Directory of AI transaction monitoring and sanctions screening vendors

Arva AI builds agents to do the manual financial crime work banks and fintechs currently staff with analysts, across three products covering sanctions and adverse media screening, know your business and know your customer onboarding, and transaction monitoring alerts. Its agents conduct web due diligence on a business to establish what it actually does, verify and extract from formation, ownership and banking documents, detect document fraud, enrich entity and individual records from online sources, and handle the information exchange with the applicant during onboarding, drawing on incorporation data across more than 150 countries. The stated design is that agents replace human analysts outright on low and medium risk cases, delivering instant onboarding, and the platform can either sit alongside an existing compliance stack or replace it end to end.

Bretton AI, which operated as Greenlite until its 2026 rebrand, supplies agents that carry out financial crime compliance work rather than tooling for humans to do it faster. The agents clear first line sanctions, politically exposed person, adverse media and transaction monitoring alerts, run customer and enhanced due diligence including financial statement and web presence analysis, and hand enriched cases with drafted narratives to human analysts for the judgement calls. Its distinguishing layer is a trust framework built around named United States banking supervisory guidance on model risk and transaction monitoring.

ComplyAdvantage supplies the risk intelligence and screening layer that banks, fintechs, insurers and crypto businesses run their financial crime programmes on, built entirely on proprietary sanctions, politically exposed person, watchlist and adverse media data rather than licensed feeds. Its Mesh platform unifies customer screening, ongoing monitoring, transaction monitoring, payment screening and risk scoring in one system, lets teams screen against 49 distinct risk sub categories, and uses agentic automation to resolve routine alerts without an analyst. It monitors over 500 million customers annually and is used by other vendors as their screening source.

Deconflict runs a two sided intelligence network connecting law enforcement agencies with banks, credit unions, fintechs, exchanges and stablecoin issuers on digital asset financial crime. On the agency side it surfaces when separate investigations converge on the same on chain entities across federal, state, local, tribal and international jurisdictions, preventing agencies from unknowingly working the same target. On the institution side an interface lets compliance teams query active law enforcement signals before a transaction settles, returning typology, source agency and confidence level formatted to drop into suspicious activity narratives and account closure records. Identifiers are exchanged in hashed form, investigative files and tactics stay siloed on the agency side, and participation requires verified law enforcement or regulated institution credentials.

Dossiers sells an anti money laundering and compliance platform to banks, fintechs and other regulated firms, bringing onboarding, screening and ongoing monitoring into one system and running purpose built agents alongside the compliance team to handle screening triage, evidence gathering, alert review and profile monitoring. Its stated design principle is that every agent action is logged, explainable and auditable, and its stated diagnosis of the problem is that static rules catch a fraction of laundering that moves through layered networks of shell companies, trade schemes and intermediaries across jurisdictions, while periodic reviews miss behavioural shifts in real time. The company began in Sri Lanka and now operates from Singapore, and its distinguishing asset is risk data for South Asia, where coverage has historically been thin, built by a team whose background is in data journalism and fact checking and connected to international investigative reporting networks. It positions the platform against Central Bank and financial intelligence unit obligations in the markets it serves, in a region where grey listing by the international standard setter has had national consequences.

Fincom screens payments and customers against sanctions and watchlists using patented Phonetic Fingerprint technology, which converts a name into a mathematical representation of how it sounds rather than how it is spelled, so matching survives misspelling, unstructured formats, different alphabets and transliteration errors across 44 languages including Arabic, Russian, Chinese and Korean. Forty eight algorithms from phonetics, computational linguistics and mathematics combine with supervised machine learning and a fuzzy logic engine that weighs attributes such as date of birth, address and identifiers alongside the phonetic match. The company reports cutting alert rates from an industry average around 30 percent to under 3 percent and operational costs by more than 80 percent, validated across numerous United States banks, screening in under 200 milliseconds. Applications span sanctions screening, payment screening, payee verification, perpetual customer due diligence, trade finance and model validation.

