Saudi-built AI takes on financial crime: Mozn's FOCAL raises the bar for AML and fraud teams
Financial crime is getting smarter. Banks, fintechs, and EMIs need tools that see across products, channels, and behaviors - without ripping out what already works. That's the gap FOCAL, an AI-driven platform from Riyadh-based Mozn, is aiming to fill.
Founded in 2017, Mozn builds AI with regional regulations and language realities in mind, then scales those capabilities across markets. The company has earned global recognition, including repeat placement among the World's Top 250 Fintech Companies. In January 2026, FOCAL was named a Category Leader in Chartis Research's RiskTech Quadrant 2025 for AML Transaction Monitoring and KYC Data and Solutions, placing it among a select group worldwide. Chartis Research is a widely referenced authority for risk technology evaluations.
Built for compliance, expanded for fraud
FOCAL started in 2018 with core AML functions: customer screening, watchlists, and transaction monitoring to support counter-terrorism financing and suspicious activity detection. As attack methods changed, the platform expanded into fraud prevention - first with device risk analysis, then device fingerprinting, behavioral biometrics, and transaction fraud detection.
That shift brought a more preventive stance. Risk is assessed across the customer lifecycle, from onboarding and login behavior to ongoing account activity, with the goal of stopping losses before they hit the P&L.
Converged "Financial Crime Intelligence" - without replacing your stack
The newest layer in FOCAL is Financial Crime Intelligence: a vendor-neutral framework that unifies alerts and insights from multiple systems into one place for investigations and reporting. The pitch to finance and risk leaders is simple - get a consolidated view without a costly core replacement.
"Our architecture eliminates blind spots in financial crime detection. It gives institutions a complete view of the user journey, combining transactional and non-transactional behavioral data," said Malik Alyousef, co-founder of Mozn and chief technology officer of FOCAL.
Differentiators for risk and operations teams
- Prevention-first model: Assess risk at onboarding, authentication, and transactions to reduce fraud before it happens.
- Expert-led delivery: On-the-ground support for system design, tuning, assessments, and continuous optimization.
- Flexible controls: Extend schemas, rules, and data fields to match specific business models and risk tolerances.
- Local context: Built with regional regulatory requirements and language challenges in mind - helpful where global tools misread names or compliance nuances.
Results from live deployments
FOCAL is used by traditional banks, digital banks, fintechs, payment firms, EMIs, and other financial service providers - and is also applied in e-commerce and telecoms. Some large EMIs report fraud reductions of up to 90 percent after deployment.
"Clients benefit from fewer fraud losses and a better customer experience, as the system minimizes unnecessary friction and false rejections," Alyousef said.
Did you know?
- Electronic money institutions using FOCAL have reported up to 90% lower fraud.
- The platform brings AML and fraud prevention together under one financial crime intelligence layer.
- It integrates with existing banking systems, avoiding a full technology replacement.
Why this matters for finance leaders
Compliance teams are under pressure to hit lower false positive rates while proving stronger financial crime controls to auditors and regulators. Operations leaders need faster case resolution, fewer escalations, and cleaner customer experiences. Security leaders want earlier detection signals, tied to device and behavioral risk, not just rules on transactions.
A converged approach checks those boxes by fusing AML, KYC, and fraud signals into one investigation flow. It also gives executives a clearer read on exposure and ROI.
Practical steps to consider
- Unify signals: Connect device risk, behavioral biometrics, and transaction data to cut alert noise and speed investigations.
- Tune continuously: Establish a cadence for model and rule calibration with measurable targets (TPR/FPR, SAR yield, fraud basis points).
- Close the loop: Feed confirmed fraud and SAR outcomes back into detection logic for faster learning.
- Design for context: Account for regional naming conventions, watchlists, and regulatory specifics that can skew screening quality.
- Measure customer impact: Track friction (challenges, holds, rejections) alongside loss rates to avoid revenue drag.
What's next: agentic AI for investigations
Mozn is investing in agentic AI to automate investigative workflows and rule-building, leveraging the Financial Crime Intelligence layer. The goal is to improve investigator productivity and reduce false positives through AI-assisted workflows and advanced machine learning.
As AI becomes widely accessible, misuse by criminals will rise. "Our goal is to stay ahead of that curve and to contribute meaningfully to positioning Saudi Arabia and the region as globally competitive leaders in AI," Alyousef said.
Resources
- Global AML/CFT standards and guidance: FATF
- Curated AI tools for finance teams: AI tools for Finance
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