Founded in 2017, Saudi Arabia-headquartered MOZN was created at a time when AI was still in its early stages. MOZN recognised a gap early on where enterprises faced challenges accessing intelligent solutions that adapted to their unique market needs and set out to build AI differently.
How did the company set out to do this? According to Malik Alyousef, co-founder and chief FOCAL product and technology officer at MOZN, this came about from developing technologies designed for the realities of every market it serves and delivering outcomes that matter.
Today, Alyousef claims, MOZN leads in two areas – financial crime prevention with FOCAL and enterprise knowledge Intelligence. “Our platforms empower organizations to make informed decisions, centralize critical operations, and unlock new opportunities for innovation,” he said.
FOCAL is purpose-built to address the most critical challenges in financial crime prevention. As an AI-powered platform, the solution enables financial institutions to prevent fraud and maintain AML compliance with unmatched efficiency.
“Its Agentic AI capabilities streamline investigations, reduce manual workload, and improve accuracy, resulting in fewer false positives and a better customer experience,” said Alyousef.
Pain points
Alyousef says one of the biggest challenges clients face is “fragmented, siloed systems that create blind spots in fraud and AML detection.” FOCAL addresses this by unifying fraud, AML and KYC into a single platform that gives institutions a holistic view of the customer journey from onboarding through ongoing monitoring.
He explains advanced AI, ML and Agentic AI automate investigations, case management and regulatory reporting, cutting down on manual work and reducing operational cost. Dynamic risk scoring and analytics help teams reduce alert fatigue and high false positives, while real-time monitoring, anomaly detection, and graph analytics allow institutions to proactively identify sophisticated threats, including organised fraud rings and money mule networks.
Alyousef adds that FOCAL also “supports compliance with changing global and regional standards” through automated reporting, audit trails and customisable workflows. Its modular, API-first architecture ensures easy integration with legacy systems or external data sources, whether clients need the full platform or particular modules. As a cloud-native solution, it scales easily to support even the largest transaction volumes.
What sets MOZN apart
Alyousef explains that MOZN has built FOCAL as a unified FRAML platform that combines fraud and AML capabilities in one centralized solution, giving clients a complete view of the customer journey from onboarding and in-app activity to payments and ongoing monitoring. This removes the blind spots that typically arise when systems are separated.
He highlights MOZN’s leadership in AI and Agentic AI, which powers real-time detection, autonomous case investigation, dynamic risk scoring and automated regulatory reporting. A key differentiator is their patented name-matching engine, which delivers industry-leading multilingual phonetic matching optimised for both Arabic and Latin scripts and significantly reduces false positives in sanctions and payment screening.
FOCAL’s flexibility also plays a major role: its modular, API-first architecture supports SaaS, private cloud and on-premise deployments, alongside no-code rule building, and highly customisable workflows.
Alyousef points to recognition from Forrester, Chartis, Frost & Sullivan, QKS Group and others, as well as measurable outcomes such as over 90% fraud reduction for major clients. He adds that a customer-centric approach—dedicated success teams, managed services and flexible pricing—ensures rapid onboarding, operational efficiency and strong regulatory compliance across their customer base.
AI’s power in ML detection patterns
Alyousef describes their AI model as “a game-changer in how we look at money laundering risk at the account level,” mainly Shown Above: Malik Alyousef, Co-founder, Chief FOCAL Product and Technology Officer at MOZN because traditional systems rely on static rules and historical data, which leaves them “always looking backward.”
What MOZN has built is dynamic and behavioural: the model continuously learns and adapts as new patterns emerge rather than simply checking boxes.
He explains that it’s a supervised learning model trained on labelled data so it can distinguish normal activity from suspicious behaviour. Its strength, he says, comes from two principles: the risk profile of an account evolves over time, and the system analyses behaviour rather than isolated transactions.
He breaks their approach into three focus areas. The first is onboarding demographics—when a customer opens an account, FOCAL creates a baseline risk signature using demographic and onboarding data. On top of that sit device and login patterns, using persistent device fingerprints to understand how, when and where accounts are accessed, which helps surface anomalies quickly.
