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, an approach it calls combined fraud and compliance operations. Its engine layers four things: patented unsupervised machine learning that finds coordinated attacks in unlabelled data without being told what to look for, supervised models for known patterns, graph based link analysis that exposes rings across accounts and devices, and agents that automate investigations and rule tuning. A conversational agent layer launched in 2026 carries logged interactions, human approval for actions, auditability and rollback. The company is a Forrester Wave leader in anti money laundering, a Forbes Fintech 50 company, and publishes annual executive research on the gap between AI driven attacks and institutional defences.

Featurespace sells the ARIC Risk Hub to banks, acquirers and payment processors, a real time machine learning platform that builds an individual behavioural 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 behaviour and scam patterns, and adapt continuously as behaviour and attack methods change. ARIC Scam Detect extends the same approach to authorised push payment scams. The company was founded out of Cambridge University engineering research and was acquired by Visa in December 2024, and the platform is now also distributed as a Visa solution.

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 dollars 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 behavioural 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 models tailored per client, entity screening and transaction monitoring, data orchestration across internal and external sources, and a cross customer intelligence network it describes as the largest anti fraud network of its kind.

Oscilar unifies onboarding, fraud, anti money laundering compliance and credit underwriting on a single no code decisioning platform, replacing the separate point tools and rule engines institutions usually run for each. Risk teams compose and test 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 behaviour and cash flow for credit, and agents trained on the institution's own procedures triage alerts and draft investigation narratives under human governance.

Sardine unifies fraud prevention, anti money laundering compliance and credit underwriting on one platform, built around proprietary device intelligence and behaviour 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 a cross industry consortium, and layers agents that automate detection, investigation and review work for risk teams.

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 behavioural 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 authorisation decisioning, fraud prevention, rewards attribution, analytics, behavioural segmentation and loan targeting.

TrustDecision is the international arm of a major Chinese risk technology group, headquartered in Singapore with offices across Southeast Asia, running a unified decision engine across fraud prevention, credit risk and compliance for banks, digital banks, consumer lenders and payment platforms. It covers the whole customer lifecycle from onboarding and identity verification through real time transaction monitoring, promotion abuse detection and credit assessment to in-repayment monitoring, returning scores and decisions within twenty milliseconds. Graph models identify fraud rings, mule networks and collusion across users, devices and transactions, and detect credential stuffing, account farming, loan stacking, deepfakes and synthetic identities. Its architecture runs privacy preserving federated learning so institutions share collective intelligence without moving data across residency boundaries, and no-code tools let risk teams deploy rules, simulate decisions and compare outcomes with an explainable reason attached to every action.

Vyntra was formed in June 2025 by merging NetGuardians, the Swiss payment fraud and anti money laundering specialist founded in 2007, with Intix, a Belgian transaction data platform, both owned by the same private equity firm. It combines financial crime prevention with what it calls transaction observability, giving banks real time visibility of every payment alongside detection. The detection engine layers unsupervised, supervised and active learning with a community scoring service that lets participating institutions extend their risk signals beyond their own data. More than 130 financial institutions across over 60 countries use it for payment fraud, internal fraud, anti money laundering monitoring and instant payment protection, including 60 percent of Swiss state owned commercial banks and three of the country's top ten private banks.