This 8-hour course aims to equip private bankers with the appropriate awareness and knowledge of the right risk management tools and strategies to mitigate cyber risks and other digital issues in this age of the digital workplace and transformation. The course will also expose them to how technologies like blockchain, AI/ML and data analytics are harnessed to optimise the risk management process including in financial crime areas like AML, KYC, data and cyber-security.

• Relationship managers and senior relationship managers in private banks
• Covered persons under Private Banking Code of Conduct

• Understand new risk paradigm and consequences of poor risk management in the digital workplace, minimize fraud risks through effective cybersecurity practices
• Aware of the growing demands of regulators to address digital risks and the use of technology by regulators
• Learn how technology are used by financial institutions to address both business and regulatory demands (AML / Blockchain)
• Develop awareness and increase sensitivity to developments in KYC/AML and cybersecurity,
• Understand the causes and consequences of data breaches and risks poses to bank;
• Know how to use of data analytics to enhance KYC/AML processes;
• Know skills to verify authenticity of information and spot fraudulent information
• Understand risk governance and how to protect data across tech platforms

Section 1 – Digital Evolution & The New Risk Paradigm

• Key Trends in Digitalization
• Digital evolution
• Advent of the digital workplace
• Reimagining the digital workforce
• Evolving Landscape
  • Data being produced anytime, anyplace, anyhow, etc.
  • Automate, automate, automate
  • Collaborate, collaborate, …: working side-by-side with RPA tools and robots
  • Enhanced dynamic outcome and performance management
  • Challenges and Risks in the new paradigm
  • Workshop
• Digital Risk Management: Building Blocks
• Case Study: Minimizing Fraud Risk

Section 2 – RegTech & SupTech to Address Digital Risks

• RegTech for Financial Institutions
• MAS FEAT Principles
• Cyber- and outsourcing risks
  • Supervisory initiatives
• Other Aspects of Digital Risk Management
  • Mitigating Risks Around Digital Enablers
• SupTech
  • Current State
  • Potential Use Cases & Outcomes
  • Conceptual Framework

Section 3 – How Technology is Used by Financial Institutions to Address Both Business & Regulatory Demands (AML / Blockchain)

• Lessons: Challenges & Competition from BigTech
• Drowning in a deluge of data
• Data Analytics:
  • Benefits of Data Insights & Analysis
• Platform enablement
  • Harnessing a Digital Platform
• Data Considerations:
• What is the Data Telling You?
• Addressing Regulatory Demands:
  • Quick overview of AML and Blockchain applications
  • Case Studies

Section 4 – Causes & Consequences of Data Breaches & Risks posed to the Bank

• Cause of Data Breaches
  • Data leakage taxonomy
• On reputation, brand and share value
  • Impact on stock prices and customer losses
• Workshop Discussion: Financial impact

Section 5 – Data Value & Increased Sensitivity to Developments in KYC/AML & Cybersecurity

• The Value of Data
• Data Value as a Universe
• Understanding the State of Compliance and Maturity amid the rise of RegTech in KYC / AML and Cybersecurity
• Cybersecurity and Privacy Defined
• The CIA & N
• Sensitive Data

Section 6 – Using Data Analytics to Enhance KYC/AML Processes

• Risk Management & Data - Big Picture Overview
• Example of an AML System
• Lifecycle of FCC Monitoring & Control
• FCC Case Studies – Lifecycle
  • Techniques of Fraud Detection using Machine Learning & Profiling
• Enhancement of Data Analytics with AI/ML
• Outlier & Anomaly Detection
• Further Case Studies

Section 7 – Verifying Authenticity of Information & Spotting Fraudulent Information

• Data Origins and Sourcing
  • Understand strengths and limitations of:
    • sources of data or information, e.g., Reuters/Bloomberg vs social media like Facebook, etc.
    • types of data, e.g., structured vs unstructured data or quantitative-based vs qualitative (e.g., surveys)
    • Document abuse, etc.
• Trend Analysis - aware of past trends incl.:
  • stability of and / or constant behavioural profiles vs anomalies, unusual profiles / patterns or spikes,
  • suspicious or non-mainstream transactional (STA) or behavioural patterns, e.g., from or in new geographies, relationships, etc.
• Risk-aware of cyber risk threats and profiles, e.g.,:
  • phishing, spam, hacks, DDoS, etc.
• Working with Technology, Domain Experts & Specialists

Section 8 – Understanding Risk Governance & Data Protection Across All Platforms

• Background & Backdrop: Types of Data & Understanding Data
• Data Assertions
• Structured & Unstructured Data
• Data Lifecycle
• Common Data Loss Vectors
• The Changing Data Loss Risk Landscape
• Mega-Trends resulting in Data Risk
• Challenges in Managing Data Loss
• Data Loss Risk Types & Lifecycle
• Data Loss Protection across Platforms using a Holistic Approach
• Data Risk Governance in DLP Framework
  • Data Governance
  • Data loss prevention controls
  • Support for information security processes

Assessment - MCQ