Regulatory Reporting

Regulatory data and documents are analyzed and processed to automate compliance reporting and validation tasks.

Business impact

• Regulatory reporting accuracy — AI reduces errors by automating data validation and calculation processes
• Regulatory compliance time — Automated workflows shorten the time needed to prepare and submit reports
• Operational risk reduction — Streamlined processes lower risks associated with manual reporting and data handling

Data requirements

• Structured regulatory data feeds (Structured) — Provide standardized inputs for automated report generation and validation
• Historical reporting data (Numeric) — Used to train AI models for anomaly detection and pattern recognition
• Regulatory documentation and guidelines (Text) — Text data used for natural language processing to interpret compliance rules
• APIs from internal systems (Code) — Enable real-time data integration and workflow automation across platforms

AI methods and techniques

• Predictive AI — Forecasts compliance risks and detects anomalies in reporting data
• Generative AI — Generates draft reports and interprets regulatory text for user queries
• Agentic AI — Autonomously manages reporting workflows while maintaining governance controls

AI models and model families

GPT-4o, Claude, Llama, Custom ML models

Industries

Real-world evidence

6 documented case studies on record.

Companies using this: ABCHK, Baker McKenzie, Clarity AI, Goldman Sachs, KPMG, WTW.