Upon completion of this course, participants will be able to:

• Define and categorize the various **SupTech applications** (e.g., AI/ML for anomaly detection, automation for compliance) relevant to prudential and conduct supervision.
• Design a strategic roadmap for the **phased implementation** of SupTech, considering data infrastructure, skill requirements, and budgetary constraints.
• Apply **Machine Learning algorithms** (e.g., clustering, classification) to supervisory data for real-time risk scoring and early warning system enhancement.
• Develop and implement a robust **Model Risk Governance (MRG)** framework for the validation and continuous monitoring of AI/ML SupTech tools.
• Master the use of automation (RPA) for streamlining repetitive tasks, such as data quality checks and standardized report generation.
• Address the **legal and ethical challenges** of AI in supervision, including data privacy, bias, and the "black box" problem.
• Formulate the necessary **change management and reskilling** strategy for supervisory teams transitioning to an AI-augmented environment.
• Understand the technical requirements for integrating SupTech systems with existing regulatory data warehouses and IT infrastructure.

• Financial Supervisors and Examiners seeking advanced data analysis skills.
• IT and Technology Strategy Managers in Regulatory Authorities.
• Chief Data Officers and Data Governance Specialists.
• Model Risk Validation and Audit Teams.
• Senior Management involved in Regulatory Modernization Strategy.
• Regulatory Policy Developers specializing in Data and Technology.

• SupTech Use Case Definition and Strategic Roadmap Workshops
• Hands-on Machine Learning Workshops (Anomaly Detection and Risk Scoring)
• Case Studies on Successful SupTech Implementation and Lessons Learned (e.g., Bank of England, MAS)
• Group Activities on Designing a Model Risk Governance (MRG) Framework for an AI Model
• Workshops on Ethical AI and Mitigating Algorithmic Bias in Supervision
• Individual Exercises on Calculating the ROI of RPA for a Supervisory Process

Unit 1: The Strategic Imperative for SupTech

• Defining SupTech and its role in meeting the challenges of a rapidly evolving, data-intensive financial sector.
• Review of key use cases for SupTech globally: early warning, market conduct surveillance, regulatory reporting.
• Analyzing the trade-offs: cost of implementation vs. gains in efficiency and supervisory effectiveness.
• The necessary foundation: data quality, infrastructure, and standardized data collection (RegTech synergy).
• The organizational shift: moving from reactive compliance review to proactive, predictive oversight.

Unit 2: AI and Machine Learning for Risk Prioritization

• Applying **AI/ML** for **real-time anomaly detection** in regulatory reporting and transaction data.
• Using ML models (e.g., Random Forests) to enhance the accuracy and efficiency of **early warning systems**.
• Clustering techniques for identifying peer groups, segmenting institutions, and spotting unusual risk concentrations.
• Addressing the "black box" problem: utilizing Explainable AI (XAI) for transparency in supervisory findings.
• Protocols for feeding ML-driven insights directly into the on-site examination planning process.

Unit 3: Automation and Streamlining Supervisory Processes

• Implementing **Robotic Process Automation (RPA)** to automate routine, high-volume supervisory tasks (e.g., data input, cross-checking).
• Developing automated tools for continuous monitoring of compliance with prudential and conduct rules.
• The use of Natural Language Processing (NLP) to analyze unstructured data (e.g., complaints, contracts, media sentiment).
• Designing the secure, auditable interface for automated data collection from regulated institutions.
• Measuring the ROI and operational impact of automation on supervisory workload and effectiveness.

Unit 4: Data Governance and Model Risk Management

• Developing a robust **Data Governance Framework** for all supervisory data used in SupTech systems.
• Establishing a formal **Model Risk Governance (MRG)** process for the validation, documentation, and backtesting of AI/ML models.
• Addressing the legal and ethical requirements for data privacy, security, and access control.
• Protocols for identifying, mitigating, and documenting potential **algorithmic bias** in AI-driven supervision.
• Compliance with national and international AI regulation and ethical guidelines.

Unit 5: Implementation Strategy and Change Management

• Designing a multi-year **SupTech implementation roadmap** with clear milestones and pilot programs.
• Developing the comprehensive **reskilling and training program** for supervisory staff (data literacy, model interpretation).
• Managing the internal change management process and overcoming cultural resistance to automation and AI.
• Strategies for effective procurement and vendor management for complex SupTech solutions.
• Measuring the ultimate impact of SupTech on financial stability and regulatory compliance rates.