Flagright provides real time transaction monitoring, sanctions and watchlist screening, customer risk scoring, case management and regulatory reporting for fintechs, neobanks, payment firms and banks, screening each transaction before it clears rather than in overnight batches. Compliance teams author detection logic themselves through a no code engine that also accepts natural language, simulate rule changes against live conditions without touching production, and run agents that triage false positives, assist investigations, enforce investigation quality and draft case closure narratives. It is offered as hosted software, hybrid, or fully on premise.

Hawk provides financial crime detection to more than 80 institutions worldwide, from tier one banks to digital-first fintechs, combining traditional rules with explainable machine learning across transaction monitoring, customer and payment screening, customer risk rating, entity risk detection and fraud prevention in one modular platform. Its overlay model supplements a bank's existing rule-based systems rather than replacing them, and it reports raising alert accuracy toward 90 percent in some deployments while cutting false positives and uncovering twice as many previously undetected cases of novel criminal activity. An investigative agent applies agentic AI to anti-money laundering casework, handling data collection, case categorisation and automated drafting of suspicious activity report narratives. Explainability is central to the design, on the argument that an institution must be able to justify why a specific customer was flagged. Integration reaches all four major US core banking providers.

Lucinity builds what it calls Human AI for financial crime prevention, pairing models with the investigators who use them rather than replacing them. Its assistant Luci, launched in 2023 as the first generative copilot for this function, summarises and analyses complex cases, runs adverse media checks, drafts suspicious activity reports and takes investigations from hours to minutes, working either inside the company's own case management and customer view modules or as a plugin into whatever transaction monitoring, fraud and know your customer systems an institution already runs. The platform is deliberately system agnostic, built on a major cloud provider's enterprise AI service, and uses retrieval augmented generation with validation and detailed audit logging. A large enterprise software group secured rights to the investigation technology in 2026 and embedded it in its own financial crime platform.

Nasdaq Verafin is a cloud financial crime management platform for banks and credit unions, founded in 2003 in St John's, Newfoundland and acquired by Nasdaq in 2021. It covers anti money laundering, fraud detection, sanctions screening, high risk customer management, investigation and regulatory reporting, and its detection layer is built on a cross institutional consortium data network spanning roughly 2,800 financial institutions, which is presented as the source of its typology awareness rather than as a background asset. Since late 2025 the company has shipped an agentic workforce designed to mirror the roles inside a bank or credit union's anti financial crime team. A second phase adds an agentic anti money laundering analyst, opening on cash structuring alerts where deposits are deliberately broken up below reporting thresholds, and an agentic fraud analyst, both automating alert triage in the manner of an experienced human investigator, plus alert auto dispositioning and consortium insights delivered inside the workflows. More than 650 institutions had adopted the agentic products by mid 2026. A platform agnostic deployment model extends the same workers across third party systems, where the agent signs into the other vendor's product, navigates its alert queue and dispositions alerts as a person would, while retaining access to the consortium network behind it. Institutions can configure the level of automation and of human review separately for each workflow according to their own risk appetite.

NICE Actimize sells financial crime risk management to banks and financial institutions through Xceed, a cloud platform that unifies fraud prevention and anti money laundering in one workflow. Entity centric monitoring, fuzzy logic watchlist matching and sanctions screening run alongside real time fraud detection, with suspicious activity and currency transaction reports filed to authorities from the same system. Xceed AI Agents, introduced in 2025, automate alert triage, backlog categorisation and high risk case summarisation, hold conversational dialogue with investigators and learn from analyst decisions, under a stated analyst in the loop model. The business is part of NICE, a listed company, and serves more than 1,000 organisations across over 70 countries.

