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