Fraud detection used to be a search for known bad behaviour. A suspicious transaction, a mismatched device, an unusual purchase location, a sudden spike in activity, or an account behaving outside its normal range could trigger a review. Much of that work depended on rules, thresholds, blacklists, manual investigation and historical pattern matching. Those tools still matter, but they are no longer enough for the way fraud now works.
AI fraud detection represents the next stage of that evolution. It uses machine learning, behavioural analytics, transaction intelligence, anomaly detection, graph analysis, explainable risk scoring, automated workflows and increasingly AI agents to identify suspicious activity in real time. The goal is not just to find fraud after losses occur but to understand whether a transaction, account, device, customer, terminal, merchant, session or payment message looks trustworthy at the moment a decision has to be made.
That distinction matters because has become faster, more automated and more distributed. Criminals no longer rely only on stolen cards or simple account takeovers. They use synthetic identities, social engineering, malware, botnets, rogue terminals, phishing kits, mule networks, compromised credentials, AI-generated content and cross-channel attacks that move across mobile, online, card, ATM, point-of-sale and real-time payment systems. A bank can no longer look only at the account. It has to look at the behaviour, the device, the payment rail, the terminal, the customer history, the transaction message, the wider network and the immediate context.
Modern systems are designed to process enormous volumes of data, detect subtle changes in behaviour and make risk decisions fast enough to matter. In payments, that often means milliseconds. A detection system that identifies fraud after authorization may help an investigation. A detection system that can understand and block a fraudulent transaction before authorization changes the economics of fraud.
Chartis Research named Vancouver-based INETCO a Category Leader in its 2026 RiskTech Quadrants for Enterprise Fraud Solutions and Payment Fraud Solutions. The recognition highlights INETCO BullzAI’s real-time transaction intelligence, adaptive machine learning and in-flight fraud prevention capabilities. In practical terms, the company is being recognized for helping financial institutions move away from fragmented, reactive controls and toward an integrated AI-driven approach that can detect and block high-risk transactions before authorization.
INETCO’s position in the market is built around the transaction itself. BullzAI analyzes payment activity at the message level, using granular transaction intelligence to understand what is happening inside an in-flight transaction. That matters because payment fraud often hides in the gaps between systems: between channels, between jurisdictions, between legacy infrastructure and new real-time payment rails, between fraud teams and operational teams, and between customer behaviour that looks unusual but legitimate and activity that is genuinely malicious.
Chartis Senior Research Principal Philip Mackenzie pointed to INETCO’s focus on payments infrastructure and its AI-powered ability to interdict individual payments. He also cited the company’s combination of granular transaction intelligence and a machine learning approach for real-time transaction monitoring, analysis and intervention. That is the modern fraud detection thesis in one sentence: the closer the system can get to the live transaction, the more precise the intervention can become.
The capabilities highlighted in the Chartis recognition show where the industry is going. INETCO BullzAI scored highly for AI and GenAI functionality, including smart investigation agents and explainable risk scoring. It was also recognized for advanced fraud detection techniques, including a patented transaction firewall that blocks fraud at the transaction level to reduce false positives; behavioural models that continuously self-learn for each customer, card, device and terminal; speed and volume capacity; real-time transaction monitoring; message-level intelligence analysis; and deployment of rules, machine learning models, AI agents and automated workflows.
The combination is important because detection is no longer a single function, but becoming an operating layer across financial infrastructure. Rules matter because financial institutions need clear controls. Machine learning matters because fraud patterns change too quickly for rules alone. Behavioural models matter because the same transaction may be normal for one customer and suspicious for another. Explainability matters because banks, regulators, compliance teams and customers need to understand why a transaction was blocked or escalated. AI agents matter because fraud teams are overwhelmed by alerts, casework and investigations. Automation matters because real-time payments compress the window for human review. The combination is the solution.
The best systems therefore do several things at once. They score risk, detect anomalies, update behavioural baselines, identify linked entities, reduce false positives, generate explanations, trigger workflows, support investigations and, in the strongest cases, stop the transaction before loss occurs. The industry is moving toward systems that are adaptive, contextual and embedded directly into the payment flow.
