AI Fintech Solutions Dubai

The challenge of credit scoring for UAE expatriates, who represent 85% of the population and many of whom arrive without UAE credit history, requires alternative data approaches that go beyond Al Etihad Credit Bureau scores. We build alternative credit models that incorporate employment verification data including employer stability and salary consistency, remittance transaction patterns that indicate financial responsibility and cross-border obligations, utility and telecoms payment history, digital banking behavioral signals including transaction frequency and savings patterns, and where available, credit history from origin countries accessed through data sharing agreements. These alternative models are built within the CBUAE's responsible lending guidelines, with model explainability features that allow adverse action notices to be generated for applicants who are declined, satisfying both regulatory requirements and customer fairness obligations.

The UAE's removal from the FATF grey list in 2024 followed significant investment in AML compliance systems across the banking sector, and ongoing regulatory scrutiny from CBUAE means AML AI systems deployed in UAE banks face a high compliance bar. Our AML transaction monitoring solutions are designed to satisfy FATF Recommendations 16 and 20, covering wire transfer monitoring, beneficial ownership tracing through network analysis, politically exposed person screening, and sanctions list matching with real-time update capabilities. We implement layered detection approaches that combine rule-based controls with machine learning anomaly detection to balance detection sensitivity with alert volume management, a key operational challenge for UAE bank compliance teams. SAR generation support is built into the workflow, with alert triage interfaces that support the documentation requirements of the UAE Financial Intelligence Unit.

The Securities and Commodities Authority regulates investment advisory services in the UAE, and robo-advisory platforms must be operated by or in partnership with SCA-licensed investment advisors. Key AI-specific requirements include a documented suitability assessment process that captures client risk tolerance, investment objectives, and financial situation before generating recommendations, explainable recommendation outputs that allow clients and SCA examiners to understand the basis for investment advice, clear disclosure of the automated nature of the advisory process in client communications, and comprehensive audit logging of all recommendations and client interactions. We design robo-advisory systems with these requirements as architectural foundations, working with legal advisors to ensure the technical implementation satisfies SCA examination standards. We can also support the SCA licensing process by providing technical documentation that demonstrates regulatory compliance.

Development timelines for fraud detection and AML AI systems in UAE financial institutions depend on the complexity of the existing data infrastructure, the number of transaction types and payment rails to be monitored, and the depth of regulatory compliance requirements. A focused payment fraud detection model integrated with a single payment processing system typically takes 14-20 weeks including data preparation, model development, validation, integration, and testing. A comprehensive AML transaction monitoring platform covering multiple product lines, correspondent banking, and trade finance typically requires 6-12 months. Projects also include a regulatory compliance review phase that may involve CBUAE notification or pre-approval depending on the nature of the deployment. Investment levels range from AED 200,000 for focused applications to AED 1M+ for enterprise-scale AML platforms. We provide detailed proposals following a paid discovery engagement.