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Machine Learning for Credit Scoring and Risk Management
  • Overview
  • Objectives & Outline
  • Methodology
  • Participant Profile
  • Trainer
  • Overview
    Programme Details
    Date
    6 November 2024
    Time
    9:00 AM – 5:00 PM
    Venue
    Asian Banking School
    HRDC PROG NO
    10001413714
    This one-day programme focuses on the application of machine learning in credit scoring and risk management within the financial domain (banks and financial institutions). Participants will gain insights into credit scoring and risk assessment methodologies, explore data preprocessing and feature engineering techniques, and understand the application of various machine learning algorithms. The programme also covers model interpretability and explainability, presents real-world case studies in credit risk, addresses ethical considerations and fair lending practices, and concludes with a hands-on activity guiding participants in building a credit scoring model.
    Learning Level
    Intermediate
    Programme Fees*

    MYR

    1,700

    *Subject to 8% Service Tax per pax

  • Objectives & Outline
    LEARNING OBJECTIVES
    By the end of the programme, participants will be able to:
    • Understand credit scoring and risk assessment principles in finance
    • Apply data preprocessing and feature engineering techniques for credit data
    • Implement machine learning algorithms for credit scoring
    • Interpret and explain machine learning models for credit risk
    • Analyse real-world case studies to gain practical insights
    • Navigate ethical considerations and promote fair lending practices
    • Gain hands-on experience in building a credit-scoring model
    PROGRAMME OUTLINE
    Credit Scoring and Risk Assessment in Finance

    Data Preprocessing and Feature Engineering

    Machine Learning Algorithms for Credit Scoring

    Model Interpretability and Explainability

    Real-world Case Studies in Credit Risk

    Ethical Considerations and Fair Lending

    Hands-on Activity: Building a Credit Scoring Model

    Q&A and Discussion
  • Methodology
    Interactive group discussions, lectures, exercises, case studies and sharing of real-world experiences
  • Participant Profile
    Senior managers, managers, and executives from financial institutions
  • Trainer
    PETER KUA
    Peter Kua is the co-founder and CEO of GradientX. His responsibilities include finding ways data can be used as a competitive advantage as well as identifying new business opportunities with data. He also heads the Data Science team in REV Media Group (formerly known as Media Prima Digital) and was instrumental in driving the National Big Data Analytics Initiative under MDEC in the areas of thought leadership and industry development. He played a key role in developing the first National BDA Framework that delivered strategic recommendations and action plans to achieve the National BDA vision.
    Peter has conducted training in areas that include public masterclasses in Big Data Strategy for NTT Data, PAS Selangor, CIIF, Pos Aviation Hitachi, FGV, Perodua Maxis, SIRIM, and Principal Asset Management CIMB. In addition, he also trained several in-house Big Data Strategy workshops for organisations such as OCBC, Keysight, TNB, TM One, and Johnson and Johnson. During the MCO period, Peter conducted several Big Data Strategy virtual classes for Citibank, Alliance Bank, Bank Islam, Intel, Osram, Dell, Sarawak Energy, Optics Balzers Penang Port, and Maxis Broadband. Peter’s core professional strengths include data science and big data strategies, web development and project management. His industry experience includes the media, internet, manufacturing, FMCG, e-learning and agriculture.

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