics and verification with deep identity intelligence both on your platform and beyond. Below is a breakdown of those components:
Identity Intelligence on Your Platform
Verifying a user against a government ID may confirm they are who they claim to be—but it says nothing about their intentions. The real risk lies in what the user does after they gain access to your platform. To uncover that, you need deep user insights spanning the entire customer journey. This includes understanding:
- Behavior Breaching Market Norms: Identify behaviors that deviate from those of other users with deep insights at every stage of the digital journey.
- Behavior Breaking User Patterns: Fortify account protection at and immediately after log-in by quickly detecting subtle behavior changes between login sessions.
But that insight only reveals half of the story.
Global Identity Trust Beyond Your Platform
A critical part of uncovering user intent is looking beyond your own platform. Global identity intelligence and identity analytics help fill in those gaps—offering insight into how a user behaves across the broader digital ecosystem. It can answer questions like:
- How long have this user’s digital attributes—devices, IPs, email addresses—been in use?
- Are those signals tied to a history of risky behavior or a track record of trust?
- In which industries, regions, and timeframes have these behaviors surfaced?
This cross-dimensional approach provides critical context about your users, giving an unprecedented window into a user’s intent by revealing a more holistic picture of their identity story.
Making Identity Intelligence Actionable
Getting the details of a user is only half of the battle when it comes to identity trust and identity theft prevention. Raw identity data only becomes valuable when transformed into actionable intelligence, which is available with modern platforms like Sift. Sift helps gather analytics to help you understand your users, but also provide insights that would take weeks to process without advanced machine learning tools. Pairing these details with context is the best way to recognize threats while continuing to provide trusted users with the low-friction experience they expect.
Advanced Analytics Through Machine Learning and Predictive Models
Advanced analytics powered by machine learning and predictive models are capable of detecting subtle patterns and correlations in users that human analysts might overlook, providing deeper insight into behavior. Detecting these irregularities is an effective approach when trying to sift out bot-like behavior, implement account takeover protection or actions associated with brute-force attacks or card testing.
Risk scoring algorithms evaluate the trustworthiness of each interaction, while anomaly detection systems flag any behavior that deviates from an individual’s normal activity. Network visualization tools support trust analysis by uncovering hidden connections between accounts, helping analysts detect coordinated, fraudulent behavior or identity manipulation.
Predictive analytics enables systems to anticipate and prepare for potential fraud attempts, while clustering algorithms group similar behavior patterns, making it easier to isolate large-scale or common fraud tactics. Feature importance analysis helps organizations understand which variables influence risk scores the most, providing transparency and guiding fraud prevention decision-making.
These technologies work in concert to deliver a smarter, more proactive approach to identity and trust management.
Contextual Decision-Making Enables Secure, Streamlined Experiences
Instead of applying the same authentication steps to every interaction, contextual and risk-based authentication evaluates each situation and adjusts accordingly, tightening security when threat levels are high and minimizing friction when the risk is low. Dynamic friction offers additional verification only when necessary, making sure that legitimate users aren’t slowed down. Real-time policy enforcement helps block high-risk actions as they occur, and automated workflow triggers automatically document unusual cases for manual review.
Organizations can use a custom rules engine to define risk scenarios specific to their operations. This flexibility ensures that security protocols and account takeover prevention align with unique organizational needs without compromising user experience. Multi-factor authentication can be applied intelligently, and step-up authentication activates when a system detects elevated risk.
Combining contextual decision making with behavior analytics is the modern approach to identity trust management, balancing strong protection with minimal disruption for the user.
Driving Business Growth Through Trust
While identity trust strategies were largely born from a need to authenticate user identities and block fraudsters, identity intelligence can actually spur business growth. The underlying mechanisms that allow an organization to take a modern approach to identity trust management and identity theft protection also streamline and optimize systems, increase customer trust, and reduce friction.
