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

Upon completing the workshop, participant will be able to:

• Understand the concept of e-KYC and its benefits for the financial services industry.
• Gain an awareness of the regulatory landscape for e-KYCand its impact on financial institutions.
• Learn about the different digital identity verification methods available for e-KYC and how to choose the right method.
• Gain an awareness of the importance of Confusion Matrix in evaluating Machine Learning models.
• Develop an understanding of the e-KYC customer onboarding process and how to streamline it for an improved customer experience.
• Understand how e-KYC can help mitigate risks in financial services and how to integrate it into overall risk management strategies.
• Plan and design a sampling approach for eKYC implementation.
• Be able to identify successful e-KYC implementation in financial institutions and understand emerging trends and innovations in e-KYC.

• Participants should be able to explain the benefits of e-KYC for the financial services industry and identify the differences between traditional KYC and e-KYC.
• Participants should be able to identify and discuss the local regulations and guidelines for e-KYC and their impact on financial institutions.
• Participants should be able to identify and compare the different digital identity verification methods available fore-KYC and choose the appropriate method for their institution.
• Participants should be able to describe the e-KYCcustomer onboarding process and suggest ways to streamline it for an improved customer experience.
• Participants should be able to identify the risks associated with e-KYC and suggest ways to mitigate them, as well as integrate e-KYC into overall risk management strategies.
• Participants should be able to calculate and interpret different evaluation metrics using Confusion Matrix.
• Communicate the results of the sampling approach effectively to stakeholders.
• Participants should be able to explain the importance of customer experience monitoring and operational efficiency monitoring in eKYC implementation.

All level of managerial roles, business owner, product owner, process owner, operations, customer support

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