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Architecting the future of sustainable finance through high-dimensional AI and ESG intelligence.

Arabesque represents a sophisticated synthesis of quantitative finance and artificial intelligence, specifically engineered for the 2026 institutional market. Its architecture leverages high-performance computing (HPC) to process over 4 million data points daily from more than 30,000 sources, transforming raw non-financial data into actionable sustainability signals. The platform is divided into two primary engines: Arabesque S-Ray, which provides systematic ESG scoring and climate transition assessments, and Arabesque AI, a portfolio construction engine that integrates these sustainability metrics with traditional financial indicators. The technical core utilizes machine learning to identify non-linear relationships between ESG performance and financial materiality, enabling asset managers to build 'Active ESG' strategies. By 2026, Arabesque has positioned itself as the backbone for SFDR and TCFD compliance, offering real-time temperature alignment scores and norm-based screening. Its market position is defined by its transparency; unlike black-box competitors, Arabesque provides a granular look into the underlying drivers of its AI-generated scores, making it a prerequisite for institutions seeking to mitigate greenwashing risks while optimizing for long-term alpha.
Arabesque represents a sophisticated synthesis of quantitative finance and artificial intelligence, specifically engineered for the 2026 institutional market.
Explore all tools that specialize in optimize investment portfolios. This domain focus ensures Arabesque delivers optimized results for this specific requirement.
Explore all tools that specialize in paris agreement tracking. This domain focus ensures Arabesque delivers optimized results for this specific requirement.
A proprietary machine learning model that analyzes corporate performance across Environmental, Social, and Governance pillars with 22 sub-categories.
Quantifies the extent to which a company's projected emissions align with 1.5°C, 2°C, or 3°C global warming scenarios.
Automated monitoring of company alignment with the United Nations Global Compact (UNGC) principles.
Utilizes neural networks to detect patterns between ESG data and financial market performance.
Automated generation of Principal Adverse Impact (PAI) statements for Article 8 and 9 funds.
Direct database-to-database sharing of sustainability metrics without the need for ETL processes.
Analysis of over 170 countries based on their commitment to SDGs and climate resilience.
Initial consultation to define investment universe and ESG objectives.
API key provisioning via the Arabesque Developer Portal.
Integration of ISIN or SEDOL mapping for existing portfolio synchronization.
Configuration of S-Ray score weightings based on proprietary investment house views.
Setup of the Climate Transition (Temperature) monitoring module.
Integration of the AI Engine for automated backtesting of ESG-financial correlations.
Establishing data pipelines through Snowflake or AWS S3 buckets.
Configuration of automated SFDR Article 8/9 reporting templates.
User Acceptance Testing (UAT) for real-time risk alerts and threshold notifications.
Production deployment and monthly review of AI model performance vs. benchmarks.
All Set
Ready to go
Verified feedback from other users.
"Highly regarded for its purely quantitative, non-biased approach to ESG. Users appreciate the depth of climate data but note the premium pricing."
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