Credit Risk Modelling and Assessment Training Course

Introduction

This intensive 5-day training course provides a comprehensive and practical exploration of credit risk modelling and assessment, equipping participants with the advanced analytical techniques and quantitative skills essential for navigating the complexities of modern credit markets. From understanding the foundations of credit risk to implementing sophisticated modelling approaches for retail and wholesale portfolios, this program offers a deep dive into the methodologies used by leading financial institutions to measure, manage, and mitigate credit exposures. Participants will gain hands-on experience with statistical models, machine learning applications, and regulatory frameworks, enabling them to make robust credit decisions and optimize risk-adjusted returns.

The course goes beyond theoretical concepts, focusing on real-world applications and the critical assessment of credit risk in various contexts, including lending, trading, and portfolio management. Through interactive case studies, practical exercises, and discussions of industry best practices, attendees will learn to build, validate, and interpret credit risk models, assess probability of default (PD), loss given default (LGD), and exposure at default (EAD), and understand their integration into capital requirements and stress testing. Whether you are a credit analyst, risk manager, quantitative modeler, or finance professional seeking to enhance your expertise in credit risk, this program offers an unparalleled opportunity to master cutting-edge techniques and stay ahead in the dynamic world of credit risk management.

Duration: 5 days

Target Audience:

  • Credit Risk Analysts
  • Risk Managers
  • Quantitative Analysts
  • Portfolio Managers
  • Credit Officers
  • Financial Regulators
  • Internal Auditors
  • Data Scientists working in finance

Objectives:

  • To provide a thorough understanding of credit risk concepts and its various facets.
  • To equip participants with the knowledge and skills to build and validate credit risk models (PD, LGD, EAD).
  • To understand the application of statistical and machine learning techniques in credit scoring.
  • To explore regulatory frameworks (e.g., Basel Accords) and their impact on credit risk capital.
  • To enable participants to implement robust credit risk assessment and management frameworks.

Course Modules:

Introduction

  • Overview of credit risk: definition, types, and sources.
  • The importance of credit risk management in financial institutions.
  • Key components of credit risk: Probability of Default (PD), Loss Given Default (LGD), Exposure at Default (EAD).
  • Introduction to the credit risk modelling landscape and its evolution.
  • Regulatory drivers for advanced credit risk measurement.

Fundamentals of Credit Risk Measurement

  • Traditional credit analysis vs. quantitative approaches.
  • Understanding default events and recovery rates.
  • Concept of unexpected loss and expected loss.
  • Credit ratings: internal and external methodologies.
  • Introduction to credit portfolio risk.

Probability of Default (PD) Modelling

  • Statistical methods for PD estimation: Logistic Regression, Probit Models.
  • Survival analysis techniques for PD.
  • Introduction to machine learning models for PD (e.g., Decision Trees, Random Forests, Gradient Boosting).
  • Data preparation and feature engineering for PD models.
  • Model validation and performance metrics for PD models (AUC, Gini, KS).

Loss Given Default (LGD) and Exposure at Default (EAD) Modelling

  • Definition and determinants of LGD.
  • Methodologies for LGD estimation: workout LGD, market LGD.
  • Empirical approaches and statistical models for LGD.
  • Understanding EAD for different product types (revolving credit, term loans).
  • Modelling EAD: deterministic vs. stochastic approaches.

Credit Portfolio Models

  • Aggregating individual credit risks to portfolio level.
  • Single-factor and multi-factor credit portfolio models.
  • Merton's model and structural models of default.
  • Copula functions for modelling dependent defaults.
  • Stress testing credit portfolios and scenario analysis.

Credit Scoring and Rating Systems

  • Design and implementation of internal credit rating systems.
  • Retail credit scoring models: application scoring, behavioral scoring.
  • Corporate credit scoring: quantitative and qualitative factors.
  • Scorecard development, calibration, and validation.
  • Challenges in credit scoring for specific industries or segments.

Model Validation and Governance

  • Principles of model validation: conceptual soundness, accuracy, stability.
  • Quantitative validation techniques: backtesting, out-of-sample testing.
  • Qualitative aspects of model validation: documentation, governance.
  • Model risk management framework: identification, measurement, and mitigation.
  • Regulatory expectations for model validation (e.g., SR 11-7).

Regulatory Capital and IFRS 9

  • Basel Accords (Basel II, Basel III) and their impact on credit risk.
  • Standardized Approach vs. Internal Ratings Based (IRB) Approach for credit risk capital.
  • Understanding IFRS 9 impairment requirements: Expected Credit Loss (ECL).
  • Calculating ECL for different stages of financial instruments.
  • Integration of credit risk models into capital planning and financial reporting.

CERTIFICATION

  • Upon successful completion of this training, participants will be issued with Macskills Training and Development Institute Certificate

TRAINING VENUE

  • Training will be held at Macskills Training Centre. We also tailor make the training upon request at different locations across the world.

AIRPORT PICK UP AND ACCOMMODATION

  • Airport pick up and accommodation is arranged upon request

TERMS OF PAYMENT

Payment should be made to Macskills Development Institute bank account before the start of the training and receipts sent to info@macskillsdevelopment.com

 

Credit Risk Modelling And Assessment Training Course in Belarus
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