Module 1: Introduction To Kyc And E-Kyc

Overview of e-KYC and how it differs from traditional KYC
Benefits of e-KYC for financial institutions and customers
Understand the role of e-KYC in streamlining the customer onboarding process and how it complements the existing checks for screening rules and post transaction monitoring
Case studies of successful e-KYC implementation in financial institutions

Module 2: Regulatory landscape for e-KYC

Overview of Bank Negara Malaysia requirements for e-KYC
Compliance with Bank Negara Malaysia’s Risk Management in Information
Technology and Personal Data Protection Act 2010
Overlap with the Anti-Money Laundering, Countering Financing of Terrorism and Targeted Financial Sanctions for Financial Institutions (AML/CFT)
Discussion of challenges and opportunities in compliance with e-KYC regulations

Module 3: Digital Identity Verification

Types of digital identity verification methods available for e-KYC
Advantages and disadvantages of each method
Case study of non-ideal and fraudulent scenarios which challenges the limitations of the different identity verification methods
Case study of existing digital identity verification methods among financial institutions

Module 4: Risk management

Understanding and managing all types of risks associated with e-KYC
Integrating e-KYC into overall risk management strategies with other authentication factors and post transaction monitoring
Understanding the importance of e-KYC in safeguarding the entry point to the financial and banking ecosystem in Malaysia
Case study of how mule accounts and scams have evolved with the introduction of the e-KYC technology

Module 5: Building and Evaluating a Machine Learning Model

Overview of the steps involved in building a Machine Learning model
Awareness of the difference between the training data and validating it on non- training data to prevent the scenario of underfitting or overfitting
Understanding the importance of data preprocessing, data splitting, and feature selection
Case study of the end-to-end process of collecting training data, training it own a machine learning model and validating it with non-training data

Module 6: Fine-Tuning a Machine Learning model using the Confusion Matrix

Definition of Confusion Matrix and its role in Risk Management
Explanation of True Positive, True Negative, False Positive, and False Negative
Understanding the importance of accuracy, precision, recall, F1-score, and ROC curve
Understanding how to fine-tune a Machine Learning model using Confusion Matrix and its derivatives such as False Acceptance Rate, False Rejection Rate and Equal Error Rate

Module 7: A Sampling Approach to Evaluate the Effectiveness of the e-KYC Implementation

Understanding the definition of sampling and the different type of sampling methods
Plan the sampling approach by selecting a suitable sample size and sampling frame to avoid bias in data
Understanding the role of confidence interval and its interpretation in assessing how representative the sample is to the actual data
Case study of how to conduct a sampling exercise to determine the effectiveness of a eKYC solution

Module 8: Customer Experience Monitoring

Understanding the importance of customer experience monitoring in eKYC implementation
Overview and the explanation of the different operational efficiency metrics to monitor
Understanding of how to evaluate the trade-off of risk management versus customer experience
Strategies for improving the customer experience during e-KYC onboarding

CLPM-102 Electronic Know-Your-Customer for Financial Institutions