Trusted Users Prefer Reduced Friction
Part of identity trust strategies is recognizing trusted users. A byproduct of knowing which users are legitimate is being able to streamline those customer experiences, which can lead to increased customer confidence, quicker transactions and checkouts, and more:
- Recognized, trusted customers are fast-tracked
- Unnecessary verification steps for low-risk transactions are eliminated
- Security is maintained without compromising convenience
- Customer confidence builds with consistent protection
- Personalizing security measures are offered based on user history
- Authentication for returning customers is streamlined
- Verification steps are based on risk level, streamlining and optimizing the process
Identity Trust Drives New Opportunities
Once an organization has effective identity trust in place, that security can work to stop fraudsters regardless of location. This aspect has the benefit of making expansion into new regions easier, safer, and faster at getting customer trust. Businesses with functional identity trust policies can:
- Safely expanding into new markets and customer segments
- Launch innovative products and services with proper risk controls
- Accelerate customer onboarding while managing exposure
- Support omnichannel experiences with consistent identity controls
- Test new business models with confidence
- Expand into high-risk markets securely
- Offer premium services to trusted customers
Identity Security Measures Optimize Operations
Identity trust measures are automated, meaning that once in place, they can optimize and refine an organization’s operations. Through pattern detection, identifying trusted customers, and automated user reviews, a business can rely less on manual processes and get the information needed to make decisions quickly. Identity trust measures are effective at:
- Reducing manual review queues through better automation
- Decreasing false positives that frustrate legitimate customers
- Improving operational efficiency with data-driven decisions
- Maintaining compliance while scaling the business
- Streamlining fraud investigation workflows
- Reducing operational costs through automation
- Improving team productivity with better tools
What is Identity Trust with Sift?
Payment fraud detection systems are an important aspect in keeping your transactions secure, your customers happy, and your business growing without worry, but the Sift Platform goes beyond basic fraud detection.
Sift Leverages the Network Effect
By processing over one trillion events annually across a global network, Sift continuously evolves alongside changing fraud patterns. This vast data stream allows for early detection of emerging fraud techniques and coordinated attacks, ensuring threats are identified before they spread. Intelligence is shared across the network so businesses can stay ahead of evolving attack patterns, while comprehensive identity profiles provide analyzed behavior across multiple touchpoints. These insights contribute to maintaining an up-to-date view of fraud, equipping organizations with the ability to recognize and respond to threats with greater speed and accuracy.
Delivering Real-Time Protection
Sift’s payment protection solution analyzes hundreds of signals in mere milliseconds, enabling rapid detection of high-risk activities and providing immediate and accurate feedback. It adapts to new threats through continuous learning so protection evolves in real time. The platform maintains consistent performance even during peak load periods and supports high-volume transaction processing without delays. This ensures fast, reliable decision-making and consistent response times, allowing businesses to operate securely and efficiently at scale.
Providing Actionable Insights That Matter
Sift offers detailed case management features that streamline investigations by organizing relevant data in a single place. Advanced analytics support pattern detection, helping security teams uncover trends and suspicious behaviors quickly and proactively. Custom reporting tools provide actionable business intelligence tailored to specific needs, while integration with existing security systems provides a unified workflow. Real-time monitoring dashboards give teams immediate visibility into activity, supported by comprehensive audit trails that track every action for compliance and review. Performance analytics and optimization tools enable ongoing system improvements to help produce threat management that evolves in tandem with ongoing threats.
Real-World Impact: Patreon’s Success Story
Patreon leveraged Sift to keep creators safe and accelerate growth. See the results below.
Looking Forward: The Future of Identity Intelligence
As digital business continues to evolve, identity intelligence must advance as well through a combination of understanding emerging trends and technologies, improving and refining analysis methods, and making payment fraud detection systems easier to use and understand.
The best payment protection solutions are always updating their signal collection to catch unusual online behaviors as they emerge. New data sources and signals are added to behavioral analytic databases to prevent next-gen attack patterns, while advances in device fingerprinting techniques allow organizations to operate at scale and grow. At the same time, emerging authentication methods and new privacy best-practices are integrated as trends change and evolve.
Analysis methods are also improving, aided by advancements in pattern recognition and machine learning. These technologies allow for more refined risk scoring algorithms and provide enhanced visualization and investigation tools. Complex decision-making is being automated through the implementation of the latest AI techniques, which allows for better anomaly detection.
Finally, the future of payment protection solutions will include easier integration and data sharing. Identification signals can be shared across platforms and security tools to provide bastions against even the latest attack vectors. By sharing information, coordinated responses to threats are becoming increasingly possible allowing for comprehensive defense strategies. Standardizing these data exchange formats further secures organizations from online threats and suggests that industry partnerships will only grow.