The final layer analyses transaction behaviour and network activity. The model learns what is normal for each account— transaction size, frequency and counterparties—and flags deviations such as micro-transfers or unusual aggregation that may suggest layering. By mapping links to known suspicious networks, it can surface hidden connections that rule-based systems typically miss.
This “holistic and adaptive” approach reduces false positives, helps teams prioritise the highest-risk cases and strengthens the institution’s ability to stay ahead of increasingly sophisticated financial crime tactics.
AI models
Alyousef is clear that FOCAL’s AI models are built with regulatory transparency and explainability “at their core,” ensuring decisions can be clearly communicated to regulators and internal stakeholders. He describes a comprehensive model-governance framework that covers development, validation, data lineage and bias monitoring, making the models robust, fair and fully auditable.
He explains that SHAP values underpin their explainability approach at both global and local levels. Globally, SHAP helps validate and interpret overall model behaviour and shows how features influence decisions across scenarios. Locally, SHAP values are integrated into dashboards and audit logs for every flagged transaction or account, allowing teams to see, feature by feature, why something was marked suspicious.
To make interpretability even more accessible, FOCAL also provides surrogate decision trees that give a simplified, visual representation of model logic. All of this, he says, ensures the system isn’t a “black box” but a transparent, accountable and regulator-ready solution that builds trust with clients and the wider ecosystem.
Tackling criminals
Alyousef notes that criminals are constantly adapting and increasingly using sophisticated tools, including AI, to try to bypass AML systems. But as he explains, because FOCAL’s detection models are dynamic and adaptive, “it actually becomes much harder for even advanced evasion tactics to succeed.”
Every new device, IP address or transaction is analysed in real time, so gradual behavioural shifts—such as those used by bot-driven laundering operations—are immediately surfaced as anomalies.
He points out that criminals often rely on layering, moving funds across multiple accounts and devices to obscure trails. FOCAL counters this with network-level analysis that maps relationships and links to known suspicious clusters; even if individual transactions appear harmless, the broader topology can still be flagged.
Explainability also plays a defensive role: each flagged event includes a clear, feature-level breakdown using SHAP values, which prevents AI-generated obfuscation from slipping through unnoticed. MOZN also trains its models with adversarial examples and monitors for concept drift, ensuring robustness even as criminals attempt to “game” the system.
As Alyousef puts it, their approach is layered, adaptive and transparent—empowering analysts to intervene early and helping institutions stay ahead of both today’s and tomorrow’s financial crime tactics.
Future plans
As MOZN looks toward the future, what is next for the business? In the words of Alyousef, the firm’s vision is clear: it is moving from being a regional leader to becoming a global powerhouse in AI-powered enterprise solutions for financial crime prevention and AML compliance.
“We’ve built a strong foundation in the MENA region, and now we’re accelerating expansion into high-growth markets across Africa, APAC, and Europe,” said Alyousef. “This is about scaling our impact and bringing our expertise to global institutions.”
In the technology arena, the MOZN co-founder believes the next big leap is Agentic AI. The company itself is taking it from pilot to full production across AML and KYC workflows.
He explained, “This means autonomous case investigations, generative rule building, and dynamic risk profiling, capabilities that will allow our clients to automate compliance, reduce false positives, and resolve cases in seconds instead of hours. We’re also doubling down on R&D. Expect innovations in explainable AI, behavioural biometrics, graph analytics, and real-time risk engines.”
Beyond technology, the company is also investing in leadership, with its FOCAL Point Research & Intelligence Center becoming a hub for collaboration with industry associations, experts, and banks. “We’ll continue hosting leadership forums, publishing insights, and shaping compliance frameworks for the future,” said Alyousef.
Alyousef concluded, “Finally, we remain committed to customer-centric growth. Our robust technology, consultative approach, and managed services will scale further, ensuring measurable outcomes like fraud loss reduction, operational efficiency, and regulatory readiness. Every innovation we deliver is tied to real-world impact for our clients.”
Copyright © 2026 FinTech Global
