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Saudi-Based Mozn Uses AI To Detect Money Laundering & Fraud
The company combats a wide range of financial crimes using pattern recognition and advanced algorithms.
Saudi Arabia’s Mozn, an AI platform founded by Dr. Mohammed Alhussein, uses advanced artificial intelligence algorithms to detect and prevent financial crimes and increase compliance.
The company’s advanced platform is known as FOCAL. It can sift through masses of financial data using pattern recognition and other advanced techniques to spot fraud and money laundering in real time.
Dr. Alhussein developed the FOCAL platform by studying anti-money-laundering and anti-terrorism legislation and compliance and quickly realized that traditional (often manual) checks and safeguards were too slow to act.
Mozn’s AI technology uses name-matching algorithms uniquely optimized for the Arabic language and reconciles its findings against 1,300 international and regional sanctions. Meanwhile, the system’s anti-fraud functionality detects suspicious patterns by confirming payee identities against the records of destination accounts — a process that is said to reduce investigation times by up to 95%.
Also Read: A Guide To Digital Payment Methods In The Middle East
Although the platform was launched in Saudi Arabia, CEO Dr. Mohammed Alhussein recently announced plans to expand into the UAE, noting that the company’s long-term goal would be to develop operations further across the GCC. “The UAE has been making significant strides in enhancing its AML compliance and combating financial fraud, and Mozn entering UAE market will help accelerate these efforts,” Alhussein stated in a press release. “We are excited to begin this next chapter in Mozn’s growth journey as we enter the broader GCC market through our UAE office”.
News
ESET Bets On Specialized Security AI At 0.5% The Training Cost
ESET says NeoCortex can estimate an attacker’s next move, with a kill switch and human oversight built in to keep it from going rogue.
A sign-in anomaly, a suspicious process launch and an unusual connection may look harmless alone. Together, they can describe an intrusion that is hard for security to teams to piece together.
To solve that problem, ESET announced ESET NeoCortex on November 4, 2026: a specialized foundational AI model for cybersecurity. It powers ESET LiveCortex, available within the ESET PROTECT Platform, which connects fragmented security signals and turns them into actionable incidents.
Filtering is the main selling point, with LiveCortex processing billions of raw telemetry events daily, and just 0.00111% of them contributing to incidents presented to customers. The aim is less alert fatigue and more analyst time spent on threats that matter.
NeoCortex is built on world-model architecture, learning how digital environments behave and how events relate. ESET says that lets it reconstruct missing incident context, recognize similar past cases and estimate an attacker’s likely next step – what the company calls “minus-one-day defense,” a chance to intervene before the next stage unfolds. ESET points to Black October, the week leading up to October 25, 2026, when critical security issues affecting PHP, Linux, GitLab and other key systems demanded rapid, prioritized responses, as the kind of moment where that context matters.
ESET’s internal research also suggests a specialized model like NeoCortex can be trained at up to 200 times lower cost than a large general reasoning model, while delivering more accurate, defense-focused results. The company credits optimized training, a focus on cybersecurity vocabulary, lower computing requirements and privately owned hardware.
Also Read: Dubai Says Its Deepfake Detector Needs Just 7 Seconds Of Video
“AI is accelerating both cyberattacks and cyberdefenses. With ESET NeoCortex, we are applying our AI investment to develop specialized foundational cybersecurity intelligence that remains under our control and strengthens, rather than replaces, human expertise,” said Juraj Malcho, Chief Technology Officer at ESET.
As part of the design, NeoCortex forms part of a layered architecture combining specialized models, deterministic controls, correlation logic, validation, auditability and expert human oversight. Safeguards include restricted sandboxes, read-only tools, continuous monitoring, defined usage and token limits, a kill switch and human oversight of critical response decisions. The model is also developed and self-hosted on ESET’s own infrastructure, which the company says reduces exposure to shifts among third-party AI providers, including commercial and geopolitical ones.
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