Shufti launches AI-powered transaction monitoring engine

Shufti has launched Transaction Trust Monitoring, an agentic FRAML system that ties transaction screening to verified identity and carries alerts through investigation to regulator-ready reports. The product is available now and is designed to help banks, fintechs and other financial firms reduce manual compliance work while keeping MLRO oversight on every filing.

Why it matters: - Shufti is trying to compress fraud and anti-money laundering monitoring into one workflow that starts at onboarding and ends with a filed report. - The launch targets compliance teams that need to review more alerts, across more jurisdictions, without adding headcount. - The product is designed to reduce manual case assembly, keep identity and transaction data in one risk view, and preserve an audit trail for regulators.

What happened: - Shufti announced Transaction Trust Monitoring (TTM), an agentic FRAML engine that carries a case from the first suspicious transaction through investigation to a regulator-ready report. - TTM scores each transaction against the identity Shufti verified at onboarding. - The system routes alerts through a role-gated investigation process and generates a report in the format required by the Financial Intelligence Unit for Money Laundering Reporting Officer review and approval. - TTM integrates with existing KYC and case management systems instead of replacing them. - Shufti said TTM is available now to banks, financial institutions, payment service providers, remittance businesses, MSBs, fintechs, neobanks, virtual asset service providers and iGaming operators.

The details: - FRAML combines fraud and anti-money laundering controls in one discipline, and TTM scores both signal types in the same pass. - TTM uses one platform Shufti owns end-to-end, with no acquired modules and no third-party stitching. - Each transaction is scored against a biometrically verified customer, with deepfake and synthetic identity defenses at onboarding and behavioural biometrics and device fingerprinting after login. - The engine screens against more than 4,000 watchlists and 6 million-plus PEPs, with sanctions coverage across OFAC, OFSI, EU CFSP, DFAT and the UN Consolidated List, plus multilingual contextual adverse media. - TTM checks more than 1,600 data points per transaction in under 500 milliseconds. - The score includes location, device, banking and threshold data, plus more than 15 velocity checks running from one minute to 180 days. - Every transaction receives a risk score from 0 to 100. - Each alert includes a case summary, tier classification, false-positive assessment and recommended next action. - The AI rule builder lets users describe a rule in plain language, then builds and calibrates it by industry, regulation and risk appetite. - Shufti says the rule builder backtests rules against 90 days of history to project alert volume, precision and fraud caught by value. - Alerts score 75 and above go to the MLRO, 50 to 74 go to L2 investigation, and 25 to 49 go to L1 triage. - Flagged transactions can be placed on a soft hold and routed to an analyst queue rather than being automatically declined. - The AI triage co-pilot drafts narratives and recommends actions, but does not execute a filing. - Twenty-four ABSOLUTE rules always trigger and cannot be suppressed. - Every decision is kept on a five-year immutable audit trail. - Unscoreable transactions return a NOT ASSESSABLE verdict. - Behaviour is measured against each customer’s own pattern using a rolling 90-day baseline across 16 dimensions. - Linked devices, IP addresses and contact details are used to expose multi-accounting and money mule networks. - Four AI agents are live across the lifecycle, with three more in development for rule recommendation, data readiness and regulatory change monitoring. - MCP integration allows external AI tools to drive a firm’s monitoring setup. - Reporting closes the workflow, with suspicious activity and suspicious transaction reports generated in regulator-native XML. - The supported formats include FinCEN BSA XML 2.0 for U.S. reporting and goAML, the UNODC schema used by financial intelligence units across Europe, the Middle East, Africa and Asia Pacific. - The MLRO reviews, edits and authorises each report before the firm downloads and submits the file. - Threshold reports are obligation-based rather than suspicion-based, so they auto-batch daily under a deadline timer. - The MLRO can pause any batch. - TTM is available over RESTful API and SDK, with sandbox access and on-premise deployment. - Detection covers SWIFT and SEPA flows, cumulative player history, corridor and agent-level monitoring, wallet screening, on-chain risk scoring and Travel Rule support across 240 countries and territories. - Transaction Monitoring Software can be configured as a standalone product or as part of Shufti’s full compliance lifecycle. - Shufti also published A Guide to Risk-Aligned Transaction Monitoring, which covers FATF monitoring requirements, five principles from the HKMA for system selection, and the framework changing under Regulation (EU) 2024/1624 from July 2027. - A video demo is available here. - The guide is available here.

Between the lines: - Shufti is positioning TTM as a single compliance stack, not a point solution, which could appeal to firms trying to reduce vendor sprawl. - The focus on explainability, audit trails and MLRO approval suggests the product is built to fit regulated workflows rather than replace human decision-making. - The plain-English rule builder is the clearest sign that Shufti wants to lower the operational burden of tuning monitoring rules.

What's next: - Shufti said three more AI agents are in development. - The company is also using the launch in its Shufti Innovation Drop product demo series. - Firms adopting TTM can deploy it through API, SDK or on-premise setups, depending on their integration needs.

The bottom line: - Shufti is betting that transaction monitoring will move toward unified, AI-assisted compliance workflows that tie identity, monitoring and reporting into one auditable engine.