Directory of AI payment fraud detection vendors for banks

DataVisor unifies fraud detection, anti money laundering monitoring, customer and business onboarding checks, case management and risk decisioning on one platform covering the whole customer lifecycle. Its engine layers patented unsupervised machine learning that finds coordinated attacks in unlabeled data, supervised models for known patterns, graph link analysis that exposes rings across accounts and devices, and AI agents that create and tune rules, triage and summarize alerts and draft suspicious activity reports, with every output carrying plain language reasoning, a complete audit trail and human review at report submission and strategic rule changes. Customers include NASA Federal Credit Union, TaskRabbit, Affirm and Galileo, and the platform processes more than 30 billion events a year.

Featurespace sells the Featurespace Platform, formerly ARIC Risk Hub, to banks, acquirers and payment processors: a real time machine learning platform that builds an individual behavioral profile for every customer and scores each payment against it rather than against fixed fraud rules. Its Adaptive Behavioral Analytics and Automated Deep Behavioral Networks profile normal activity, peer group behavior and scam patterns, and adapt as behavior and attack methods change, and the platform can be deployed on premise or as a hosted service. The company was founded on University of Cambridge engineering research and was acquired by Visa in December 2024, and it now also powers Visa A2A Protect.

Feedzai runs real time fraud, scam and financial crime prevention for the world's largest banks, payment networks and acquirers, risk assessing around 120 billion events and $9 trillion of payment volume a year across onboarding, digital activity, card payments, instant transfers and anti money laundering workflows. Its Pulse engine combines customer authored rules with machine learning and builds a behavioral baseline for each individual customer, and in 2026 it introduced a foundational model purpose built for financial risk data alongside a network derived scoring service delivered through a single interface.

Fraud.net unifies fraud prevention, anti money laundering compliance and risk management on one platform for banks, credit unions, payment processors, acquirers, remittance companies, fintechs and commerce businesses. It combines a no code rules engine the customer authors with machine learning tailored per client, including anomaly detection and graph neural networks, entity screening and monitoring, transaction monitoring, case management, data orchestration across internal and external sources, and its Global Anti Fraud Network, which pools insights across members.

Oscilar unifies onboarding, fraud, anti money laundering compliance and credit underwriting on a single no code agentic risk platform, replacing the separate point tools and rule engines institutions usually run for each. Risk teams compose and backtest workflows through a visual builder or in natural language, more than eighty data sources connect through an integration hub, named machine learning models score balance, repayment behavior and cash flow for credit, and more than fifteen agents, from alert triage and sanctions screening to suspicious activity report and credit explainability agents, work under analyst review. Nacha lists Oscilar as a Preferred Partner for account validation and fraud monitoring.

Sardine unifies fraud prevention, anti money laundering compliance and credit underwriting on one platform, built around proprietary device intelligence and behavior biometrics that it folds into every other signal rather than offering as a separate module. It covers the lifecycle from onboarding and account funding through payments, adds sanctions and politically exposed person screening, transaction monitoring, network investigation tooling and the Sonar cross industry consortium, and offers ten agents, from transaction monitoring and due diligence to suspicious activity report generation, that risk teams run against alert queues or call from their own systems.

SEON combines fraud prevention and anti money laundering compliance in one platform, built around enriching a thin signup input such as an email address, phone number or address into a wide risk picture. It checks those identifiers against hundreds of online platforms and breach sources to expose fake or shallow digital trails, layers device fingerprinting and behavioral signals on top, and returns an enriched profile, rule evaluation and score in a single real time response. Decisioning runs on customer authored rules alongside machine learning that the company deliberately keeps inspectable rather than opaque.

Spade turns the indecipherable strings that banks and fintechs receive from card, ACH and wire transactions into verified merchant records, matching raw data against a proprietary ground truth database so an institution knows exactly where and with whom each transaction occurred. AI agents continuously scan the web and external sources to fill metadata gaps and remove duplicates, producing precise geolocation and verified merchant categories independent of the legacy category codes the industry has relied on. The company publishes 99.9 percent coverage of United States and Canadian merchants at over 99 percent accuracy, with tail latency under 40 milliseconds. Customers use the enriched data for authorization decisioning, fraud prevention, rewards attribution, analytics, behavioral segmentation and loan targeting.

TrustDecision, headquartered in Singapore with offices in Malaysia, Indonesia, the Philippines, the United Arab Emirates, Saudi Arabia, Mexico and Hong Kong, sells fraud, credit and identity risk technology to banks, consumer lenders, payment companies and digital businesses. Its products include device intelligence, a global risk persona built from IP, email and phone signals, credit scoring and credit data insights, the ARGUS fraud management platform, the PISTIS credit management platform and the ARCHER risk decisioning system, returning scores, labels and decisions within 20 milliseconds through public APIs and SDKs for Android, iOS, web and other platforms. The company reports more than 1,000 clients and an estimated $10 billion in losses avoided for businesses each year, and its privacy policy states that end user data is never shared between clients.

Vyntra was formed in 2025 when NetGuardians, the Swiss payment fraud and AML specialist founded in 2007, merged with Intix, a Belgian transaction data platform founded in 2011, under the ownership of Summa Equity. Its financial crime platform scores payment fraud, scams, insider threats, AML risk and sanctions matches with a rules and machine learning engine, attaches the drivers to every score and adds an opt in community scoring service in which members share risk signals but not raw customer data. Its transaction observability platform gives banks real time visibility of payments across SWIFT, ISO 20022 and domestic and instant payment schemes. The platform is always deployed on the customer's own infrastructure, on premises, in its own cloud or hybrid, and is priced as a subscription on transaction volume and users. More than 130 financial institutions in over 60 countries use it, including four of the top ten European banks.