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March 12, 2024 - Greg Oprendek

How to Use Data Orchestration to Fight Real-Time Fraud

The battle against fraud, in today’s digital-first age, happens in real time. Moment to moment, fraudsters are leveraging bot armies, sophisticated phishing schemes, and a host of other coordinated attacks to break through financial institutions’ (FI) defenses. Thanks to instant payment services like Zelle, RTP® and FedNow, customers enjoy real-time transactions as a standard when banking. As the financial industry has moved to a real-time-first focus, so too has financial fraud prevention to keep those customers safe.

Fraud teams stay ahead of these real-time attacks using artificial intelligence (AI). Specifically, data-science-focused approaches have proven to be game changers. At the heart of this transformative tactic is data orchestration, acting as the backbone of machine learning (ML) fraud prevention.

While AI has proven to be the key to defeating real-time fraud, unorganized and siloed data can lead to missed fraud signals and fraud that sneaks through the wall. That’s why fraud teams need to know how well they’re leveraging their data to fight real-time payment fraud. Inefficient analysis, limited adaptability, high false positives—all can be remedied through data orchestration.

How data orchestration powers fraud prevention

Data orchestration solutions integrate, coordinate, and manage data from many different sources into one place. This orchestration ensures AI algorithms have access to high-quality, up-to-date data to make informed real-time decisions.

Unifying these fragmented data landscapes gives organizations a comprehensive view of potential threats across the customer lifecycle. Legacy linear approaches analyze data one point at a time sequentially. This approach “misses the forest for the trees” because it is unable to view data from multiple sources at once to reveal larger connected fraud patterns.

A holistic view through strong data orchestration, on the other hand, enhances detection capabilities by including crucial information at every relevant touchpoint. Using this connected data, FIs can respond to threats as they happen and prevent them, keeping good customers protected. As technology evolves and fraudsters’ methods advance with it, data orchestration only becomes more critical in fraud prevention.

Scaling AI fraud prevention while preserving accuracy

No matter the type of FI, all have the goal to grow and add customers. As more customers come aboard and service offerings grow, so too does the volume of fraud attacks. Scaling an AI fraud solution to face these increased threats requires efficient data management and utilization. Every important touchpoint throughout the customer journey, from application and signup through onboarding and into regular customer activity, needs to be carefully scrutinized. This of course creates an ever-expanding set of data sources and volumes of customer data.

Through data orchestration, fraud teams can create and utilize robust fraud models capable of accurately identifying fraudulent activities. Not only that, with data orchestration AI models can access this data as it is collected. That immediate recognition and response creates a proactive defense against dynamic fraud attacks.

Feature engineering and the role of custom features

Feature engineering plays a pivotal role in enhancing the efficacy of fraud prevention models. Through selecting and transforming relevant data attributes, feature engineering refines the input data, making it more conducive for ML algorithms to identify patterns indicative of fraudulent behavior. Custom features, tailored to the unique characteristics of an organization’s data, further bolster the model’s ability to detect subtle anomalies that generic models might overlook.

Real-time fraud prevention through data orchestration

When it comes to responding to fraud attacks as they happen, data orchestration is the key component ensuring swift and accurate responses. Centralizing data is foundational, enabling the construction of a thorough understanding of customer activities by leveraging all available data. This comprehensive viewpoint is vital, but achieving real-time detection hinges on the capabilities of data orchestration to process and transform this data instantly.

However, centralizing and orchestrating data in real time is just part of the equation. The effectiveness of this data in fraud prevention relies on advanced feature engineering. This process is essential for transforming vast datasets into intelligible risk indicators by identifying and calculating features that signal potential fraud. Crucially, these features must be computed in real time, ensuring that rules and models can utilize them immediately to spot and respond to fraud as it happens. Thus, it’s the combination of real-time data orchestration and potent feature engineering that empowers financial institutions to achieve precise, reliable fraud detection at the moment it matters most.

Being able to create and tune features and rules faster and more efficiently is a significant advantage as well. Through Generative AI-powered tools like AI Co-Pilot, fraud teams gain the upper hand against evolving fraud patterns. This advantage comes from the ability to rapidly develop robust features independently of IT support and to quickly optimize their rules with intelligent, AI-generated suggestions. This streamlined process ensures that fraud detection efforts are always a step ahead, effectively countering the tactics of fraudsters.

Adding new data sources and third-party connectors is another area where data orchestration shines. When compared with batch processing, real-time data orchestration can prevent 45% more losses. FIs who utilize DataVisor can easily add more data points and calculate them in real time as well, something that many legacy solutions can’t do.

In the battle against real-time fraud, data orchestration emerges as a fundamental tool, empowering organizations to scale AI and ML for accurate and proactive results. The combination of feature engineering and custom features further refines the fraud prevention models, ensuring that even the most subtle anomalies are detected.

To dive deeper into the intricacies of data orchestration for real-time fraud prevention, check out our full data orchestration product sheet, or book a customized demo with our expert team.

about Greg Oprendek
Greg is a passionate digital marketer, avid basketball fan, aspiring fraud expert, and Content Marketing Manager at DataVisor.
about Greg Oprendek
Greg is a passionate digital marketer, avid basketball fan, aspiring fraud expert, and Content Marketing Manager at DataVisor.