How to Harness the Power of Fraud Decisioning and Orchestration

Current Trends in Fraud Prevention

As organizations continue to evolve their counter-fraud strategies, it's projected that the global fraud detection and prevention market will grow from $63.90 billion in 2025 to $246.16 billion by 2032, exhibiting a CAGR of 21.2% during the forecast period^2. This number continues growing because fraudster tactics and tools continue to evolve and become more sophisticated, requiring constant adaptation from fraud teams.

Here at Ping Identity, we recently polled fraud professionals across ecommerce and financial services businesses and discovered that the majority of the polled organizations are using between 4-8 fraud detection solutions.

And, while they were a minority in the poll, some even mentioned using ten or more sources of fraud and risk signals in their drive to improve fraud prevention. It is likely that due to the additive nature of fraud prevention, these numbers will continue to go up.

To effectively counter fraud, businesses must move beyond simply collecting fraud signals—they need to translate these signals into informed, real-time actions. This is where modern risk decisioning comes in.

Risk decisioning involves evaluating multiple data points in real-time to determine the legitimacy of a user’s actions. Rather than relying on rigid rules alone, modern risk decisioning employs machine learning models, behavioral analytics, and contextual awareness to assess risks dynamically. By analyzing a combination of device attributes, geolocation, behavioral biometrics, and historical fraud trends, organizations can make more precise risk assessments and take proactive measures to mitigate fraud.

A risk decision engine is the backbone of modern fraud prevention strategies. It acts as a central hub that ingests various fraud signals, processes them through predefined risk models, and determines the appropriate action in milliseconds.

This engine can be configured to approve, challenge, or block user actions based on risk scores, ensuring seamless user experiences while preventing fraudulent activity. Unlike traditional fraud systems that operate in silos, a well-integrated risk decision engine continuously learns and adapts, making fraud prevention more effective and scalable.

In fraud prevention, timing is everything. A delayed response to fraudulent activity can mean significant financial losses, reputational damage, and compromised user trust. Effective risk management hinges on the ability to make accurate decisions in the moment, ensuring that legitimate users experience minimal friction while fraudulent actors are intercepted instantly.

To achieve this, businesses must leverage AI-powered analytics, automated risk assessments, and dynamic decisioning frameworks that operate in real-time. When fraud teams have access to immediate, high-confidence risk scores, they can enforce appropriate authentication steps—such as step-up verification or transaction holds—precisely when needed.

Fraudsters are constantly adapting, employing sophisticated tactics to bypass detection mechanisms. A risk decision engine enhances fraud detection by providing a holistic view of user activity across different channels, eliminating blind spots that fraudsters could exploit.

By aggregating diverse risk signals—such as login behavior, transaction anomalies, and device fingerprinting—a risk decision engine can identify suspicious activity more effectively than standalone fraud detection tools. Moreover, it enables adaptive authentication, ensuring that fraud mitigation efforts do not disrupt legitimate customer journeys.

Simplifying Fraud Prevention With Decisioning and Orchestration

In order for fraud teams to adapt successfully, most companies have adopted new counter-fraud technologies to enhance–not replace–previous ones. These detection solutions come into play at various points throughout a user’s session, looking at everything from network attributes to user behavior in hopes of spotting and stopping fraudsters early. Better detection leads to improved prevention, or so the thinking goes.

However, while this additive approach can solve for new attack types and vectors, it creates its own set of problems, making fraud prevention more difficult to manage due to the increased complexity that comes with integrating new tools on top of existing systems. Notably, more than 40% of merchant organizations still rely on manual fraud prevention processes³, which can be inefficient and error-prone.

Yet, collecting these signals is only the first step in stopping fraud. To go from detection to prevention, fraud teams need to implement real-time fraud mitigation, which requires some form of automated decision making and orchestration. As the number of fraud signals grows, centralized fraud decisioning is critical to ensure all this context translates to effective mitigation.

An organization’s ability to make precise, data-driven decisions can mean the difference between stopping fraud and allowing it to persist. Organizations lose an average of 5% of their revenue to fraud each year, with median losses increasing by 24% compared to previous years⁴. Poor decision-making—whether due to outdated fraud rules, incomplete data, or lack of automation—can lead to false positives (blocking legitimate customers) or false negatives (allowing fraud through).

Accurate decision-making relies on a combination of AI-driven risk models, behavioral analytics, and real-time orchestration. By continuously refining risk decisioning processes, businesses can minimize friction for trusted users while taking decisive action against fraudsters.

Historically, many fraud teams have left authorization logic embedded in multiple disparate tools, which makes updating fraud policies and flows cumbersome and slow. Other organizations have developed and built decisioning tools internally based on their specific requirements, but these homegrown tools are often difficult to keep up to date as new fraud detection methods come on board.

The decisioning phase becomes more complex as organizations must scan for fraud throughout the user journey and may choose to initiate mitigation at different points throughout the session–for example, not only at the point of transaction but also when viewing saved personal information, changing profile information, and changing user settings.

The good news is that a centralized decisioning tool can help fraud teams to set up automated, effective fraud mitigation. Within this tool, fraud teams can define the logic that determines the risk levels that will trigger mitigation, and the types of mitigation measures that are appropriate for different types of situations. Implementing these policies no longer requires development resources from the owners of various applications and allows fraud teams to easily control their technology stack.

The Role of Identity Orchestration in Fraud Prevention

While fraud teams often operated separately from identity teams in the past, this is beginning to change. With the advent of new technologies in online fraud detection, identity proofing, and access management, these groups can now work together to the benefit of the broader organization.

Identity orchestration promises to make designing, deploying, testing, and modifying user journeys easier. Orchestration brings together a variety of tools from different vendors and allows organizations to create user experiences that feel seamless and easy from a customer perspective even when many systems are at work behind the scenes.

Well-orchestrated customer journeys allow for many branching paths, with users sent down the appropriate one based on their circumstances and characteristics. This includes paths reserved for suspicious users that can help greatly reduce fraud. Adding or removing fraud signals becomes simpler as well, which is extremely helpful given the additive nature of fraud prevention discussed above.

As the focus of fraud prevention shifts from protecting the transaction to protecting the end-to-end user journey, integrating identity and fraud tools from different vendors into seamless and secure user flows can help both fraud and identity teams meet their metrics.

Ping’s Approach to Fraud Prevention

Ping Identity’s fraud prevention solution addresses fraud detection, decisioning, mitigation, and orchestration in one integrated platform. Our fraud decisioning and orchestration tools allow organizations to easily aggregate fraud signals from a variety of sources, including Ping’s own detection tools as well as external ones, and build out policies that allow for flexible mitigation at any point throughout the user journey.

Ping offers a variety of tools that can help address mitigation use cases, such as multi-factor authentication and identity verification, and can also integrate with third-party tools and vendors. Our orchestration capability offers a low-code approach to designing, implementing, testing, and modifying user journeys to ensure organizations can deliver exceptional customer experiences without sacrificing security. We aim to offer our customers a comprehensive fraud prevention solution that can be the cornerstone of a dynamic counter-fraud strategy.

^1. Fraud Detection and Prevention Market Growth Report, Fortune Business Insights

^2. Fraud Detection and Prevention Market Growth Report, Fortune Business Insights

^3. The True Cost of Fraud^TM Study, LexisNexis

^4. ACFE Report to the Nations, ACFE