Are you working in finance, compliance, or crypto and unsure how to keep up with fast-changing regulations?
As digital assets become mainstream, understanding crypto compliance is no longer optional—it’s essential. This intensive course will equip you with the tools, frameworks, and practical skills to navigate the evolving regulatory landscape of blockchain, VASPs, and DeFi.
Stay ahead of the curve—enroll now and become a leader in crypto compliance.
About the Course
This 2-day intensive course offers a practical and structured introduction to the compliance and regulatory dimensions of the cryptocurrency ecosystem. Participants will explore the foundational principles of blockchain and digital assets, understand key risk typologies, and gain insight into global regulatory frameworks. The course bridges theory and real-world applications, enabling professionals to build a solid understanding of compliance tools, responsibilities, and best practices in an evolving digital finance landscape.
Learning Outcomes
Understand the fundamentals of Bitcoin, altcoins, wallets, and transaction structures
Use block explorers to trace and analyze crypto transactions
Identify key risk areas in the crypto sector, including AML/CFT, fraud, and sanctions evasion
Understand global regulatory frameworks (e.g. MiCA,) and how they apply to VASPs
Apply compliance tools and techniques such as transaction monitoring and blockchain analytics
Evaluate future trends in crypto compliance, including DeFi, privacy coins, and CBDCs
Compliance officers and AML professionals in traditional and digital finance
Regulators and policymakers involved in crypto oversight
Law enforcement and financial intelligence professionals
Legal and risk advisors working with blockchain-based businesses
Crypto exchange operators and fintech executives
Growth managers and business operations professionals in fintech and crypto firms
Banking professionals responsible for onboarding crypto clients and managing crypto-related partnerships
Who is this Course for?
Modules
Session 1: Overview of AI and its Role in Fintech
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Definition of AI, machine learning, and generative AI
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AI applications in financial services: payments, lending, insurance, wealth management
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The transformative potential of AI in fintech
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Case studies of AI in finance
Session 2: Fundamentals of AI: Key Concepts
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Introduction to data-driven decision-making
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Key AI technologies: machine learning, natural language processing, and computer vision
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Overview of AI lifecycle: data collection, model building, and deployment
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Session 3: Machine Learning Fundamentals for Fintech
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Supervised vs. unsupervised learning
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Application of machine learning models in financial forecasting and risk assessment
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Understanding credit scoring models and fraud detection
Session 4: Hands-On Introduction to Machine Learning
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Basic hands-on exercises in data analysis and model training (simplified using no-code/low-code tools)
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Demo of a machine learning tool in the financial context
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Session 5: Introduction to Generative AI
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What is generative AI and its core components
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Use cases of generative AI in fintech: personalized financial services,= chatbot development, and automated content generation
Session 6: Generative AI and Chatbots in Financial Services
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Exploring AI chatbots for customer service and financial advice
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Case studies of chatbot deployment in fintech companies
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Session 7: The Role of Data in AI and Fintech
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Importance of data quality and data governance in AI applications
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Introduction to data analytics tools in fintech
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Compliance and privacy concerns related to financial data
Session 8: Data-Driven Insights for Financial Decision-Making
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Practical example: building basic financial dashboards using data analytics tools
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Real-world case study: leveraging AI for personalized financial product recommendations
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Session 9: AI for Fraud Detection in Fintech
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Machine learning algorithms for detecting fraudulent transactions
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AI-driven risk-scoring models
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Case study: AI tools used by banks and payment processors
Session 10: Implementing AI for Risk Management
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Identifying risks using predictive modeling
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AI applications for credit risk, operational risk, and liquidity risk management
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Demo: Basic predictive risk model using AI tools
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Session 11: AI in Payments
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How AI optimizes payment processing systems
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Use cases: AI-driven payment gateways and fraud prevention
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Case studies: PayPal, Stripe, and AI-driven payment solutions
Session 12: AI for Lending and Credit Scoring
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AI’s impact on peer-to-peer lending and microfinance
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Credit scoring models powered by AI
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Case studies on AI in digital lending platforms
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Session 13: Regulatory Landscape for AI in Finance
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Overview of financial regulations impacting AI adoption
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Compliance with data protection laws (e.g., GDPR, PSD2)
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Addressing algorithmic bias and fairness in AI models
Session 14: Ethical AI and Responsible Innovation in Fintech
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Importance of transparency, fairness, and accountability in AI models
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Ethical considerations in customer data usage
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Best practices for deploying ethical AI in fintech
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Session 15: Future Trends in AI for Fintech
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Trends shaping the future of AI in finance: decentralized finance (DeFi), blockchain, AI-driven asset management
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How AI and fintech will evolve in the coming years
Session 16: Capstone Project Presentation and Course Wrap-Up
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Team-based or individual capstone projects
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Presentations and feedback ○ Course review and next steps
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Agenda
Day 1 | 21 Oct, 2025
Module 1 - Welcome & Course Introduction
Module 2 - Introduction to Bitcoin & UTXO
Module 3 - Wallets, Keys, and Addresses
Module 4 - From Transaction to Block
Module 5 - Block Explorers
Module 6 - Altcoins: Beyond Bitcoin
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Day 2 | 22 Oct, 2025
Module 7 - Industry Typologies
Module 8 - Cryptocurrency Risk
Module 9 - Regulation
Module 10 - Blockchain Analysis
Module 11 - Transaction Monitoring & Compliance
Module 12 - What the Future Holds (Q&A)
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Trainer
Zulfikar Moledina LL.B, MSc, GCIH, CAMS
Senior Training Specialist at Chainalysis
Zulfi Moledina is a Senior Training Specialist at Chainalysis where he delivers training to law enforcement, crypto companies, and financial institutions on understanding the fundamentals of crypto currencies and training Chainalysis tools (Reactor / Know Your Transaction).
Prior to joining Chainalysis, he was a Senior Compliance Officer at JP Morgan's Financial Intelligence Unit, specialising in assessing the linkage between cryptocurrencies and the regulated financial sector. Further to this role, he represented JP Morgan at the UK Joint Money Laundering Intelligence Taskforce (JMLIT). He also worked as a Cyber Threat Intelligence Analyst within Cybersecurity operations at JP Morgan, and conducted proactive intelligence investigations into nation state cyber threat actors. Zulfi has worked extensively within law enforcement and was an Intelligence Officer and an Investigator in the UK National Cyber Crime Unit (NCCU) at the National Crime Agency and its precursor the Serious Organised Crime Agency (SOCA).
Zulfi holds a Masters degree in Computer Forensics and Digital Investigations, and a Law degree, he is a Certified Anti Money Laundering Specialist (ACAMS), and is an alumni of University of Oxford’s Artificial Intelligence Program.
