CLZK-EKYCFI: 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-KYC and 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 e-KYC implementation.
• Be able to identify successful e-KYC implementation in financial institutions and understand emerging trends and innovations in e-KYC.

No prerequisites.

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

• 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 for e-KYC and choose the appropriate method for their institution.
• Participants should be able to describe the e-KYC customer 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 e-KYC implementation.

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 Mode

• 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 e-KYC solution

Module 8:    Customer Experience Monitoring

• Understanding the importance of customer experience monitoring in e-KYC 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