INETCO is one of the leaders in that movement because of its focus on real-time payment fraud prevention and transaction intelligence. The company says it monitors more than 100 billion transactions annually and serves financial institutions, fintechs and payment service providers across more than 30 countries. Its stated performance claims include detecting emerging fraud patterns in milliseconds and blocking confirmed fraudulent transactions in under 20 milliseconds. Those are the kinds of metrics that matter in a fraud environment where real-time payments, instant transfers and automated attacks reduce the time available for intervention.
Two other companies sit in the same leading tier of the AI fraud detection market.
FICO remains one of the most established names in fraud decisioning and analytics. Chartis named FICO a Category Leader in the 2026 RiskTech Quadrants for Enterprise Fraud Solutions, Payment Fraud Solutions and Fraud Platforms, citing its long-standing market presence, advanced analytics capabilities and scalable decisioning infrastructure. FICO’s strength comes from its depth in scoring, decision management, enterprise analytics and fraud operations across large financial institutions. In a market where fraud detection has to connect risk models, payment decisions, customer experience and operational workflows, that kind of infrastructure matters.
Unit21 is also emerging as a major AI-led fraud platform. Chartis named Unit21 a Category Leader in its 2026 Enterprise and Payment Fraud Quadrants, and the company says it received the highest AI score across more than 40 fraud platforms evaluated. Its positioning reflects another major direction in the market: configurability, workflow orchestration, behavioural monitoring, graph analytics, mule detection and case management. That is especially relevant as fraud becomes networked. Fraud teams increasingly need to see not only one suspicious event, but the relationships among accounts, devices, identities, counterparties, payment flows and behavioural signals.
Feedzai, Featurespace, SAS, NICE Actimize, Hawk, Sumsub and others also play significant roles in the broader fraud detection market. The field is crowded because the problem is large, urgent and changing quickly. Different vendors lead in different environments: card fraud, real-time payments, account opening, identity verification, AML-adjacent monitoring, scam detection, enterprise case management, behavioural biometrics, device intelligence or payment orchestration. The direction of travel across the sector is clear. Fraud platforms are becoming more AI-native, more real-time, more explainable and more integrated into the transaction lifecycle.
The implication for fraud overall is significant. AI fraud detection should reduce certain categories of fraud, especially where the attack depends on speed, repetition, detectable behavioural anomalies or known network relationships. It can reduce false positives, catch emerging patterns earlier, identify account takeover attempts, detect mule networks, flag unusual device or terminal behaviour, and stop high-risk transactions before money leaves the institution. It can also improve analyst productivity by triaging cases, summarizing evidence, recommending next steps and focusing human attention on the highest-risk events.
Fraud is adversarial by nature and adapts to detection systems. By testing controls, probing thresholds, use of synthetic data, exploiting social engineering, movement across channels and increasingly using AI themselves, a model trained on yesterday’s fraud patterns can by exploited when the threat pattern evolves and looks different. A highly accurate system in one payment environment may not perform the same way in another. A risk score is not a verdict but an estimate based on available signals, historical data, model design and operational context.
Reliability in AI fraud detection has to be understood carefully. These systems are still probabilistic. They assign likelihoods, scores or classifications based on patterns in data. Even when they use advanced machine learning, graph analytics, behavioural modelling or GenAI-assisted investigation, the underlying detection problem still requires diligent verification. The system is estimating whether an event is suspicious enough to block, challenge, approve or escalate.
The practical question, though, is not whether AI fraud detection is perfect. It is whether it is better: fast enough, accurate enough, explainable enough and operationally reliable enough to improve fraud outcomes without harming legitimate customers. False negatives allow fraud through. False positives block real customers and damage trust. The strongest platforms are judged by how well they manage that tradeoff at scale.
That is why explainability is becoming central. Financial institutions cannot rely on opaque scores alone. They need to know which signals contributed to the decision: a new device, abnormal transaction velocity, unusual location, suspicious terminal, deviation from customer behaviour, link to a known mule account, risky merchant pattern, compromised credential signal or abnormal message-level attribute. Explainable AI helps analysts investigate, helps compliance teams document decisions and helps institutions tune models without turning fraud controls into an unaccountable black box.
