AI in Regulatory Reporting & Financial Compliance

Leading regulators and financial firms are rapidly adopting AI-driven RegTech to automate reporting, monitor anomalies, and interpret complex regulations. This interactive course equips participants with hands-on skills in NLP-driven compliance, AI-based anomaly detection, and real-time regulatory dashboards. Through global case studies and emerging frameworks such as the EU AI Act and SEC oversight delegates will design audit-ready, trustworthy AI systems tailored for modern financial compliance.

TRAINING TOPICS

• RegTech evolution: AI in compliance, risk, and reporting
• NLP for extracting regulatory obligations and document summarization
• AI‑powered anomaly detection in financial transactions and reporting
• Automated KYC/AML workflows with machine learning
• Real-time dashboards for compliance metrics and regulatory KPIs
• Explainable AI and audit trails under AI Act, ECOA, SEC oversight
• Generative AI for dynamic regulatory report generation
• Integration of ML with RPA and blockchain for immutable audit logs
• Regulator use-cases in ML-driven supervisory analytics
• Environmental and sustainability compliance via AI governance

BY THE END OF THIS TRAINING COURSE, DELEGATES WILL BE ABLE TO

• Interpret regulation using NLP and build compliance automations
• Detect reporting anomalies and suspicious activity with AI models
• Deploy end-to-end RegTech pipelines integrating AI and RPA
• Apply explainable AI and maintain audit compliance under AI Act
• Build real-time regulatory dashboards and compliance KPIs
• Automate KYC/AML workflows backed by ML explainability
• Generate and validate regulatory reports using generative AI
• Assess sustainability and carbon footprint risks of AI systems

TRAINING IS TAILORED TO

• Regulatory affairs and reporting teams
• Compliance and audit professionals
• Risk managers in financial institutions
• FinTech and RegTech developers
• Data scientists in the regulatory domain
• Legal and policy advisors in financial services

TRAINING METHODOLOGY

• Live demos: NLP pipelines, RPA‑AI integrations, regulatory dashboards
• Labs: automated document parsing, anomaly model building, report generation
• Explainability: SHAP, LIME, and transparent ML model auditing
• Use-case simulations: KYC, AML alerts, SEC/AI claim disclosures
• Policy sessions: EU AI Act, SEC oversight, AI-washing risk
• Governance exercises: data lineage, carbon-risk frameworks, compliance frameworks

Day One:

FOUNDATIONS OF AI IN REGTECH & COMPLIANCE

• RegTech landscape: trends, benefits, challenges
• AI Act, SEC AI-washing, UK cyber resilience requirements
• Lab: NLP extraction of regulatory clauses
• Tool demo: Doc summarization and obligation mapping

Day Two:

NLP & DOCUMENT INTELLIGENCE FOR COMPLIANCE

• Automating regulation summarization using NLP
• Mapping document clauses to obligations
• Lab: build a regulation compliance comparison engine
• Demo: rule-based vs ML-enhanced anomaly detection

Day Three:

AUTOMATED REPORTING & DASHBOARDS

• Generative AI for multi-jurisdiction report synthesis
• RPA tool integration (e.g., UiPath) with AI pipelines
• Lab: generate compliance KPI dashboards
• Case study: Basel III report via ML & NLP

Day Four:

ANOMALY DETECTION & KYC/AML SCHEMES

• Transaction monitoring with ML models
• Use AI to speed screening in AML/KYC pipelines
• Lab: AI-based suspicious pattern detection
• Governance: balancing alert sensitivity and compliance

Day Five:

EXPLAINABILITY, GOVERNANCE & ETHICS

• Implementing audit trails, SHAP/LIME explainability
• Simulate SEC AI-washing oversight & governance review
• Lab: embed traceability and carbon footprint metrics
• Panel: building future-proof AI compliance frameworks