As payment volumes grow and instant payments become more common, banks are facing pressure to detect suspicious activity quickly while keeping false alerts and investigation backlogs under control.
Fraud analytics platforms that combine transaction monitoring with case management can help address these challenges.
In this article, we’re comparing five solutions, including Vyntra, NICE Actimize, Feedzai RiskOps, Hawk AI, and Featurespace, to see how their capabilities differ.
The best fraud analytics platform for transaction monitoring and case management
Fraud analytics and case management platforms compared:
Platform | Key strength | Case management | Best for |
Vyntra | AI anomaly detection and unified fraud and AML profiling | Centralised workspace with explainable risk scores and complete audit trails | Banks looking for real-time monitoring and unified fraud and AML case handling |
NICE Actimize | Multi-layered rule libraries and entity risk scoring | Enterprise workflow automation with network visualisation that surfaces hidden relationships | Large enterprise and complex multi-jurisdictional banks |
Feedzai RiskOps | Real-time multi-channel risk scoring and agentic AI | RiskOps Studio with centralised strategy and case configuration | Tier 1 to 2 banks and high-volume payment processors |
Hawk AI | Payment-rail-agnostic detection with rapid model deployment | Centralised workspace with transaction timeline tracking | Banks seeking real-time payment fraud prevention across multiple rails |
Featurespace | Adaptive behavioural analytics and transaction neural networks | Multi-tenant case routing and individual behavioural profiling | Banks and card issuers needing adaptive card and payment protection |
“Banks are heavily impacted by too many false positives from traditional rule-based systems, which forces them to hire many investigators and data scientists. This cost, coupled with unhappy customers due to blocked transactions, is the main driver for banks to seek third-party fraud detection solutions. These solutions need to improve detection rates, mitigate false positives, offer real-time payment fraud prevention, and provide explainability for better investigations.” – Sandy Lavorel, Head of Fraud Intelligence, Vyntra.
Vyntra
Since we’re writing the guide, we’ll start with our approach to transaction monitoring and case management. Vyntra combines transaction monitoring and case management in one financial crime prevention platform.
We use machine learning, behavioural profiling, and configurable rules to detect suspicious activity across real-time and batch transactions. Banks can monitor payment fraud, internal fraud, AML, and sanctions risks through one intelligence layer, with centralised investigations and reporting.
Key features include:
- AI-powered fraud detection: Combines pre-built rules, entity profiling, and machine learning models to identify unusual transaction behaviour and help banks reduce false positives in transaction monitoring.
- Real-time transaction monitoring: Integrates into existing payment flows to score transactions, prioritise high-risk alerts, and help stop suspicious payments before funds leave.
- End-to-end case management: Consolidates fraud, AML, and sanctions alerts into one workspace, with automated task assignments, customizable investigation workflows, and complete audit trails.
- Explainable AI and configurable dashboards: Provides clear risk scores, supporting evidence, and transaction insights to help analysts investigate alerts. Its no-code/low-code Modelling Studio also allows AML teams to adjust detection scenarios.
Best for: Banks and financial institutions looking to combine AI-powered transaction monitoring, fraud and AML detection, and end-to-end case management in one platform, while reducing false positives and investigation backlogs.
Read more: Solutions that offer real-time transaction observability for banks.
We know Vyntra may not be a good fit for every bank, so here are four alternative platforms.
NICE Actimize
NICE Actimize provides financial crime management solutions for large banks and financial institutions. Its Suspicious Activity Monitoring (SAM) solution combines established detection rules with machine learning and predictive analytics to identify suspicious transactions and support AML investigations.
Key features include:
- Multi-layered detection: Uses multiple layers of rules and advanced analytics to identify known threats and adapt to emerging money laundering techniques.
- Machine learning and anomaly detection: Combines machine learning algorithms and unsupervised customer segmentation to identify statistical anomalies in transaction activity.