Quantexa builds a resolved view of customers, counterparties and beneficial owners by reconciling records across internal systems, third party feeds, public records and corporate registries, then generates the network context around each entity so investigators see relationships rather than isolated alerts. Entity resolution runs on a predictive model the company describes as transparent and tuneable, network analytics layer community detection and pathway analysis on top, and the same foundation is reused across financial crime, customer due diligence, fraud, credit risk and customer intelligence.

Quantifind runs risk screening and investigations for financial crime through its Graphyte platform, resolving entities and scoring risk from billions of external sources covering sanctions lists, public records, adverse media and corporate data. Its differentiator is name matching and contextual typology assessment built on a decade of patented research, aimed at the false positive burden that forces banks to staff large analyst teams, and it serves tier one, regional and digital banks alongside federal, state and defence agencies.

Refine Intelligence inverts the usual anti money laundering approach by clearing legitimate customers rather than hunting suspicious ones, a method it calls greenflagging. Its models are trained on a proprietary dataset of genuine customer activity built from millions of financial records, and map each transaction alert to the ordinary life events most likely to explain it, ranked by probability, covering things like selling a house, paying a contractor or buying a used car. Alongside that, automated digital inquiries ask the customer directly about source of funds and the nature of the activity, letting many alerts be resolved by the customer themselves and giving investigators a real time explanation with an audit trail. Questions are deliberately structured and consistent to avoid both investigator bias and tipping off risk. The company reports that 64 percent of all alerts at its banking partners trace to just five everyday scenarios.

SAS sells a unified financial crime portfolio to banks, credit unions and other financial institutions, spanning anti money laundering transaction monitoring, payments fraud, application and identity fraud, sanctions and watchlist screening, customer risk rating, investigation workflow and regulatory reporting. The line runs on the company's own analytics platform and is positioned as a single environment for customer centric decisioning rather than separate fraud and compliance systems, with transparent and auditable models offered as the answer to supervisory expectations. It is a named and separately maintained industry business under its own executive, distinct from the company's wider horizontal analytics work.

Silent Eight builds custom AI models for each bank it serves, trained on that institution's own historical case data so the system replicates how its investigators actually reason and decide. Its Iris platform runs end to end compliance automation across name and transaction screening, investigation, decision making and quality assurance, closing alerts with explainable, auditable reasoning rather than a score, and benchmarking an institution's performance against peers worldwide. Agents have run in live environments at global banks since 2018, and the company reports investigation times cut by up to 60 percent and manual workloads by 70 percent. Two of the world's largest banks are both customers and investors.

ThetaRay detects financial crime using unsupervised anomaly detection that learns what normal looks like in an institution's payment flows and flags deviations without being told in advance what a typology looks like, which is aimed squarely at the schemes rule based systems cannot describe: mule networks, layered transfers and undisclosed nested correspondent relationships. Its platform covers transaction monitoring, sanctions and watchlist screening, customer risk assessment and an agentic investigation suite, and it is built to overlay existing monitoring engines rather than replace them.

Tookitaki unifies anti money laundering monitoring, fraud prevention, screening and case management into one platform for banks, digital banks and payment institutions across Asia Pacific, pre configured for the requirements of four named regional supervisors. Its distinguishing asset is a collaborative intelligence network of more than 200 institutions contributing anonymised typologies, red flags and fraud patterns, now exceeding 1,200 risk scenarios, which reach members through federated learning so detection improves without customer data ever being shared. Every flagged transaction carries an explanation of the data and logic behind it, an investigation copilot drafts case summaries and regulatory filings, compliance teams adjust thresholds without engineering support, and drift detection and retraining are built into the model lifecycle.

Unit21 consolidates fraud prevention and anti money laundering into one platform covering real time transaction monitoring, entity and network analysis, customer risk rating, case management and regulatory filing. Risk teams author detection logic through a no code interface without engineering support, machine learning scores expose which variables drove each alert, and configurable agents carry investigations from signal through evidence collection and narrative drafting to a regulator ready filing with a full audit trail.