Fraud Management

Last updated:October 23, 2026

Draft reviewReview before publishing (remove this box before publishing)

  • The Journey file I received is identical to the published tutorial, so the five cards were not in it. I used the five integration options from the published page as the cards. Replace them if the real cards differ.
  • Cards 1 to 3 link to the standalone and Fraud API Gateway use cases, now filled from the published integration page. Endpoints and samples in them are placeholders.
  • The published section "Use cases" is renamed "Fraud models" so it no longer clashes with the integration "Use cases".
  • Text edits: merged the two near-identical closing paragraphs, fixed grammar, and added the managed-service sentence from the Configuration Sheet introduction.
  • Please confirm the marketing claims carried over from the published page (for example "patented") are still approved.

Fraud schemes are increasingly AI-driven, and friendly fraud is raising the cost of every chargeback. Our Fraud Management solution gives merchants and Payment Service Providers (PSPs) AI-powered technology that monitors transactions from inception to post-checkout, and returns an instant accept, challenge or deny decision on e-commerce and m-commerce transactions. It is delivered as a managed service by a team of experienced risk analysts.

The solution reduces fraud losses and operational costs by maximizing automation, experience and intelligence. It combines transaction and event monitoring with digital identity services to build trust with each transaction, supported by dedicated Machine Learning models for enhanced precision.

With a lightweight API integration, or access to a full Payments Orchestration Platform, you can monitor transactions before authorization, during checkout and after authorization. Self-learning AI adapts to emerging threats and protects conversion. We also support compliance (for example PCI, 3DS and SCA) and offer a suite of products for frictionless payment journeys.

An intuitive UI lets you visualize impact and implement fraud and risk strategies using rules and business policies, lists, and a broad library of features and signals. An Analytics and Business Intelligence portal shows trends and key insights in real time. By combining AI, data insights and human foresight, Fraud Management helps you stay ahead of threats with greater predictability and security at scale.

Key features

Decision-making is powered by an evolving set of algorithms and features that run in the background and keep pace with new fraud and payment trends. These are the core features and what they mean for your business.

Patented Machine Learning technology

Self-learning algorithms, aided by a global consortium across industry sectors, for enhanced precision.

The technology lets algorithms self-learn to update your existing payment strategies and reduces the hours spent validating transactions manually.

Dedicated Machine Learning models

Focused fraud signals and risk models that run in parallel.

Our data science teams built dedicated models that provide fraud signals to detect Account Takeover (ATO), anomalies (bots) and card testing, before and after authorization. The models run in parallel to minimize fraud while optimizing conversion. We also provide risk scores for industry sectors and geographies, and custom models, to tune your overall fraud and risk strategy.

Digital identity services

Profile each transaction to build trust.

Digital identity services use AI and behavioral analytics to establish trust and confirm legitimate transactions. By orchestrating consumer data, they provide real-time fraud prevention designed to raise conversion and block sophisticated fraudsters.

Global consortium

Extensive intelligence with a global footprint.

With leading banks, billers and merchants as members, the consortium analyzes data to identify anomalies and fraud patterns. Our AI uses this intelligence to learn new patterns, simplify decisions and increase automation.

↑ Back to top

Fraud models

  • Account Takeover models detect and prevent unauthorized account access by monitoring changes in customer behavior. They help analysts prioritize and handle alerts.
  • Anomaly or Card Testing models identify unusual transaction patterns without predefined cases, such as spikes in transaction velocity or unusual use of phone numbers or emails.
  • Pre- and Post-Authorization models address payment issues and reduce false positives throughout the transaction life cycle by ensuring accurate transaction data. They monitor at multiple points to improve overall detection.
  • Customer or sector models are pre-built, ready-to-deploy models that combat sector-specific threats and maximize revenue.

↑ Back to top

Your journey

Choose how you want to use Fraud Management. Each path leads to step-by-step use cases in the integration guide.

New to the decision flow? Read how Fraud Management works in a payment first.

↑ Back to top

Learn more