Taking Action
Businesses and organizations can and should strengthen their identity intelligence capabilities today. Here are seven steps to making the most out of your identity trust strategies.
- Assess Current Identity Trust Vulnerabilities
The first step in strengthening identity trust is assessing any current gaps or weak points. Take a close look at your existing systems to identify weaknesses that fraudsters can exploit or where legitimate users face unnecessary difficulty. Understanding these weaknesses provides a foundation for your identity trust improvements.
- Map Customer Journeys and Identify Friction Points
Mapping customer journeys is a good way to pinpoint areas where users experience friction or where potential risks lay. By analyzing each step from account creation to transaction, organizations can identify where verification processes may be too intrusive, too lenient, or ineffective. This approach ensures both security and user satisfaction.
- Evaluate Additional Identity Signal Sources
It’s important to evaluate additional signal sources and data providers, incorporating diverse signals like device behavior, geolocation, and biometric data. This approach helps create a more accurate picture of user identity, and by partnering with reliable data providers you can improve the quality and reliability of identity checks.
- Integrate Comprehensive Identity Intelligence Platforms
Implementing comprehensive identity intelligence platforms allows organizations to unify and streamline their approach. These platforms centralize identity data, analytics, and decision-making tools, making it easier to detect anomalies, enforce policies, and respond to threats in real time. They also support scalability and integration across systems.
- Continuously Measure and Optimize Performance
Continuous measurement and optimization let you stay ahead of evolving threats by regularly analyzing performance metrics such as false positive rates, verification time, and fraud incidents. Organizations can fine-tune their identity systems to maximize both security and efficiency.
- Train Teams on New and Emerging Techniques
As identity tools and techniques evolve, your teams need to understand how to use new platforms effectively, interpret risk signals, and respond appropriately to different scenarios. Ongoing education ensures that best practices are observed and your policies stay aligned with the latest technologies and advancements.
- Develop Response Procedures for Risk Scenarios
When implementing identity theft protection, account takeover prevention, and other risk protection protocols, developing clear response procedures for different risks ensures consistency and preparedness. Account takeover attempts, suspicious login patterns, and onboarding anomalies can all be mitigated with predefined workflows to help teams act swiftly and accurately to strengthen account takeover protection. This structured approach reduces confusion, minimizes risk, and supports user trust.
The Necessity of Identity Intelligence in the AI Age
With proper identity intelligence, businesses can confidently grow while protecting themselves and their customers from increasingly sophisticated threats. The key is finding the right balance between security and user experience, leveraging advanced technology while maintaining operational efficiency.
Success in the digital economy requires more than just stopping fraud—it demands a comprehensive approach to identity intelligence that enables growth while managing risk. By implementing the right tools and strategies, businesses can build trust, reduce fraud, and create better customer experiences.
To learn more about fraud decisioning and identity intelligence in the AI age, explore Identity Trust XD.
Identity Intelligence FAQs
Identity intelligence refers to the real-time analysis of digital behavior, device data, and user context to determine if an online identity is trustworthy. It’s essential because traditional identity verification methods are no longer enough to stop increasingly sophisticated fraud tactics. Identity intelligence helps businesses detect threats proactively while maintaining a seamless customer experience.
Traditional identity verification checks if a user can match government-issued credentials. Identity intelligence goes further by assessing the user’s intent using behavioral data, device fingerprinting, and global identity patterns. This deeper analysis allows companies to detect fraud, prevent account takeovers, and reduce false positives more effectively.
The most pressing fraud threats include social engineering, phishing, synthetic identities, and manipulated browser environments. Additionally, the rise of the dark web-driven Fraud Economy has made it easier for bad actors to buy and sell compromised accounts, putting pressure on businesses to adopt more advanced identity trust strategies.
Sift uses AI-powered machine learning, behavioral analytics, and identity intelligence to analyze trillions of signals in real time. Its platform enables businesses to detect anomalies, assess risk, and apply dynamic friction only when needed—improving both security and user experience. Sift also shares intelligence across its network to stay ahead of emerging threats.
By identifying and fast-tracking trusted users, businesses can reduce friction in the customer journey, leading to faster checkouts, higher customer satisfaction, and increased loyalty. Identity trust also supports secure expansion into new markets and enables the launch of innovative services with lower risk.