The most important change is that fraud detection is becoming less like a checkpoint and more like a living intelligence system. It learns from each transaction. It updates behavioural baselines. It connects events across channels. It supports human analysts. It acts inside the payment flow. It does not wait for fraud to become obvious.
This is why platforms like INETCO BullzAI are becoming essential tools. The future of fraud prevention belongs to systems that can understand transactions as they happen, intervene before loss occurs and adapt as fraudsters change tactics. INETCO’s Chartis recognition shows how valuable that capability has become. In an environment where payment speed has become a competitive advantage and a fraud risk at the same time, real-time transaction intelligence is no longer a back-office tool. It is becoming core financial infrastructure.
AI fraud detection will reduce fraud where it is deployed well, governed well and connected to the right data. It will not remove uncertainty from financial crime. It will change the balance of speed, intelligence and response. Fraudsters are using AI to scale deception. Financial institutions are using AI to restore visibility, precision and control. The outcome will depend on which side learns faster.
Sources:
- INETCO announcement — Chartis Research names INETCO Category Leader
https://www.inetco.com/chartis-research-names-inetco-as-category-leader/
Use for: the June 25, 2026 news peg; BullzAI; real-time transaction intelligence; adaptive ML; in-flight fraud prevention; Chartis quote; INETCO quote.  - INETCO — 100B+ annual transactions milestone
https://www.inetco.com/inetco-surpasses-100-billion-annual-transactions-as-demand-for-payment-fraud-protection-soars/
Use for: INETCO monitoring more than 100 billion transactions annually and the shift toward real-time transaction intelligence.  - Chartis — FICO vendor spotlight, Enterprise and Payment Fraud Solutions 2026
https://www.chartis-research.com/financial-crime/7947506/vendor-spotlight-fico-enterprise-and-payment-fraud-solutions-2026
Use for: FICO as a 2026 Category Leader in Enterprise Fraud, Payment Fraud and Fraud Platforms; advanced analytics; scalable decisioning infrastructure.  - FICO — Chartis vendor spotlight / analyst report page
https://www.fico.com/en/latest-thinking/analyst-report/chartis-vendor-spotlight-enterprise-and-payment-fraud-solutions-2026
Use for: FICO’s own supporting page for the Chartis recognition and enterprise/payment fraud positioning.  - Chartis — Unit21 vendor spotlight, Enterprise and Payment Fraud Solutions 2026
https://www.chartis-research.com/financial-crime/fraud/7947495/vendor-spotlight-unit21-enterprise-and-payment-fraud-solutions-2026
Use for: Unit21 as a 2026 Category Leader in Enterprise Fraud and Payment Fraud Solutions and Enterprise Solution for Fraud Platforms.  - Unit21 — Chartis 2026 vendor spotlight
https://www.unit21.ai/resources/chartis-2026-vendor-spotlight
Use for: Unit21’s AI score, configurability, workflow, modeling and case management claims.  - Unit21 blog — Category Leader and highest AI score
https://www.unit21.ai/blog/unit21-named-category-leader-by-chartis-in-enterprise-and-payment-fraud-solutions
Use for: the statement that Unit21 scored highest across vendors evaluated for AI functionality.  - Business Wire — Unit21 Chartis 2026 announcement
https://www.businesswire.com/news/home/20260511183808/en/Unit21-Named-Category-Leader-in-Chartis-2026-RiskTech-Quadrants-for-Enterprise-and-Payment-Fraud-Solutions
Use for: independent press-release distribution, best-in-class scores and the market shift toward unified AI-first fraud prevention platforms.  - VentureLabs — INETCO BullzAI transaction firewall patent
https://venturelabs.ca/venturelabs-perago-company-inetco-awarded-patent-for-transaction-firewall-to-instantly-block-ai-driven-fraud-attacks/
Use for: patented transaction firewall, AI-driven fraud attacks, bot/zero-day blocking in milliseconds.  - INETCO / Chartis collaborative research report page — Targeting Fraud Today
https://www.inetco.com/resources/whitepapers/chartis-collaborative-research-report-targeting-fraud-today/
Use for: the Chartis collaborative research report connected to INETCO BullzAI deployment and business impact claims. Â