- Graph-based risk analysis: Uses entity-link graph technology to uncover hidden relationships between accounts, shell companies, and payment beneficiaries.
- Predictive risk scoring: Helps compliance teams prioritise high-risk alerts and allocate investigative resources effectively.
Best for: Large enterprise and complex multi-jurisdictional banks.
Feedzai RiskOps
Feedzai RiskOps connects fraud detection, risk scoring, and case resolution across the complete customer lifecycle. The platform processes high-volume transactional data alongside device signals and behavioural biometrics.
- Real-time fraud detection: Assesses risk continuously across account opening, ongoing authentication, and high-frequency payment processing.
- Behavioural analytics and risk profiling: Integrates device signals, behavioural biometrics, and transaction history into unified customer risk profiles.
- AI-powered case management: Uses specialised AI agents within RiskOps Studio to automate routine investigative steps and accelerate decision-making.
- Configurable fraud strategies: Offers no-code strategy configuration, backtesting, and challenger testing to validate detection rules before live deployment.
Best for: Tier 1–2 banks and high-volume payment processors managing fraud risks across multiple channels and customer interactions.
Read more: How to reduce bank AML system false alerts
Hawk AI
Hawk AI provides cloud-native payment fraud detection and AML transaction monitoring through a single payment-rail-agnostic API. The system focuses on rapid model deployment and low-latency transaction scoring.
- Real-time transaction monitoring: Evaluates transactions in real time across traditional clearing networks, instant payment schemes, and peer-to-peer mobile applications.
- AI-powered fraud detection: Deploys tailored detection models based on institutional data patterns within short implementation windows.
- Explainable risk scoring: Delivers risk scores with low processing latency to support immediate payment decisions within the processing window.
- Configurable fraud controls: Offers a multi-tenant structure and self-service sandbox controls for testing new detection rules before deployment.
Best for: Banks and payment processors seeking real-time fraud prevention across multiple payment rails, with configurable detection rules and centralised case management.
Featurespace
Featurespace (now part of Visa following the 2024 acquisition) uses behavioural analytics to protect payment networks, card issuers, and merchant acquirers from emerging financial crime schemes. Its proprietary algorithms build individual behavioural profiles to identify statistical anomalies in real time.
Key features include:
- Adaptive behavioural analytics: Updates customer risk baselines as behaviour changes, helping distinguish legitimate activity from fraud.
- Advanced fraud detection models: Use deep neural network architectures to identify complex scam patterns, card fraud, and account takeovers.
- Multi-tenant architecture: Allows card issuers and acquirers to manage distinct fraud controls for separate customer portfolios.
- Flexible deployment: Supports fully hosted cloud environments and on-premises banking infrastructure.
Best for: Banks, payment processors, and merchant acquirers looking for adaptive behavioural fraud detection, particularly across cards and payments.
Look for real-time detection with end-to-end case management
Selecting a fraud analytics platform requires balancing instant payment speed, detection accuracy, and operational efficiency for investigation teams. A solution that connects AI-driven anomaly detection with end-to-end case management can help your bank reduce false alerts, prioritise investigations, meet regulatory requirements, and prevent fraud before funds leave.
FAQs
How do fraud analytics platforms reduce false positives in transaction monitoring?
Advanced platforms use AI models to learn what normal activity looks like for each customer instead of relying only on fixed rules. By analysing transaction patterns and customer behaviour, they can tell the difference between legitimate activity and suspicious transactions, helping reduce unnecessary alerts.
What is the difference between rule-based transaction monitoring and AI anomaly detection?
Rule-based systems flag transactions that break predefined rules or limits, which can lead to false alerts and missed fraud. AI anomaly detection learns from past transaction patterns to spot unusual activity, including new fraud tactics that existing rules might miss.
Why is integrated case management important for bank fraud operations?
Integrated case management brings transaction data, customer history, and alert details into one place. This means investigators can review and manage cases without switching between systems, helping them resolve alerts faster while keeping records for regulatory reporting.
